CN118737361B - Severe department patient monitoring management system based on data analysis - Google Patents

Abstract

The invention discloses a severe department patient monitoring management system based on data analysis, which relates to the field of clinical medicine and solves the problem of poor condition monitoring effect of the severe department patient monitoring management system, and comprises a data acquisition module: the data analysis module is used for acquiring the disease period monitoring data: the change monitoring module is used for obtaining the disease period analysis data according to the disease period monitoring data analysis: the monitoring and early warning module is used for acquiring the illness state change monitoring data of the severe patient according to the illness state period analysis data: the method is used for monitoring and early warning according to the disease period monitoring data and the critical patient disease change monitoring data, and can more accurately analyze and predict the disease change trend of the patient by utilizing the period change of the period disease reference index, thereby being beneficial to finding out the signs of disease deterioration in advance, and timely adjusting the treatment strategy to avoid rapid disease deterioration or excessive treatment.

Description

A patient monitoring and management system for critical care units based on data analysis

Technical Field

The present invention belongs to the field of clinical medicine and relates to data analysis technology, in particular to a critical care department patient monitoring and management system based on data analysis.

Background Art

The existing patient monitoring and management system for critical care units has the following defects:

1. The inability to automatically monitor the patient's facial expressions and analyze symptoms results in the medical staff being unable to understand the patient's pain level or emotional state in a timely manner, affecting the comprehensive assessment of the patient's condition;

2. The inability to systematically analyze symptoms results in a lack of depth in monitoring data, making it difficult for medical staff to obtain comprehensive information support when formulating treatment plans.

To this end, we propose a patient monitoring and management system for critical care units based on data analysis.

Summary of the invention

In view of the deficiencies in the prior art, the purpose of the present invention is to provide a patient monitoring and management system for a critical care unit based on data analysis. The present invention obtains the periodic medical resource usage data, the cumulative abnormal time ratio of periodic physiological indicators and the patient's condition weight corresponding to each treatment monitoring cycle, obtains the patient's typical symptom data, defines the periodic medical resource usage data, the cumulative abnormal time ratio of periodic physiological indicators, the patient's condition weight and the patient's typical symptom data as condition cycle monitoring data, calculates the periodic condition reference index corresponding to each treatment monitoring cycle according to the condition cycle monitoring data, obtains the condition cycle analysis data, calculates the patient's condition cycle change covariance according to the condition cycle analysis data, divides the patient's condition cycle change covariance into intervals, obtains the critical patient's condition change monitoring data, and performs monitoring and early warning according to the condition cycle monitoring data and the critical patient's condition change monitoring data.

In order to achieve the above purpose, the present invention adopts the following technical solution: The specific working process of each module of a patient monitoring and management system for a critical care unit based on data analysis is as follows:

Data acquisition module: used to divide the time period of critically ill patients receiving treatment into multiple treatment monitoring cycles, respectively obtain the periodic medical resource usage data, the cumulative abnormal time ratio of periodic physiological indicators, and the patient's condition weight corresponding to each treatment monitoring cycle, obtain the patient's typical symptom data, and define the periodic medical resource usage data, the cumulative abnormal time ratio of periodic physiological indicators, the patient's condition weight, and the patient's typical symptom data as the condition cycle monitoring data;

Data analysis module: used to calculate the periodic disease reference index corresponding to each treatment monitoring period according to the disease period monitoring data, and obtain the disease period analysis data;

Change monitoring module: used to calculate the patient's condition cycle change covariance based on the condition cycle analysis data, and divide the patient's condition cycle change covariance into intervals to obtain the critically ill patient's condition change monitoring data;

Monitoring and early warning module: used for monitoring and early warning based on the disease cycle monitoring data and the monitoring data of the condition changes of critically ill patients.

Further, the data acquisition module includes a visual data unit, a symptom data unit, a condition weight unit, a medical data unit and a physiological parameter unit;

During the time period when the critically ill patients are receiving treatment in the intensive care unit, mk treatment monitoring cycles are marked respectively, and the cycle start time corresponding to each treatment monitoring cycle is numerically obtained to obtain mk cycle start time values, and the time value corresponding to the current moment is obtained as the reference time value, and the difference between the mk cycle start time values and the reference time value is obtained respectively to obtain mk time difference values;

Arrange the mk time difference values in order according to their numerical values, and name the treatment monitoring cycles corresponding to the mk time difference values as the m1 to mk treatment monitoring cycles according to the arrangement order from large to small;

The symptom data unit analyzes the patient's clinical symptoms and obtains the patient's typical symptom data;

The visual data unit acquires and analyzes facial images of critically ill patients in the m1th treatment monitoring cycle to obtain the facial target image cycle duration ratio;

The medical data unit obtains the periodic medical resource usage data corresponding to the m1th treatment monitoring period for critically ill patients;

The physiological parameter unit acquires the periodic physiological index data corresponding to the m1 treatment monitoring cycle of the critically ill patient, and obtains the cumulative abnormal time ratio of the periodic physiological index;

The condition weight unit analyzes the treatment symptoms of critically ill patients in the m1 treatment monitoring cycle to obtain the patient's condition weight;

The facial target image cycle duration ratio, cycle medical resource usage data, and cycle physiological index cumulative abnormal time ratio corresponding to each treatment monitoring cycle are obtained respectively, and defined as the disease cycle monitoring data together with the patient's typical symptom data.

Furthermore, the symptom data unit acquires typical symptom data of the patient, as follows:

Obtain the cumulative symptom types that occur during the period of time when the critically ill patient receives treatment in the critical care department, obtain the first appearance time value corresponding to each type of symptom during the patient monitoring process, and obtain multiple symptom first appearance time values;

According to the time values of the first manifestation of multiple symptoms, the corresponding symptom types are named the first to the hth clinical symptoms in order from far to near;

According to the admission records of the intensive care unit, several patients with the same symptoms as the critically ill patients are obtained as sample patients, and the text of the clinical symptoms corresponding to each sample patient is obtained to obtain the clinical symptom record texts of multiple sample patients;

Performing identical text matching on clinical symptom record texts of multiple sample patients, taking clinical symptoms existing in each clinical symptom record text of each sample patient as typical clinical symptoms, and obtaining multiple typical clinical symptoms;

The number of digits of each typical clinical symptom appearing in the corresponding sample patients is obtained respectively, so as to obtain a plurality of different rank orders corresponding to each typical clinical symptom, and the mode is obtained for the plurality of different rank orders corresponding to each typical clinical symptom respectively, so as to obtain a typical rank order corresponding to each typical clinical symptom;

The first to h clinical symptoms are respectively compared with multiple typical clinical symptoms, and the clinical symptoms with the same comparison results are counted to obtain the second symptom number, and the ratio of the second symptom number to h is calculated to obtain the patient's typical symptom similarity ratio;

Obtain whether the typical sequence rank corresponding to each typical clinical symptom is consistent with the ranks corresponding to the first to the hth clinical symptoms;

If they are consistent, the corresponding clinical symptoms are marked as sequentially consistent clinical symptoms;

If they are inconsistent, the corresponding clinical symptoms are marked as sequence-inconsistent clinical symptoms;

Counting the number of clinical symptoms marked as sequentially consistent from the first to the hth clinical symptoms, and obtaining the number of clinical symptoms in the regular sequence;

Calculate the ratio of the number of clinical symptoms in the conventional sequence to h to obtain the ratio of the number of symptoms in the consistent sequence;

The ratio of the number of sequentially consistent symptoms and the similarity ratio of the patient's typical symptoms were defined as the patient's typical symptom data.

Furthermore, the visual data unit acquires the period duration ratio of the facial target image as follows:

In the m1th treatment monitoring cycle, the facial image of the critically ill patient is acquired in real time by the first condition monitoring device to obtain a video stream of the facial image of the patient, and the video duration of the video stream of the facial image of the patient is acquired to obtain the duration of the first image cycle;

The video stream of the patient's facial image is intercepted into a plurality of patient's facial images in units of a single video frame, and the playing time of the single video frame in the video stream of the patient's facial image is obtained to obtain the unit video frame time;

Establish a facial expression recognition model to perform image recognition on multiple patient facial images respectively, divide each patient facial image into a target type facial recognition image and a non-target type facial recognition image respectively, count the number of target type facial recognition images, and obtain the number of target type images;

The product of the number of target type images and the unit video frame duration is calculated, and then the ratio of the obtained product to the first image cycle duration is calculated to obtain the facial target image cycle duration ratio.

Furthermore, the medical data unit acquires periodic medical resource usage data as follows:

Obtain the online medical records of critically ill patients, obtain the medical expenses of critically ill patients corresponding to the m1 treatment monitoring cycle through the online medical records, and obtain the cumulative medical expenses value of the cycle;

According to the online medical records corresponding to the critically ill patients, the number of types of emergency drugs used by the critically ill patients in the m1th treatment monitoring cycle is obtained, and the emergency drugs used are named as the first to jth types of emergency drugs respectively;

The dosages of the first to j-th types of emergency drugs in the m1-th treatment monitoring period are respectively obtained to obtain the dosages of the first to j-th drugs;

The dosage of the first to jth drugs, the number of emergency drug types, and the cumulative medical expenditure value of the period are defined as the period medical resource utilization data.

Furthermore, the physiological parameter unit obtains the cumulative abnormal time ratio of the periodic physiological index, as follows:

In the m1th treatment monitoring cycle, the physiological indicators monitored for critically ill patients include the first to kth physiological indicators;

Performing periodic analysis on the first physiological indicator to obtain a first abnormal time ratio;

The details are as follows:

In the m1th treatment monitoring cycle, the first physiological index is monitored in real time by the monitoring equipment of the intensive care unit to obtain the value of the monitoring index;

If the value of the monitored indicator is within the normal interval corresponding to the first physiological indicator, it is determined that the first physiological indicator at the current moment is in the normal period;

If the value of the monitored indicator is not within the normal interval corresponding to the first physiological indicator, it is determined that the first physiological indicator at the current moment is in an abnormal period, and the time length of the abnormal period is obtained to obtain the abnormal duration of the first physiological indicator;

Obtaining the cumulative monitoring time of the first physiological indicator in the m1th treatment monitoring cycle to obtain the first cycle cumulative monitoring time, calculating the ratio of the abnormal time of the first physiological indicator to the first cycle cumulative monitoring time to obtain the first abnormal time ratio;

Repeat the process of acquiring the first abnormal time ratio, and acquire the abnormal time ratios of the second to k-th physiological indicators in the m1-th treatment monitoring cycle respectively, to obtain the second to k-th abnormal time ratios;

The first to kth abnormal time ratios are summed to obtain the cumulative abnormal time ratio of the periodic physiological index.

Furthermore, the condition weight unit obtains the patient's condition weight as follows:

The symptoms that occur in the patient during the m1th treatment monitoring cycle are divided into the first to tth treatment symptoms, and the symptom severity scores are respectively performed for the first to tth treatment symptoms to obtain the first to tth symptom severity scores;

Count the number of occurrences of the first to tth treatment symptoms in the m1th treatment monitoring period, and obtain the number of occurrences of the first to tth symptoms;

The patient's condition weight corresponding to the m1th treatment monitoring period is obtained by calculating the number of occurrences of the first to tth symptoms and the severity scores of the first to tth symptoms corresponding to the m1th treatment monitoring period;

The details are as follows:

;

Among them, Zym is the patient's condition weight corresponding to the m1th treatment monitoring cycle, Zc1 to Zct are the number of occurrences of the first to tth symptoms, and Zy1 to Zyt are the severity scores of the first to tth symptoms, respectively.

Furthermore, the data analysis module acquires the disease cycle analysis data, as follows:

Obtaining disease cycle monitoring data, and obtaining facial target image cycle duration ratio, patient disease weight, cycle medical resource usage data, and cycle physiological index cumulative abnormal time ratio corresponding to the m1 treatment monitoring cycle according to the disease cycle monitoring data;

According to the periodic medical resource usage data, the first to j-th drug usage dosages, the number of emergency drug types and the periodic cumulative medical expenditure values corresponding to the m1-th treatment monitoring period are obtained respectively;

The period medical resource utilization coefficient corresponding to the m1th treatment monitoring cycle is obtained by calculating the dosage of the first to jth drugs, the number of emergency drug types and the cumulative medical expenditure value of the period through a formula;

The formula is as follows:

;

Among them, Zzy is the period medical resource utilization coefficient, Jl1 to Jlj are the first to jth drug dosages, j is the quantity value corresponding to the emergency drug type, Jzl is the number of emergency drug types, and Lhf is the period cumulative medical expenditure value;

The periodic medical resource utilization coefficient, the facial target image period duration ratio corresponding to the m1th treatment monitoring period, the patient's condition weight, and the periodic physiological index cumulative abnormal time ratio are calculated by a formula to obtain the periodic condition reference index corresponding to the m1th treatment monitoring period;

The formula is as follows:

;

Among them, Zzs1 is the reference index of the m1th cycle condition, Zym is the patient condition weight, Zzy is the cycle medical resource utilization coefficient, Ysb is the cumulative abnormal time ratio of the cycle physiological index, and Mtb is the facial target image cycle duration ratio;

Repeat the process of obtaining the periodic disease condition reference index corresponding to the m1th treatment monitoring cycle, and obtain the periodic disease condition reference indexes corresponding to the m2th to mkth treatment monitoring cycles respectively;

The periodic disease condition reference index corresponding to the treatment monitoring periods m1 to mk is defined as the disease condition periodic analysis data.

Furthermore, the change monitoring module acquires the monitoring data of the condition change of the critically ill patient, as follows:

Obtaining disease cycle analysis data, and obtaining the cycle disease reference index corresponding to the treatment monitoring cycle from m1 to mk according to the disease cycle analysis data;

The periodic disease condition reference indexes corresponding to the treatment monitoring periods from m1 to mk are named as the periodic disease condition reference indexes from m1 to mk respectively;

Obtain time values for the start time of the treatment monitoring cycles m1 to mk respectively, and obtain the start time values of the cycles m1 to mk;

Calculate the average of the start time values of the cycles m1 to mk to obtain the average start time of the cycle;

Calculate the average of the disease condition reference indexes from cycle m1 to cycle mk to obtain the average of the disease condition reference indexes;

The covariance of the patient's condition cycle change is obtained by calculating the average value of the cycle start time, the values of the start time of the m1 to mk cycles, the average value of the cycle condition reference index, and the m1 to mk cycle condition reference index;

The covariance of the patient's condition periodic changes is calculated, and the specific formula configuration is as follows:

;

Among them, Cov (X, Y) is the covariance of the patient's condition cycle change, X1 to Xmk are the starting time values of the m1 to mk cycles, and Y1 to Ymk are the reference indexes of the m1 to mk cycles. is the average of the cycle start time, is the average value of the periodic disease reference index;

The first cycle covariance change threshold and the second cycle covariance change threshold are obtained respectively, and the patient's condition cycle change covariance is numerically compared with the first cycle covariance change threshold and the second cycle covariance change threshold to obtain the monitoring data of the condition change of the critically ill patient;

The details are as follows:

When the patient's condition periodic change covariance is greater than or equal to the first period covariance change threshold, it is judged that the severe condition is in the first condition change interval;

When the patient's condition periodic change covariance is less than the first period covariance change threshold and greater than the second period covariance change threshold, it is judged that the patient is in the second condition change interval;

When the patient's condition periodic change covariance is less than or equal to the second period covariance change threshold, it is judged that the severe condition is in the third condition change interval.

Furthermore, the monitoring and early warning module performs monitoring and early warning according to the monitoring data of the condition change of the critically ill patient, as follows:

Obtain monitoring data on the condition changes of critically ill patients;

When a critically ill patient is in the first condition change interval, the monitoring system issues an early warning of periodic deterioration of the condition;

When the critically ill patient is in the third condition change interval, the monitoring system monitors the condition change of the critically ill patient to normal;

When a critically ill patient is in the second condition change range, the monitoring system will assess the condition of the critically ill patient and issue an early warning based on the assessment results;

The details are as follows:

Acquire the disease cycle monitoring data, and acquire the abnormal time ratio corresponding to the mkth treatment monitoring cycle according to the disease cycle monitoring data to obtain the mkth abnormal time ratio;

Obtain the patient's typical symptom data, and obtain the ratio of the number of symptoms with consistent sequence and the similarity ratio of the patient's typical symptoms based on the patient's typical symptom data;

The coefficient of critically ill patients' condition assessment is obtained by calculating the ratio of mk abnormal time, the ratio of the number of symptoms with consistent sequence, and the similarity ratio of typical symptoms of patients;

The coefficient of critically ill patients' condition assessment is calculated, and the specific formula is configured as follows:

;

Among them, Zyp is the coefficient for judging the condition of critically ill patients, Ymk is the ratio of the mkth abnormal time, Sxy is the ratio of the number of symptoms with consistent sequence, and Hdx is the similarity ratio of typical symptoms of patients;

Obtaining a threshold value of a coefficient for judging the condition of a critically ill patient, and comparing the coefficient for judging the condition of a critically ill patient with the threshold value of the coefficient for judging the condition of a critically ill patient;

The details are as follows:

Obtain the mkth abnormal time ratio threshold, the sequential consistent symptom quantity ratio threshold, and the patient's typical symptom similarity ratio threshold respectively;

The threshold of the coefficient of critical patient condition assessment is obtained by calculating the mkth abnormal time ratio threshold, the threshold of the number of symptoms with consistent sequence ratio, and the threshold of the similarity ratio of typical symptoms of patients;

When the critical patient condition assessment coefficient ratio is greater than the critical patient condition assessment coefficient threshold, the monitoring system issues a critical patient treatment warning;

When the critically ill patient's condition assessment coefficient is less than or equal to the critically ill patient's condition assessment coefficient threshold, the monitoring system monitors the critically ill patient's condition normally.

In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

1. The present invention can periodically monitor the condition of critically ill patients by acquiring periodic condition monitoring data, and can systematically analyze the periodic changes of the patient's condition, so as to provide comprehensive information support for medical staff to formulate treatment plans;

2. The present invention utilizes the periodic changes of the periodic disease reference index to more accurately analyze and predict the patient's disease change trend, which helps to find signs of disease deterioration in advance, adjust the treatment strategy in time, and avoid rapid deterioration of the disease or overtreatment;

3. The present invention performs real-time monitoring and early warning based on the monitoring data of the condition changes of critically ill patients. The medical team can respond quickly to changes in the condition and take necessary intervention measures in time to improve the safety of patients and the treatment effect.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

FIG1 is a block diagram of the overall system of the present invention;

FIG2 is a structural block diagram of a data acquisition module unit in the present invention;

FIG. 3 is a data interaction diagram in the present invention.

DETAILED DESCRIPTION

The technical scheme of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

Embodiment 1

Please refer to FIG1 . The present invention provides a technical solution: a patient monitoring and management system for critical care departments based on data analysis, comprising a data acquisition module, a data analysis module, a change monitoring module, a monitoring and early warning module and a server, wherein the data acquisition module, the data analysis module, the change monitoring module and the monitoring and early warning module are respectively connected to the server, and the server controls the data acquisition module, the data analysis module, the change monitoring module and the monitoring and early warning module respectively;

The data acquisition module acquires the disease cycle monitoring data;

Please refer to FIG2 , the data acquisition module includes a visual data unit, a symptom data unit, a condition weight unit, a medical data unit, and a physiological parameter unit;

During the time period when the critically ill patients are receiving treatment in the intensive care unit, mk treatment monitoring cycles are marked respectively, and the cycle start time corresponding to each treatment monitoring cycle is numerically obtained to obtain mk cycle start time values, and the time value corresponding to the current moment is obtained as the reference time value, and the difference between the mk cycle start time values and the reference time value is obtained respectively to obtain mk time difference values;

Arrange the mk time difference values in order according to their numerical values, and name the treatment monitoring cycles corresponding to the mk time difference values as the m1 to mk treatment monitoring cycles according to the arrangement order from large to small;

It should be noted here that:

In the present application, mk is the quantity value corresponding to the marked treatment monitoring cycle, and mk is an integer greater than 0;

The symptom data unit analyzes the patient's clinical symptoms and obtains the patient's typical symptom data;

Obtain the cumulative symptom types that occur during the period of time when the critically ill patient receives treatment in the critical care department, obtain the first appearance time value corresponding to each type of symptom during the patient monitoring process, and obtain multiple symptom first appearance time values;

According to the time values of the first manifestation of multiple symptoms, the corresponding symptom types are named the first to the hth clinical symptoms in order from far to near;

It should be noted here that:

h is the quantity value corresponding to the symptom type;

According to the admission records of the intensive care unit, several patients with the same symptoms as the critically ill patients are obtained as sample patients, and the clinical symptoms corresponding to each sample patient are respectively obtained to obtain the clinical symptom record texts of multiple sample patients;

Performing identical text matching on clinical symptom record texts of multiple sample patients, taking clinical symptoms existing in each clinical symptom record text of each sample patient as typical clinical symptoms, and obtaining multiple typical clinical symptoms;

The number of digits of the order in which each typical clinical symptom appears in the corresponding sample patients is obtained respectively, so as to obtain a plurality of different order positions corresponding to each typical clinical symptom, and the mode is obtained for the plurality of different order positions corresponding to each typical clinical symptom respectively, so as to obtain the typical order position corresponding to each typical clinical symptom;

It should be noted here that:

If there is a same mode, the corresponding typical clinical symptoms will be ranked in order;

Compare the first to h clinical symptoms with multiple typical clinical symptoms respectively, count the clinical symptoms with the same comparison results, obtain the second symptom number, calculate the ratio of the second symptom number to h, and obtain the patient's typical symptom similarity ratio;

Obtain whether the typical sequence rank corresponding to each typical clinical symptom is consistent with the ranks corresponding to the first to the hth clinical symptoms;

If they are consistent, the corresponding clinical symptoms are marked as sequentially consistent clinical symptoms;

If they are inconsistent, the corresponding clinical symptoms are marked as sequence-inconsistent clinical symptoms;

It should be noted here that:

For example: a typical clinical symptom is randomly selected as a characteristic typical clinical symptom. If the characteristic typical clinical symptom corresponds to the second typical order, and the second clinical symptom of the critically ill patient is the same as the characteristic typical clinical symptom, the second clinical symptom is marked as a clinical symptom with consistent order.

Counting the number of clinical symptoms marked as sequentially consistent from the first to the hth clinical symptoms, and obtaining the number of clinical symptoms in the regular sequence;

Calculate the ratio of the number of clinical symptoms in the conventional sequence to h to obtain the ratio of the number of symptoms in the consistent sequence;

The ratio of the number of sequentially consistent symptoms and the similarity ratio of the patient's typical symptoms were defined as the patient's typical symptom data;

The visual data unit acquires and analyzes facial images of critically ill patients in the m1th treatment monitoring cycle to obtain the facial target image cycle duration ratio;

The details are as follows:

In the m1th treatment monitoring cycle, the facial image of the critically ill patient is acquired in real time by the first condition monitoring device to obtain a video stream of the facial image of the patient, and the video duration of the video stream of the facial image of the patient is acquired to obtain the duration of the first image cycle;

The video stream of the patient's facial image is intercepted into a plurality of patient's facial images in units of a single video frame, and the playing time of a single video frame in the video stream of the patient's facial image is obtained to obtain a unit video frame time;

It should be noted here that:

In this application, the frame rate of the video stream of the patient's facial image is 30fps, so each frame of the image takes 0.33 seconds, that is, the unit video frame duration involved here is 0.33 seconds;

Establish a facial expression recognition model to perform image recognition on multiple patient facial images respectively, divide each patient facial image into a target type facial recognition image and a non-target type facial recognition image respectively, count the number of target type facial recognition images, and obtain the number of target type images;

It should be noted here that:

In this application, the target type facial recognition image is a painful expression image, and the non-target type facial recognition image is a non-painful expression image. The painful expressions involved here include but are not limited to tears, crying, and eyes closed but tears rolling down. The non-painful expressions involved here include but are not limited to smiles and joy.

The facial expression recognition model involved here can be a recurrent neural network;

Calculate the product of the number of target type images and the unit video frame duration, and then calculate the ratio of the obtained product to the first image cycle duration to obtain the facial target image cycle duration ratio;

The medical data unit acquires the periodic medical resource usage data corresponding to the m1th treatment monitoring period for critically ill patients;

The details are as follows:

Obtain the online medical records of critically ill patients, obtain the medical expenses of critically ill patients corresponding to the m1 treatment monitoring cycle through the online medical records, and obtain the cumulative medical expenses value of the cycle;

It should be noted here that:

In this application, the cumulative medical expenses for the period involved here do not include the cost of some basic examinations included in the initial hospitalization of the patient;

According to the online medical records corresponding to the critically ill patients, the number of types of emergency drugs used by the critically ill patients in the m1th treatment monitoring cycle is obtained, and the emergency drugs used are named as the first to jth types of emergency drugs respectively;

It should be noted here that:

In this application, j is the quantity value corresponding to the type of emergency medicine used by the critically ill patient in the m1th treatment monitoring cycle, and j is an integer;

Among them, if the critically ill patient did not use any emergency medicine during the m1th treatment monitoring cycle, j is 0;

In this application, the classification of emergency medication types is based on specific reference to the emergency medication guidelines developed by the regional health department, among which common emergency medications include but are not limited to epinephrine, aminophylline, and dobutamine;

The dosages of the first to j-th types of emergency drugs in the m1-th treatment monitoring period are respectively obtained to obtain the dosages of the first to j-th drugs;

The first to j-th drug dosage, the number of emergency drug types, and the cumulative medical expenditure value of the period are defined as the period medical resource utilization data;

The physiological parameter unit acquires the periodic physiological index data corresponding to the m1 treatment monitoring cycle of the critically ill patient, and obtains the cumulative abnormal time ratio of the periodic physiological index;

The details are as follows:

In the m1th treatment monitoring cycle, the physiological indicators monitored for critically ill patients include the first to kth physiological indicators;

It should be noted here that:

The k involved here specifically refers to the number of types of physiological indicators for real-time monitoring of critically ill patients. In practical applications, the number of types of physiological indicators will be adjusted accordingly for different critically ill patients, and k is an integer greater than 0;

Performing periodic analysis on the first physiological indicator to obtain a first abnormal time ratio;

The details are as follows:

In the m1th treatment monitoring cycle, the first physiological index is monitored in real time by the monitoring equipment of the intensive care unit to obtain the value of the monitoring index;

If the value of the monitored indicator is within the normal interval corresponding to the first physiological indicator, it is determined that the first physiological indicator at the current moment is in the normal period;

If the value of the monitored indicator is not within the normal interval corresponding to the first physiological indicator, it is determined that the first physiological indicator at the current moment is in an abnormal period, and the time length of the abnormal period is obtained to obtain the abnormal duration of the first physiological indicator;

Obtaining the cumulative monitoring time of the first physiological indicator in the m1th treatment monitoring cycle to obtain the first cycle cumulative monitoring time, calculating the ratio of the abnormal time of the first physiological indicator to the first cycle cumulative monitoring time to obtain the first abnormal time ratio;

Repeat the process of acquiring the first abnormal time ratio, and acquire the abnormal time ratios of the second to k-th physiological indicators in the m1-th treatment monitoring cycle respectively, to obtain the second to k-th abnormal time ratios;

The first to kth abnormal time ratios are summed to obtain the cumulative abnormal time ratio of the periodic physiological index;

The condition weight unit analyzes the treatment symptoms of critically ill patients in the m1 treatment monitoring cycle to obtain the patient's condition weight;

The details are as follows:

The symptoms that occur in the patient during the m1th treatment monitoring cycle are divided into the first to tth treatment symptoms, and the symptom severity scores are respectively performed for the first to tth treatment symptoms to obtain the first to tth symptom severity scores;

It should be noted here that:

The specific treatment symptoms presented here need to be specifically set according to the actual symptoms of the severe illness;

In specific clinical practice, the symptom severity score needs to be set accordingly based on the patient's specific symptoms and combined with medical literature and the doctor's clinical diagnosis;

For example, if the patient is a tuberculosis patient, the first treatment symptom may be dyspnea, the second treatment symptom may be chest pain, the third treatment symptom may be fever, the fourth treatment symptom may be cough, and the fifth treatment symptom may be a painful expression recognized by the facial expression recognition model. The first to fifth treatment symptoms are scored as F1 to F5, respectively, and F1>F2>F3>F4>F5>0, to obtain the severity scores of the first to fifth symptoms;

Count the number of occurrences of the first to tth treatment symptoms in the m1th treatment monitoring period, and obtain the number of occurrences of the first to tth symptoms;

The patient's condition weight corresponding to the m1th treatment monitoring period is obtained by calculating the number of occurrences of the first to tth symptoms and the severity scores of the first to tth symptoms corresponding to the m1th treatment monitoring period;

The details are as follows:

;

Among them, Zym is the patient's condition weight corresponding to the m1th treatment monitoring cycle, Zc1 to Zct are the number of occurrences of the first to tth symptoms, and Zy1 to Zyt are the severity scores of the first to tth symptoms, respectively;

Please refer to FIG3 , repeat the process of obtaining the facial target image cycle time ratio, the patient's condition weight, the periodic medical resource usage data, and the periodic physiological index cumulative abnormal time ratio corresponding to the m1th treatment monitoring cycle, respectively obtain the facial target image cycle time ratio, the periodic medical resource usage data, and the periodic physiological index cumulative abnormal time ratio corresponding to each treatment monitoring cycle, and define them and the patient's typical symptom data as the condition cycle monitoring data;

The data acquisition module monitors the disease periodic data and transmits it to the data analysis module and the monitoring and early warning module;

The data analysis module performs periodic condition analysis on critically ill patients based on the periodic condition monitoring data to obtain periodic condition analysis data;

Obtaining disease cycle monitoring data, and obtaining facial target image cycle duration ratio, patient disease weight, cycle medical resource usage data, and cycle physiological index cumulative abnormal time ratio corresponding to the m1 treatment monitoring cycle according to the disease cycle monitoring data;

According to the periodic medical resource usage data, the first to j-th drug usage dosages, the number of emergency drug types and the periodic cumulative medical expenditure values corresponding to the m1-th treatment monitoring period are obtained respectively;

The period medical resource utilization coefficient corresponding to the m1th treatment monitoring period is obtained by calculating the dosage of the first to jth drugs, the number of emergency drug types and the cumulative medical expenditure value of the period through a formula;

The formula is as follows:

;

Among them, Zzy is the period medical resource utilization coefficient, Jl1 to Jlj are the first to jth drug dosages, j is the quantity value corresponding to the emergency drug type, Jzl is the number of emergency drug types, and Lhf is the period cumulative medical expenditure value;

The periodic medical resource utilization coefficient, the facial target image period duration ratio corresponding to the m1th treatment monitoring period, the patient's condition weight, and the cumulative abnormal time ratio of the periodic physiological indicators are calculated by a formula to obtain the periodic condition reference index corresponding to the m1th treatment monitoring period;

The formula is as follows:

;

Among them, Zzs1 is the reference index of the m1th cycle condition, Zym is the patient condition weight, Zzy is the cycle medical resource utilization coefficient, Ysb is the cumulative abnormal time ratio of the cycle physiological index, and Mtb is the facial target image cycle duration ratio;

Repeat the process of obtaining the periodic disease condition reference index corresponding to the m1th treatment monitoring cycle, and obtain the periodic disease condition reference indexes corresponding to the m2th to mkth treatment monitoring cycles respectively;

The periodic disease reference index corresponding to the treatment monitoring period from m1 to mk is defined as disease period analysis data;

The data analysis module acquires the disease cycle analysis data and transmits it to the change monitoring module and the monitoring and early warning module;

The change monitoring module monitors the condition changes of critically ill patients according to the condition cycle analysis data, and obtains the condition change monitoring data of critically ill patients;

Obtaining disease cycle analysis data, and obtaining the cycle disease reference index corresponding to the treatment monitoring cycle from m1 to mk according to the disease cycle analysis data;

The periodic disease condition reference indexes corresponding to the treatment monitoring periods from m1 to mk are named as the periodic disease condition reference indexes from m1 to mk respectively;

Obtain time values for the start time of the treatment monitoring cycles m1 to mk respectively, and obtain the start time values of the cycles m1 to mk;

Calculate the average of the start time values of the cycles m1 to mk to obtain the average start time of the cycle;

Calculate the average of the disease condition reference indexes from cycle m1 to cycle mk to obtain the average of the disease condition reference indexes;

The covariance of the patient's condition cycle change is obtained by calculating the average value of the cycle start time, the values of the start time of the m1 to mk cycles, the average value of the cycle condition reference index, and the m1 to mk cycle condition reference index;

The covariance of the patient's condition periodic changes is calculated, and the specific formula configuration is as follows:

;

Among them, Cov (X, Y) is the covariance of the patient's condition cycle change, X1 to Xmk are the starting time values of the m1 to mk cycles, and Y1 to Ymk are the reference indexes of the m1 to mk cycles. is the average of the cycle start time, is the average value of the periodic disease reference index;

The first cycle covariance change threshold and the second cycle covariance change threshold are obtained respectively, and the patient's condition cycle change covariance is numerically compared with the first cycle covariance change threshold and the second cycle covariance change threshold to obtain the monitoring data of the condition change of the critically ill patient;

It should be noted here that:

The first cycle covariance change threshold is greater than 0, the second cycle covariance change threshold is less than 0, and the absolute values of the first cycle covariance change threshold and the second cycle covariance change threshold are the same;

The details are as follows:

When the patient's condition periodic change covariance is greater than or equal to the first period covariance change threshold, it is judged that the severe condition is in the first condition change interval;

When the patient's condition periodic change covariance is less than the first period covariance change threshold and greater than the second period covariance change threshold, it is judged that the patient is in the second condition change interval;

When the patient's condition periodic change covariance is less than or equal to the second period covariance change threshold, it is judged that the severe condition is in the third condition change interval;

It should be noted here that:

The critically ill patients corresponding to the first condition change interval are in the stage of periodic worsening of their condition, the critically ill patients corresponding to the third condition change interval are in the stage of periodic improvement of their condition, and the critically ill patients corresponding to the first condition change interval have no obvious periodic changes in their condition;

The monitoring and early warning module conducts monitoring and early warning based on the monitoring data of the critically ill patients’ condition changes;

Obtain monitoring data on the condition changes of critically ill patients;

When a critically ill patient is in the first condition change interval, the monitoring system issues an early warning of periodic deterioration of the condition;

When the critically ill patient is in the third condition change interval, the monitoring system monitors the condition change of the critically ill patient to normal;

When a critically ill patient is in the second condition change range, the monitoring system will assess the condition of the critically ill patient and issue an early warning based on the assessment results;

The details are as follows:

Acquire the disease cycle monitoring data, and acquire the abnormal time ratio corresponding to the mkth treatment monitoring cycle according to the disease cycle monitoring data to obtain the mkth abnormal time ratio;

Obtain the patient's typical symptom data, and obtain the ratio of the number of symptoms with consistent sequence and the similarity ratio of the patient's typical symptoms based on the patient's typical symptom data;

The coefficient of critically ill patients' condition assessment is obtained by calculating the ratio of mk abnormal time, the ratio of the number of symptoms with consistent sequence, and the similarity ratio of typical symptoms of patients;

The coefficient of critically ill patients' condition assessment is calculated, and the specific formula is configured as follows:

;

Among them, Zyp is the coefficient for judging the condition of critically ill patients, Ymk is the ratio of the mkth abnormal time, Sxy is the ratio of the number of symptoms with consistent sequence, and Hdx is the similarity ratio of typical symptoms of patients;

Obtaining a threshold value of a coefficient for judging the condition of a critically ill patient, and comparing the coefficient for judging the condition of a critically ill patient with the threshold value of the coefficient for judging the condition of a critically ill patient;

The details are as follows:

Obtain the mkth abnormal time ratio threshold, the sequential consistent symptom quantity ratio threshold, and the patient's typical symptom similarity ratio threshold respectively;

The threshold of the coefficient of critical patient condition assessment is obtained by calculating the mkth abnormal time ratio threshold, the threshold of the number of symptoms with consistent sequence ratio, and the threshold of the similarity ratio of typical symptoms of patients;

The threshold of the coefficient for judging the condition of critically ill patients is calculated, and the specific formula configuration is as follows:

;

Among them, Zypy is the threshold of the coefficient of critically ill patients, Ymky is the threshold of the ratio of the mkth abnormal time, Sxyy is the threshold of the ratio of the number of symptoms with consistent order, and Hdxy is the threshold of the similarity ratio of the typical symptoms of patients;

When the critical patient condition assessment coefficient ratio is greater than the critical patient condition assessment coefficient threshold, the monitoring system issues a critical patient treatment warning;

When the critical patient condition assessment coefficient is less than or equal to the critical patient condition assessment coefficient threshold, the monitoring system monitors the critical patient's condition normally;

In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims (9)

1. A patient monitoring and management system for critical care units based on data analysis, characterized by comprising: Data acquisition module: used to divide the time period of critically ill patients receiving treatment into multiple treatment monitoring cycles; obtain the ratio of the number of symptoms with consistent order and the similarity ratio of the patient's typical symptoms corresponding to each treatment monitoring cycle to obtain the patient's typical symptom data; The data acquisition module includes a symptom data unit, which acquires typical symptom data of the patient; The symptom data unit acquires typical symptom data of the patient, as follows: Obtain the cumulative symptom types that occur during the period of time when the critically ill patient receives treatment in the critical care department, obtain the first appearance time value corresponding to each type of symptom during the patient monitoring process, and obtain multiple symptom first appearance time values; According to the values of the first manifestation time of multiple symptoms, the corresponding symptom types are named as the first to the hth clinical symptoms in order from far to near; Obtain several patients with the same symptoms as the critically ill patients as sample patients, obtain the text of the clinical symptoms corresponding to each sample patient, and obtain the clinical symptom record texts of multiple sample patients; Performing identical text matching on clinical symptom record texts of multiple sample patients, taking clinical symptoms existing in the clinical symptom record texts of each sample patient as typical clinical symptoms, and obtaining multiple typical clinical symptoms; The number of digits of each typical clinical symptom appearing in the corresponding sample patients is obtained respectively, and a plurality of different rank orders corresponding to each typical clinical symptom is obtained; the mode is obtained for a plurality of different rank orders corresponding to each typical clinical symptom respectively, and a typical rank order corresponding to each typical clinical symptom is obtained; Compare the first to h clinical symptoms with multiple typical clinical symptoms respectively, count the clinical symptoms with the same comparison results, obtain the second symptom number, calculate the ratio of the second symptom number to h, and obtain the patient's typical symptom similarity ratio; Obtain whether the typical sequence rank corresponding to each typical clinical symptom is consistent with the ranks corresponding to the first to the hth clinical symptoms; If they are consistent, the corresponding clinical symptoms are marked as sequentially consistent clinical symptoms; If they are inconsistent, the corresponding clinical symptoms are marked as sequence-inconsistent clinical symptoms; Counting the number of clinical symptoms marked as sequentially consistent from the first to the hth clinical symptoms, and obtaining the number of clinical symptoms in the regular sequence; Calculate the ratio of the number of clinical symptoms in the conventional sequence to h to obtain the ratio of the number of symptoms in the consistent sequence; The ratio of the number of sequentially consistent symptoms and the similarity ratio of the patient's typical symptoms were defined as the patient's typical symptom data; Acquire facial images of critically ill patients in each treatment monitoring cycle, and divide the facial images into target type facial recognition images and non-target type facial recognition images, calculate the ratio of the appearance duration of the target type facial recognition images to the duration of the corresponding image cycle, and obtain the facial target image cycle duration ratio; The abnormal time period of the physiological indicators of critically ill patients in each treatment monitoring cycle is judged, and the ratio of the abnormal duration of each indicator to the corresponding cumulative monitoring duration of the cycle is calculated to obtain multiple abnormal time ratios, and the multiple abnormal time ratios are summed to obtain the cumulative abnormal time ratio of the periodic physiological indicators; The severity score and frequency of symptoms in each treatment monitoring cycle were calculated to obtain the patient condition weights corresponding to different treatment monitoring cycles; The amount of medicine and the cost of each treatment monitoring cycle of critically ill patients are obtained to obtain the periodic medical resource usage data; the data obtained in the treatment monitoring cycle is defined as the disease cycle monitoring data; Data analysis module: Calculate the periodic medical resource utilization coefficient corresponding to each treatment monitoring period according to the periodic monitoring data of the disease state, and calculate the periodic disease state reference index by combining the periodic duration ratio of the facial target image, the patient's disease state weight and the cumulative abnormal time ratio of the periodic physiological index to obtain the periodic disease state analysis data; Change monitoring module: used to calculate the patient's condition cycle change covariance based on the condition cycle analysis data, and divide the critically ill patients into the first condition change interval, the second condition change interval and the third condition change interval according to the patient's condition cycle change covariance, so as to obtain the critically ill patients' condition change monitoring data; Monitoring and early warning module: used for monitoring and early warning based on the disease cycle monitoring data and the monitoring data of the condition changes of critically ill patients. 2. According to the data analysis-based patient monitoring and management system for critical care units in claim 1, the data acquisition module further comprises a visual data unit, a disease weight unit, a medical data unit and a physiological parameter unit; During the time period when the critically ill patients are receiving treatment in the intensive care unit, mk treatment monitoring cycles are marked respectively, and the cycle start time corresponding to each treatment monitoring cycle is numerically obtained to obtain mk cycle start time values, and the time value corresponding to the current moment is obtained as the reference time value, and the difference between the mk cycle start time values and the reference time value is obtained respectively to obtain mk time difference values; Arrange the mk time difference values in order according to their numerical values, and name the treatment monitoring cycles corresponding to the mk time difference values as the m1 to mk treatment monitoring cycles according to the arrangement order from large to small; The visual data unit acquires and analyzes facial images of critically ill patients in the m1th treatment monitoring cycle to obtain the facial target image cycle duration ratio; The medical data unit obtains the periodic medical resource usage data of the critically ill patients in the m1th treatment monitoring cycle; The physiological parameter unit acquires the periodic physiological index data of the critically ill patient in the m1th treatment monitoring cycle to obtain the cumulative abnormal time ratio of the periodic physiological index; The condition weight unit analyzes the treatment symptoms of the critically ill patients in the m1th treatment monitoring cycle to obtain the patient's condition weight; The facial target image cycle duration ratio, cycle medical resource usage data, and cycle physiological index cumulative abnormal time ratio corresponding to each treatment monitoring cycle are obtained respectively, and defined as the disease cycle monitoring data together with the patient's typical symptom data. 3. According to the data analysis-based intensive care unit patient monitoring and management system of claim 2, it is characterized in that the visual data unit acquires the facial target image cycle duration ratio as follows: In the m1th treatment monitoring cycle, the facial image of the critically ill patient is acquired in real time by the first condition monitoring device to obtain a video stream of the facial image of the patient, and the video duration of the video stream of the facial image of the patient is acquired to obtain the duration of the first image cycle; The video stream of the patient's facial image is intercepted into a plurality of patient's facial images in units of a single video frame, and the playing time of a single video frame in the video stream of the patient's facial image is obtained to obtain a unit video frame time; Establish a facial expression recognition model to perform image recognition on multiple patient facial images respectively, divide each patient facial image into a target type facial recognition image and a non-target type facial recognition image respectively, count the number of target type facial recognition images, and obtain the number of target type images; The product of the number of target type images and the unit video frame duration is calculated, and then the ratio of the obtained product to the first image cycle duration is calculated to obtain the facial target image cycle duration ratio. 4. According to the data analysis-based intensive care unit patient monitoring and management system of claim 2, it is characterized in that the medical data unit acquires periodic medical resource usage data as follows: Obtain the medical expenses of critically ill patients corresponding to the m1 treatment monitoring cycle through online medical records, and obtain the cumulative medical expenses value of the cycle; According to the online medical records corresponding to the critically ill patients, the number of types of emergency drugs used by the critically ill patients in the m1th treatment monitoring cycle is obtained, and the emergency drugs used are named as the first to jth types of emergency drugs respectively; The dosages of the first to j-th types of emergency drugs in the m1-th treatment monitoring period are respectively obtained to obtain the dosages of the first to j-th drugs; The dosage of the first to jth drugs, the number of emergency drug types, and the cumulative medical expenditure value of the period are defined as the period medical resource utilization data. 5. According to a data analysis-based patient monitoring and management system for critical care units in claim 2, it is characterized in that the physiological parameter unit obtains the cumulative abnormal time ratio of the periodic physiological index, specifically as follows: In the m1th treatment monitoring cycle, the physiological indicators monitored for critically ill patients include the first to kth physiological indicators; Performing periodic analysis on the first physiological indicator to obtain a first abnormal time ratio; The details are as follows: In the m1th treatment monitoring cycle, the first physiological index is monitored in real time by the monitoring equipment of the intensive care unit to obtain the value of the monitoring index; If the value of the monitored indicator is within the normal interval corresponding to the first physiological indicator, it is determined that the first physiological indicator at the current moment is in the normal period; If the value of the monitored indicator is not within the normal interval corresponding to the first physiological indicator, it is determined that the first physiological indicator at the current moment is in an abnormal period, and the time length of the abnormal period is obtained to obtain the abnormal duration of the first physiological indicator; Obtaining the cumulative monitoring time of the first physiological indicator in the m1th treatment monitoring cycle to obtain the first cycle cumulative monitoring time, calculating the ratio of the abnormal time of the first physiological indicator to the first cycle cumulative monitoring time to obtain the first abnormal time ratio; Repeat the process of acquiring the first abnormal time ratio, and acquire the abnormal time ratios of the second to k-th physiological indicators in the m1-th treatment monitoring cycle respectively, to obtain the second to k-th abnormal time ratios; The first to kth abnormal time ratios are summed to obtain the cumulative abnormal time ratio of the periodic physiological index. 6. According to the data analysis-based patient monitoring and management system for critical care units in claim 2, it is characterized in that the condition weight unit obtains the patient's condition weight as follows: The symptoms that occur in the patient during the m1th treatment monitoring cycle are divided into the first to tth treatment symptoms, and the symptom severity scores are respectively performed for the first to tth treatment symptoms to obtain the first to tth symptom severity scores; Count the number of occurrences of the first to tth treatment symptoms in the m1th treatment monitoring period, and obtain the number of occurrences of the first to tth symptoms; The patient's condition weight corresponding to the m1th treatment monitoring period is obtained by calculating the number of occurrences of the first to tth symptoms and the severity scores of the first to tth symptoms corresponding to the m1th treatment monitoring period; The details are as follows: ; Among them, Zym is the patient's condition weight corresponding to the m1th treatment monitoring cycle, Zc1 to Zct are the number of occurrences of the first to tth symptoms, and Zy1 to Zyt are the severity scores of the first to tth symptoms, respectively. 7. According to the data analysis-based patient monitoring and management system for critical care units in claim 1, it is characterized in that the data analysis module acquires the disease cycle analysis data as follows: Obtaining disease cycle monitoring data, and obtaining facial target image cycle duration ratio, patient disease weight, cycle medical resource usage data, and cycle physiological index cumulative abnormal time ratio corresponding to the m1 treatment monitoring cycle according to the disease cycle monitoring data; According to the periodic medical resource usage data, the first to j-th drug usage dosages, the number of emergency drug types and the periodic cumulative medical expenditure values corresponding to the m1-th treatment monitoring period are obtained respectively; The period medical resource utilization coefficient corresponding to the m1th treatment monitoring period is obtained by calculating the dosage of the first to jth drugs, the number of emergency drug types and the cumulative medical expenditure value of the period through a formula; The formula is as follows: ; Among them, Zzy is the period medical resource utilization coefficient, Jl1 to Jlj are the first to jth drug dosages, j is the quantity value corresponding to the emergency drug type, Jzl is the number of emergency drug types, and Lhf is the period cumulative medical expenditure value; The periodic medical resource utilization coefficient, the facial target image period duration ratio corresponding to the m1th treatment monitoring period, the patient's condition weight, and the periodic physiological index cumulative abnormal time ratio are calculated by a formula to obtain the periodic condition reference index corresponding to the m1th treatment monitoring period; The formula is as follows: ; Among them, Zzs1 is the reference index of the m1th cycle condition, Zym is the patient condition weight, Zzy is the cycle medical resource utilization coefficient, Ysb is the cumulative abnormal time ratio of the cycle physiological index, and Mtb is the facial target image cycle duration ratio; Repeat the process of obtaining the periodic disease condition reference index corresponding to the m1th treatment monitoring cycle, and obtain the periodic disease condition reference indexes corresponding to the m2th to mkth treatment monitoring cycles respectively; The periodic disease reference index corresponding to the treatment monitoring period from m1 to mk is defined as disease period analysis data; The periodic analysis data is transmitted to the change monitoring module, and the change monitoring module obtains the monitoring data of the condition changes of critically ill patients. 8. A patient monitoring and management system for critical care units based on data analysis according to claim 7, characterized in that the change monitoring module acquires the monitoring data of the condition changes of critically ill patients, specifically as follows: Obtaining disease cycle analysis data, and obtaining the cycle disease reference index corresponding to the treatment monitoring cycle from m1 to mk according to the disease cycle analysis data; The periodic disease condition reference indexes corresponding to the treatment monitoring periods from m1 to mk are named as the periodic disease condition reference indexes from m1 to mk respectively; Obtain time values for the start time of the treatment monitoring cycles m1 to mk respectively, and obtain the start time values of the cycles m1 to mk; Calculate the average of the start time values of the cycles m1 to mk to obtain the average start time of the cycle; Calculate the average of the disease condition reference indexes from cycle m1 to cycle mk to obtain the average of the disease condition reference indexes; The covariance of the patient's condition cycle change is obtained by calculating the average value of the cycle start time, the values of the start time of the m1 to mk cycles, the average value of the cycle condition reference index, and the m1 to mk cycle condition reference index; The covariance of the patient's condition periodic changes is calculated, and the specific formula configuration is as follows: ; Among them, Cov (X, Y) is the covariance of the patient's condition cycle change, X1 to Xmk are the starting time values of the m1 to mk cycles, and Y1 to Ymk are the reference indexes of the m1 to mk cycles. is the average of the cycle start time, is the average value of the periodic disease reference index; The first cycle covariance change threshold and the second cycle covariance change threshold are obtained respectively, and the patient's condition cycle change covariance is numerically compared with the first cycle covariance change threshold and the second cycle covariance change threshold to obtain the monitoring data of the condition change of the critically ill patient; The numerical comparison process is as follows: When the patient's condition periodic change covariance is greater than or equal to the first periodic covariance change threshold, it is judged that the severe condition is in the first condition change interval; When the patient's condition periodic change covariance is less than the first period covariance change threshold and greater than the second period covariance change threshold, it is judged that the patient is in the second condition change interval; When the patient's condition periodic change covariance is less than or equal to the second period covariance change threshold, it is judged that the severe condition is in the third condition change interval; The monitoring data of the condition changes of critically ill patients are transmitted to the monitoring and early warning module. 9. A patient monitoring and management system for critical care units based on data analysis according to claim 8, characterized in that the monitoring and early warning module performs monitoring and early warning according to the monitoring data of the condition change of critically ill patients, as follows: Obtain monitoring data on the condition changes of critically ill patients; When a critically ill patient is in the first condition change interval, the monitoring system issues an early warning of periodic deterioration of the condition; When the critically ill patient is in the third condition change interval, the monitoring system monitors the condition change of the critically ill patient to normal; When a critically ill patient is in the second condition change range, the monitoring system will assess the condition of the critically ill patient and issue an early warning based on the assessment results; The details are as follows: Acquire the disease cycle monitoring data, and acquire the abnormal time ratio corresponding to the mkth treatment monitoring cycle according to the disease cycle monitoring data to obtain the mkth abnormal time ratio; Obtain the patient's typical symptom data, and obtain the ratio of the number of symptoms with consistent sequence and the similarity ratio of the patient's typical symptoms based on the patient's typical symptom data; The coefficient of critically ill patients' condition assessment is obtained by calculating the ratio of mk abnormal time, the ratio of the number of symptoms with consistent sequence, and the similarity ratio of typical symptoms of patients; The coefficient of critically ill patients' condition assessment is calculated, and the specific formula is configured as follows: ; Among them, Zyp is the coefficient for judging the condition of critically ill patients, Ymk is the ratio of the mkth abnormal time, Sxy is the ratio of the number of symptoms with consistent sequence, and Hdx is the similarity ratio of typical symptoms of patients; Obtaining a threshold value of a coefficient for judging the condition of a critically ill patient, and comparing the coefficient for judging the condition of a critically ill patient with the threshold value of the coefficient for judging the condition of a critically ill patient; The details are as follows: Obtain the mkth abnormal time ratio threshold, the sequential consistent symptom quantity ratio threshold, and the patient's typical symptom similarity ratio threshold respectively; The threshold of the coefficient of critical patient condition assessment is obtained by calculating the mkth abnormal time ratio threshold, the threshold of the number of symptoms with consistent sequence ratio, and the threshold of the similarity ratio of typical symptoms of patients; When the critical patient condition assessment coefficient ratio is greater than the critical patient condition assessment coefficient threshold, a critical patient treatment warning is issued; When the critically ill patient's condition assessment coefficient is less than or equal to the critically ill patient's condition assessment coefficient threshold, the critically ill patient's condition is monitored normally.