CN119132631A - An emergency pre-examination and triage system - Google Patents
An emergency pre-examination and triage system Download PDFInfo
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Abstract
本发明提供一种急诊预检分检系统，包括信息登记模块，用于登记患者基本信息及病史症状信息；预评估模块，用于根据患者基本信息及病史症状信息建立病状数据集，并根据病状数据集与医学资料文献中相关的分诊规则建立总数据集，根据总数据集进行分类器训练，并根据患者病情利用朴素贝叶斯算法进行分诊类别概率分析，并推荐初步治疗检查科室及医生；分级模块，根据预评估的结果，将患者按照病情的危急程度进行分级；后处理模块，用于将患者的就诊记录、初步的治疗检查科室及医生、分级信息进行归档保存，本发明通过算法自动分析患者的病状数据集，快速推荐初步治疗科室和医生，避免患者被错误地引导至不合适的科室，减少不必要的检查和治疗。
The present invention provides an emergency pre-examination and triage system, comprising an information registration module for registering basic information and medical history symptom information of patients; a pre-evaluation module for establishing a disease data set according to the basic information and medical history symptom information of patients, and establishing a total data set according to the disease data set and triage rules related to medical data documents, performing classifier training according to the total data set, and performing triage category probability analysis using a naive Bayes algorithm according to the patient's condition, and recommending a preliminary treatment and examination department and a doctor; a grading module for grading patients according to the severity of their condition according to the result of the pre-evaluation; and a post-processing module for archiving and storing the patient's medical records, preliminary treatment and examination departments and doctors, and grading information. The present invention automatically analyzes the patient's disease data set through an algorithm, quickly recommends a preliminary treatment department and a doctor, avoids the patient being mistakenly guided to an inappropriate department, and reduces unnecessary examinations and treatments.
Description
Technical Field
The invention relates to the field of sorting systems, in particular to an emergency pre-examination sorting system.
Background
Emergency refers to the emergency department of a hospital, meaning treatment in an emergency situation. The emergency treatment and the rescue are divided into emergency treatment and rescue. The medical inquiry method ensures that the doctor can obtain professional and scientific treatment in the shortest time when the patient is ill and hurt by accident, the inquiry refers to the inquiry process of the doctor for solving the cause, the disease course, the related symptoms and other conditions related to the disease, the main purpose of the inquiry is that the doctor obtains the disease information of the useful patient so as to determine the treatment direction and strategy, and the medical inquiry method is the basis of follow-up work and has important significance in the diagnosis of the disease. In the medical diagnosis and treatment process, it is desirable for a doctor to comprehensively understand the condition of a user to formulate an accurate treatment method, and for the user to obtain treatment as quickly and effectively as possible.
The existing emergency pre-examination and diagnosis system generally needs to wait for medical staff to arrive at an emergency treatment center in the use process, and then gradually starts to use the system through each function of the system, but the time is very precious in emergency treatment, emergency doctors still need to know the medical history and clinical conditions of patients before rescuing, if the patients wait for the patients to arrive at a hospital and then observe the patients, a great part of rescuing time can be wasted, and the rescuing is not timely.
Therefore, it is necessary to provide a new emergency pre-examination and sorting system to solve the above technical problems.
Disclosure of Invention
In order to solve the technical problems, the invention provides an emergency pre-examination and sorting system.
The emergency pre-examination and sorting system provided by the invention comprises an information registration module, a control module and a control module, wherein the information registration module is used for registering basic information and medical history symptom information of patients;
The pre-evaluation module is used for establishing a pathology data set according to basic information and medical history symptom information of a patient, establishing a total data set according to diagnosis rules related to the pathology data set and medical data documents, performing classifier training according to the total data set, performing diagnosis classification probability analysis according to the illness state of the patient by using a naive Bayesian algorithm, and recommending a preliminary treatment examination department and a doctor according to analysis results;
The grading module is used for grading the patient according to the critical degree of the illness state according to the pre-evaluation result, wherein the critical degree is graded into five grades and is respectively corresponding to non-emergency, sub-emergency, very emergency and immediate treatment;
the post-processing module is used for archiving and storing the treatment record of the patient, the preliminary treatment examination department, the doctor and the grading information;
An alarm module that immediately sounds an alarm when both a very urgent situation and an immediate processing situation are encountered.
S1, reading a triage rule data set to be trained, wherein each group of data comprises symptom combinations and corresponding classification labels;
S2, circularly training all data in the triage rule data set to generate a symptom vocabulary summary containing all symptom feature words;
s3, according to the symptom vocabulary table, circularly training all data in the triage rule data set to generate a feature vector corresponding to each piece of data;
S4, circularly classifying the triage class set, and calculating the prior probability and class conditional probability of each classification label by adopting a polynomial model;
S5, calculating the inverse document frequency of each feature word in the symptom vocabulary table, and generating a corresponding adjusting factor according to the degree of subject classification related to the current symptom feature.
Further, the naive bayes algorithm includes: Where c is the triage class set of the sample, P (c) is the probability of class occurrence, P (w|c) is the class conditional probability of the sample w relative to the class label c, i.e. the probability of the feature vector w relative to the sample class c, and P (w) is the probability of the feature vector occurrence.
Further, data preprocessing is required for patient base information and medical history symptom information prior to establishing a pathology data set, the data preprocessing including removal of nonsensical symbols, missing value processing, data normalization and data normalization.
Further, the patient basic information includes patient name, patient gender, patient age, patient condition duration.
Further, a tracking mechanism is built in the post-treatment module for a particular patient to periodically record the patient's treatment progress, condition changes and medication.
Compared with the related art, the emergency pre-examination and sorting system provided by the invention has the following beneficial effects:
According to the invention, a pathology data set is established according to basic information and medical history symptom information of a patient, a total data set is established according to diagnosis dividing rules related to the pathology data set and medical data literature, classifier training is carried out according to the total data set, diagnosis dividing type probability analysis is carried out according to the illness state of the patient by using a naive Bayesian algorithm, a preliminary treatment examination department and a doctor are recommended according to analysis results, the pathology data set of the patient is automatically analyzed through the algorithm, the preliminary treatment department and the doctor are rapidly recommended, the pressure of medical staff is reduced, the patient is prevented from being wrongly guided to an unsuitable department, and unnecessary examination and treatment are reduced.
Drawings
FIG. 1 is a block diagram of an emergency pre-examination and sorting system provided by the invention;
fig. 2 is a block flow diagram of classifier training provided by the present invention.
Detailed Description
The invention will be further described with reference to the drawings and embodiments.
Referring to fig. 1 and fig. 2 in combination, fig. 1 is a block diagram of an emergency pre-examination and sorting system provided by the present invention, and fig. 2 is a block diagram of a classifier training flow provided by the present invention.
In a specific implementation process, as shown in fig. 1-2, the emergency pre-examination and sorting system comprises an information registration module, which is used for registering basic information of a patient and symptom information of medical history, wherein the basic information of the patient comprises the name of the patient, the gender of the patient, the age of the patient, the pathology of the patient and the duration of the pathology of the patient;
The pre-evaluation module is used for establishing a pathology data set according to basic information and medical history symptom information of a patient, establishing a total data set according to diagnosis dividing rules related to the pathology data set and medical data documents, performing classifier training according to the total data set, performing diagnosis dividing type probability analysis by using a naive Bayesian algorithm according to the illness state of the patient, recommending a preliminary treatment examination department and a doctor according to an analysis result, automatically analyzing the pathology data set of the patient through the algorithm, rapidly recommending the preliminary treatment department and the doctor, relieving the pressure of medical staff, avoiding the patient from being mistakenly guided to an unsuitable department, and reducing unnecessary examination and treatment;
The grading module is used for grading the patients according to the critical degree of the illness state according to the pre-evaluation result, and the critical degree is graded into five grades, and the five grades correspond to non-emergency, sub-emergency, very emergency and immediate treatment respectively:
Grade V (non-emergency) patients are slightly ill, can wait several hours or even schedule for consultation, such as minor abrasion, small-scale burn, etc.;
Grade IV (non-emergency) patients are relatively stable and can wait longer (typically no more than 1 hour) for example, mild pain, chronic episodes, etc.;
Grade III (emergency) patients are ill, but can wait for a period of time (typically no more than 30 minutes), such as acute abdominal pain, moderate trauma, mild poisoning, etc.;
Class II (very urgent) patients are potentially life threatening and need to receive treatment within 10 minutes, such as acute chest pain, severe dyspnea, severe poisoning, etc.;
class I (immediate treatment) patients are in life threatening conditions requiring immediate intervention, such as sudden cardiac arrest, severe traumatic hemorrhage, acute airway obstruction, etc.;
the post-processing module is used for archiving and storing the treatment record of the patient, the preliminary treatment examination department, the doctor and the grading information;
It should be noted that, for a specific patient (such as three patients without patient, group injury patient, etc.), a tracking mechanism is built in the post-processing module to record the treatment progress, disease change and medication condition of the patient regularly;
The tracking mechanism comprises 1, performing emergency treatment immediately after the patient is admitted, starting the tracking mechanism at the same time, collecting patient information and establishing an information file;
2. For three patients, searching for identity information and family members through a plurality of channels such as public security departments, social media and the like, and for group injury patients, timely communicating with an accident handling department to acquire a wounded list and family member contact ways;
3. Tracking the treatment process, namely recording the treatment progress, the disease change and the medication condition of a patient at regular intervals, and adjusting the treatment scheme in time;
4. The discharge schedule and the follow-up tracking, wherein, before the discharge of patients, a detailed discharge schedule is established, including follow-up treatment, rehabilitation training and review schedule, for three patients, the economic sources and the arrangement problems of the patients are assisted to be solved, and for group wound patients, the clear discharge guidance and the follow-up schedule of each patient are ensured;
And the alarm module can immediately give an alarm when the alarm module encounters a very urgent situation and immediately processes the two situations.
Function of post-processing module
1. Data archiving:
The medical records are archived, and the medical records of the patient are collated and archived, including personal information of the patient, medical history symptom information, diagnosis results, treatment suggestions and the like.
And (5) archiving the information of the preliminary treatment examination departments and doctors, namely storing the information of the preliminary treatment examination departments and doctors recommended by the triage system.
And the grading information is filed, and the grading information is recorded according to the severity of the illness state of the patient, so that the follow-up tracking and management are convenient.
2. Data analysis and reporting:
Statistical analysis the patient's records of visits are analyzed statistically at regular intervals to identify common diseases, epidemic trends, etc.
Quality control by analyzing the data, evaluating the quality and efficiency of medical services, and finding out room for improvement.
3. Patient tracking:
and the follow-up reminding is set according to the illness state and the treatment progress of the patient, so that the patient can review on time.
Efficacy assessment by tracking the progress of treatment of the patient, the efficacy of the treatment is assessed.
4. Data security and privacy protection:
And (3) encrypting the data, namely encrypting the sensitive information to ensure the data security.
Access control, setting different access rights to ensure that only authorized personnel can access the medical records of the patient
5. Interface and integration:
the system is integrated with an electronic medical record system, ensures that a post-processing module can be in seamless connection with the existing electronic medical record system of a hospital, and supports the export of archived data into a standard format for further analysis or sharing with other systems.
Effects of the post-processing Module
1. Improving the medical quality:
Continuous nursing by keeping detailed records of the visits, doctors can better understand the medical history of patients and provide continuous nursing.
Tracking therapeutic effect of patient, and timely adjusting therapeutic scheme
2. And the working efficiency is improved:
automatic filing, namely automatically filing the doctor records of the patient, and relieving the manual input burden of medical staff.
And data sharing, namely simplifying the data sharing flow and improving the collaboration efficiency among multiple departments.
3. Improving patient experience:
personalized services providing personalized medical services based on patient history and treatment records reduces latency by an efficient archiving and querying system.
4. Promoting scientific research:
Data accumulation-long-term accumulated visit records can provide valuable data resources for clinical research.
Trend analysis, namely identifying the development trend of diseases and potential public health problems through analysis of historical data.
5. Strengthening supervision compliance:
And data integrity, namely ensuring that all medical records are properly stored, and meeting the requirements of laws and regulations.
Privacy protection, namely, adhering to related privacy protection regulations and ensuring the safety of patient information.
It should be noted that the classifier training includes the following steps:
s1, reading a triage rule data set T to be trained, wherein each group of data comprises symptom combinations and corresponding classification labels;
S2, circularly training all data in the triage rule data set T to generate a symptom vocabulary summary K containing all symptom feature words;
S3, according to the symptom vocabulary table K, circularly training all data in the triage rule data set T to generate a feature vector w corresponding to each piece of data;
S4, circularly classifying the triage class set C, and calculating the prior probability p (C) and the class conditional probability p (w|c) of each classification label by adopting a polynomial model;
S5, calculating the inverse document frequency d of each feature word in the symptom vocabulary table K, and generating a corresponding adjusting factor according to the number of subject classifications related to the current symptom features, wherein the more the subject classifications related to the symptoms are, the lower the significance and the importance of the subject classifications are for distinguishing subjects, and the lower the corresponding weight is.
In one specific embodiment, the naive bayes algorithm comprises: Wherein c is a triage class set of the sample, P (c) is the probability of class occurrence, P (w|c) is the class conditional probability of the sample w relative to the class mark c, namely the probability of the feature vector w relative to the sample class c, P (w) is the probability of the feature vector occurrence, a weight factor d is introduced for the feature vector w, w '=wd is defined, w' is the feature vector after the weight factor d is introduced, and d is a weight factor matrix trained from sample data based on an IDF algorithm;
Wherein the IDF calculation formula is Wherein D is the total dataset, which is divided into a pathology dataset U and a triage rule dataset T.
Data preprocessing is required for patient basic information and medical history symptom information before a pathology data set is established, and includes removal of meaningless symbols, missing value processing, data normalization and data normalization.
The data preprocessing is specific:
1. nonsensical symbols are removed, and "& quot", line-feed symbols and space symbols in the data are removed;
2. missing value processing, marking data under the condition of existence of duplicate names and further complementing information
3. Data normalization, normalizing sex characteristics according to the description of the patient, using "0" for female and "1" for male;
4. Data normalization, and the normalization processing of age characteristics adopts the formula:
From the above description of embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus a general purpose hardware platform, or may be implemented by hardware. Based on such understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the related art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the method described in the respective embodiments or some parts of the embodiments.
While the fundamental and principal features of the invention and advantages of the invention have been shown and described, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing exemplary embodiments, but may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
Claims (8)
1. The emergency pre-examination and sorting system is characterized by comprising an information registration module, a control module and a control module, wherein the information registration module is used for registering basic information and medical history symptom information of a patient;
The pre-evaluation module is used for establishing a pathology data set according to basic information and medical history symptom information of a patient, establishing a total data set according to diagnosis rules related to the pathology data set and medical data documents, performing classifier training according to the total data set, performing diagnosis classification probability analysis according to the illness state of the patient by using a naive Bayesian algorithm, and recommending a preliminary treatment examination department and a doctor according to analysis results;
the grading module is used for grading the patients according to the critical degree of the illness state according to the pre-evaluation result;
And the post-processing module is used for archiving and storing the treatment records of the patient, the preliminary treatment examination departments, doctors and grading information.
2. The emergency pre-examination and sorting system according to claim 1, wherein the classifier training comprises the steps of S1, reading a triage rule data set to be trained, each group of data comprising symptom combinations and corresponding classification labels;
S2, circularly training all data in the triage rule data set to generate a symptom vocabulary summary containing all symptom feature words;
s3, according to the symptom vocabulary table, circularly training all data in the triage rule data set to generate a feature vector corresponding to each piece of data;
S4, circularly classifying the triage class set, and calculating the prior probability and class conditional probability of each classification label by adopting a polynomial model;
S5, calculating the inverse document frequency of each feature word in the symptom vocabulary table, and generating a corresponding adjusting factor according to the degree of subject classification related to the current symptom feature.
3. The emergency pre-screening system of claim 2, wherein the naive bayes algorithm comprises: Where c is the triage class set of the sample, P (c) is the probability of class occurrence, P (w|c) is the class conditional probability of the sample w relative to the class label c, i.e. the probability of the feature vector w relative to the sample class c, and P (w) is the probability of the feature vector occurrence.
4. The emergency pre-screening system of claim 3, wherein the patient base information and medical history symptom information are pre-processed prior to establishing the pathology data set, the pre-processing including removal of nonsensical symbols, missing value processing, data normalization, and data normalization.
5. The emergency pre-screening system of claim 4, wherein the patient basic information includes patient name, patient gender, patient age, patient condition duration.
6. The emergency pre-screening system of claim 5, wherein a tracking mechanism is established in the post-establishment processing module for a particular patient to periodically record patient treatment progress, condition changes, and medication.
7. The emergency pre-screening system of claim 6, wherein the criticality score is five-level, corresponding to non-emergency, sub-emergency, very emergency and immediate treatment, respectively.
8. The emergency pre-screening system of claim 7, further comprising an alarm module that immediately alerts when both a very urgent situation and an immediate processing situation are encountered.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119418916A (en) * | 2025-01-09 | 2025-02-11 | 吉林大学 | Intelligent clinical support decision system and method for emergency triage |
| CN119626492A (en) * | 2025-02-11 | 2025-03-14 | 浙江赛卜恩科技有限公司 | Intelligent collection method and system of medical record data based on smart hospital |
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2024
- 2024-08-13 CN CN202411108037.6A patent/CN119132631A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119418916A (en) * | 2025-01-09 | 2025-02-11 | 吉林大学 | Intelligent clinical support decision system and method for emergency triage |
| CN119626492A (en) * | 2025-02-11 | 2025-03-14 | 浙江赛卜恩科技有限公司 | Intelligent collection method and system of medical record data based on smart hospital |
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