CN112802612B - Medication analysis method, device, equipment and storage medium based on schizophrenia - Google Patents
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Abstract
The embodiment of the application discloses a medication analysis method, a medication analysis device, medication analysis equipment and a storage medium based on schizophrenia. The method comprises the following steps: acquiring sample data of medication effects of a plurality of schizophrenia; training a data model of medication of the schizophrenia according to the sample data; and obtaining optimal medication data required by the schizophrenic patient according to the data model. According to the application, the optimal medication data can be automatically obtained according to the clinical condition of the patient with the schizophrenia, the problem of unscientific medication of the schizophrenia in the prior art is effectively solved, and the medication of the schizophrenia can be optimized so as to improve the treatment effect of the schizophrenia.
Description
Technical Field
The application relates to the technical field of medication analysis, in particular to a medication analysis method, a medication analysis device, medication analysis equipment and a storage medium based on schizophrenia.
Background
Schizophrenia is the most serious disease of the psychiatric department, and is mostly caused by young and old people, and is often caused by special thinking, perception, emotion, behavior and other disorders, and the schizophrenia needs to be treated by long-term or lifelong antipsychotic drugs. The heterogeneity of the schizophrenia is high, individual clinical symptoms are greatly different, the response of treatment is different, the course and the ending are quite different, the treatment of the schizophrenia is greatly dependent on the subjective experience of clinicians, and the lack of tools for objectively and quantitatively evaluating the clinical curative effect is a key problem to be solved in the clinical urgency of the schizophrenia.
Wherein, repeated "empirical trial and error treatment" may strengthen negative cognition of schizophrenia and reduce treatment compliance, thereby increasing treatment difficulty, high recurrence rate, onset rate and mortality rate. Therefore, the prior art needs improvement urgently to break through the bottleneck of 'empirical trial-and-error treatment', and the accurate individual treatment of the schizophrenia is possible, so that the cure rate of the patients with the schizophrenia is improved, and the disability rate and the socioeconomic burden are reduced.
Disclosure of Invention
In view of the foregoing drawbacks or shortcomings of the prior art, it is desirable to provide a method, apparatus, device and storage medium for medication analysis based on schizophrenia to achieve optimal medication effects for schizophrenia.
According to one aspect of the embodiment of the application, the embodiment of the application provides a medication analysis method based on schizophrenia, which comprises the following steps:
acquiring sample data of medication effects of a plurality of schizophrenia;
training a data model of medication of the schizophrenia according to the sample data;
and obtaining optimal medication data required by the schizophrenic patient according to the data model.
In one embodiment, the training the data model for medication therapy of schizophrenia based on the sample data comprises:
Counting medication data of patients with schizophrenia, and obtaining a first data model of a plurality of different medication conditions of a plurality of patients with schizophrenia;
acquiring a second data model after classifying different clinical characteristics of the schizophrenic patient according to the first data model;
and acquiring a third data model after clustering the medication condition of the schizophrenic patient according to the second data model.
In one embodiment, the statistics of medication data of patients with schizophrenia acquire a first data model of a plurality of different medication situations of a plurality of patients with schizophrenia, including:
selecting a first machine learning module from a group comprising at least one first machine learning module, performing feature extraction on clinical data and medication data of the schizophrenic patient;
acquiring first data of different medication situations of a patient suffering from schizophrenia;
repeating the steps until a plurality of first data of different medication conditions corresponding to the clinical data of the schizophrenic patient are obtained;
the plurality of first data is aggregated into a first data model.
In one embodiment, the obtaining the second data model classified by different clinical characteristics of the schizophrenic patient according to the first data model includes:
Selecting a second machine learning module from the group consisting of at least one second machine learning module, performing a clinical feature extraction on the first data model of the schizophrenic patient;
acquiring second data of a schizophrenic patient after different clinical feature classifications;
repeating the steps until a plurality of second data of all the schizophrenic patients after different clinical feature classifications are obtained;
the plurality of second data is aggregated into a second data model.
In one embodiment, the obtaining, according to the second data model, a third data model after clustering medication conditions of the schizophrenic patient includes:
selecting a third machine learning module from a group comprising at least one third machine learning module, and performing medication feature extraction on a second data model of the schizophrenic patient;
acquiring third data after clustering medication features of a patient suffering from schizophrenia;
repeating the steps until third data after drug clustering of all the schizophrenic patients are obtained;
and the plurality of third data are statistically calculated as a third data model.
In one embodiment, the obtaining optimal medication data for a patient suffering from schizophrenia according to the data model comprises:
Acquiring demographic parameters and clinical parameters of a patient suffering from schizophrenia;
acquiring different medication condition data of the patient with the schizophrenia through the first data model according to the demographic parameters and the clinical parameters of the patient with the schizophrenia;
according to the different medication condition data of the schizophrenic patient, acquiring the clinical disease data of the schizophrenic patient through the second data model;
and acquiring medication data of the schizophrenic patient according to the clinical data of the schizophrenic patient through the third data model.
In one embodiment, the training a data model of the efficacy of the drug based on the sample data comprises:
and performing feature extraction on the input data by using a plurality of neural networks in a plurality of machine learning modules, and training a data model of the drug efficacy.
In accordance with another aspect of embodiments of the present invention, there is disclosed a medication intake analysis device based on schizophrenia, the device comprising:
the acquisition module is used for acquiring sample data of a plurality of medication effects;
the modeling module is used for training a data model of the curative effect of the medicine according to the sample data;
And the generating module is used for generating optimal medication data required by the patient according to the data model.
In accordance with yet another aspect of an embodiment of the present application, an electronic device is disclosed that includes one or more processors and memory for storing one or more programs; when the one or more programs are executed by the processor, the processor is caused to implement the method for detecting the charging of the battery cell provided by the embodiments of the present application.
In accordance with yet another aspect of embodiments of the present application, a computer-readable storage medium storing a computer program that, when executed, implements a method for detecting battery charging provided by embodiments of the present application is disclosed.
Compared with the prior art, the application has the following advantages:
in an embodiment of the present application, by acquiring sample data of the effects of medication of a plurality of schizophrenia; training a data model of medication of the schizophrenia according to the sample data; according to the data model, optimal medication data required by the schizophrenic patient are acquired, the optimal medication data can be automatically acquired according to the clinical condition of the schizophrenic patient, the problem that the medication of the schizophrenic patient is not scientific in the prior art is effectively solved, and the medication of the schizophrenic patient can be optimized so as to improve the treatment effect of the schizophrenic patient.
The technical scheme of the application is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description, serve to explain the principles of the application.
The application may be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings, in which:
FIG. 1 is an exemplary flow chart of a method of schizophrenia-based medication intake analysis provided in one embodiment of the application;
FIG. 2 is another exemplary flow chart of a method of schizophrenia-based medication intake analysis according to one embodiment of the application;
FIG. 3 is yet another exemplary flow chart of a method of schizophrenia-based medication intake analysis according to one embodiment of the application;
FIG. 4 is a schematic diagram of a medication intake analysis device based on schizophrenia according to an embodiment of the present application;
fig. 5 is an internal structural diagram of an electronic device in one embodiment.
Detailed Description
The application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be noted that, for convenience of description, only the portions related to the application are shown in the drawings.
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
Unless defined otherwise, technical or scientific terms used in this disclosure should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. The terms "first," "second," and the like, as used in this disclosure, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. Likewise, the word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical or signal connections, whether direct or indirect.
In the embodiments described below, the communication connection includes connection through a wireless network, a wired network, and/or any combination of wireless and wired networks. The network may include a local area network, the internet, a telecommunications network, an internet of things based on the internet and/or telecommunications network, any combination of the above, and/or the like. The wired network may transmit information by using a metal wire, a twisted pair, a coaxial cable, or an optical fiber transmission, and the wireless network may use a communication mode such as a WWAN mobile communication network, bluetooth, zigbee, or Wi-Fi.
In the embodiments described below, the processor is a device having a data processing capability and/or a program execution capability, such as a Central Processing Unit (CPU), a field programmable logic array (FPGA), a Digital Signal Processor (DSP), a single chip Microcomputer (MCU), an application specific logic circuit (ASIC), an image processor (GPU), or the like, which performs logic operations. It will be readily appreciated that the processor is typically communicatively coupled to a memory, on which is stored any combination of one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. Volatile memory can include, for example, random Access Memory (RAM) and/or cache memory (cache) and the like. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, erasable programmable read-only memory (EPROM), USB memory, flash memory, and the like. One or more computer instructions may be stored on the memory and executed by the processor to perform the relevant analysis functions. Various applications and various data, such as various data used and/or generated by the applications, may also be stored in the computer readable storage medium.
In the embodiments described below, each module may be implemented by a processor executing related computer instructions, for example, the image transformation module may be implemented by a processor executing instructions of an image transformation algorithm, the machine learning module may be implemented by a processor executing instructions of a machine learning algorithm, and the neural network may be implemented by a processor executing instructions of a neural network algorithm.
In the embodiments described below, the modules may run on the same processor or may run on multiple processors; the modules may be run on processors of the same architecture, e.g., all on processors of the X86 architecture, or on processors of different architectures, e.g., the image conversion module runs on the CPU of the X86 architecture, and the first and second machine learning modules run on the GPU. The modules may be packaged in a computer product, for example, the modules are packaged in a computer software and run in a computer (server), or may be packaged separately or partially in different computer products, for example, the image conversion module is packaged in a computer software and run in a computer (server), and the first and second machine learning modules are packaged separately in separate computer software and run in another computer or computers (servers); the computing platform when each module executes may be local computing, cloud computing, or hybrid computing composed of local computing and cloud computing.
In the embodiments described below, the machine learning (e.g., neural network) may be implemented by using an existing machine learning (e.g., neural network) framework in which parameters are preset, or by using an existing machine learning (e.g., neural network) framework to train through the relevant image samples according to the embodiments of the present application to obtain the machine learning (e.g., neural network) framework with the required parameters, or by building the machine learning (e.g., neural network) framework and training according to the spirit of the embodiments of the present application.
Referring to fig. 1, an exemplary flow of a medication intake analysis method based on schizophrenia to which the embodiment of the present application can be applied is shown.
As shown in fig. 1, the medication analysis method based on schizophrenia includes:
step 101, obtaining sample data of medication effects of a plurality of schizophrenia.
Specifically, the sample data of the medication effect of the schizophrenia may include clinical characteristics of the schizophrenia patient, such as electrocardiogram parameter indexes, electroencephalogram parameter indexes, blood analysis parameter indexes, behavior analysis parameter indexes, bioelectrical impedance characteristic indexes, etc.; the medicine also comprises medication parameters of the patients with the schizophrenia, such as the medication name, the dosage, the medication combination mode, the medication time length and the like for treating the schizophrenia; also comprises the treatment effect characteristics of the schizophrenic patients, such as the electrocardiogram parameter change condition, the electroencephalogram parameter change condition, the blood analysis parameter change condition, the behavior analysis parameter change condition, the bioelectrical impedance characteristic change condition and the like. By taking sample data of the medication effect of a plurality of schizophrenic patients, a treatment effect model of the schizophrenic patients with different medications under different clinical characteristics is established, wherein the sample data comprises individual demographics, clinical data, cognitive function data, imaging data, blood indexes and other related data of the schizophrenic patients.
Step 102, training a data model of medication of the schizophrenia according to the sample data.
Specifically, the purpose of establishing a data model of medication of the schizophrenia is to establish a treatment effect model of the schizophrenia patient with different medications under different clinical characteristics by analyzing clinical characteristic parameters of the schizophrenia patient, so that the most effective medication information can be timely obtained after the clinical characteristics of the schizophrenia patient are obtained.
Specifically, in one specific embodiment, as shown in fig. 2, the training the data model for medication of schizophrenia according to the sample data includes:
in step 201, statistics are performed on medication data of patients with schizophrenia, and a first data model of a plurality of different medication situations of a plurality of patients with schizophrenia is obtained.
Specifically, in an embodiment of the present application, the first data model is a data model of a drug that establishes treatment of schizophrenia, such as: information such as the dosage and the dosage time of the certain medicine A for treating the schizophrenia and treatment effect parameters for treating the mild-moderate severe schizophrenia. And establishing a data model of the medicine A according to the parameters.
Specifically, in one specific embodiment of the present application, the statistics of medication data of schizophrenic patients are performed to obtain a first data model of a plurality of different medication situations of a plurality of schizophrenic patients, including:
a first machine learning module is selected from the group consisting of at least one first machine learning module, and feature extraction is performed on clinical data and medication data of the schizophrenic patient.
In the embodiment of the application, the first machine learning module can be implemented by adopting any available feature extraction algorithm in the machine learning field, for example, principal component analysis, fisher linear identification, projection tracking, neural network, genetic algorithm and the like. The features that need to be extracted may be various features contained in clinical data and medication data, including, for example, but not limited to, medication name, medication amount, length of time of medication, any combination of these features, and the like.
In the above embodiment, the appropriate first machine learning module is selected according to the requirements to perform the feature extraction of the medication data, so that the clinical data of the schizophrenic patient and the useless features and ineffective data in the medication data can be effectively removed, the mutual interference during the feature extraction during the modeling of the second data models is prevented, the essential features of the first data models in each second data model are reserved, and the accuracy and the adaptability of the data processing process are improved.
First data of different medication situations of a patient with schizophrenia are obtained.
Repeating the steps until a plurality of first data of different medication conditions corresponding to the clinical data of the patients with the schizophrenia are obtained.
The plurality of first data is aggregated into a first data model.
The first machine learning module includes a first neural network.
In some embodiments of the present application, each first neural network is the same in the group including at least one first machine learning module, and may perform related training in advance according to a training process of a general neural network to obtain different parameters, and select a first neural network with the best performance as a feature extraction for use.
In some embodiments of the present application, in the group including at least one first machine learning module, at least part of the first neural networks of the first machine learning modules are different, for example, the first neural networks have the same architecture but different parameters, for example, the first neural networks have different architectures, and the first neural networks with the best performance can be selected as the feature extraction for use by performing related training according to a training process of a general neural network in advance. When the method is used, each second image is used for extracting the characteristics, and one or more first neural networks with the best performance are randomly selected from the first neural networks for extracting the characteristics.
In some embodiments of the application, in the group comprising at least one first machine learning module, the first neural network of at least part of the second data is a feature extraction by a person skilled in the art for at least a suitable part of the first data selected from the neural networks already in the art for the type of the first data converted, these first neural networks being in one-to-one correspondence with the feature extraction of the first data aimed at; or, optionally, training the first neural networks with different architectures, and selecting a part of the first neural networks formed by training to perform feature extraction in one-to-one correspondence with at least a part of the first data; or, alternatively, training a plurality of first neural networks with the same architecture, and selecting part of first neural networks with different parameters formed by training to perform feature extraction in one-to-one correspondence with at least part of first data.
The first neural network may adopt various common neural network architectures such as a convolutional neural network CNN, a deep neural network DNN, a cyclic neural network RNN, a self-coding neural network AENN, a sparse self-coding machine SAE, or a neural network product implemented based on the common neural network architecture, for example AlexNet, VGGNet, google Inception Net, res net, etc., and may also design a neural network structure according to the principle of the neural network.
Step 202, obtaining a second data model after classifying different clinical characteristics of the schizophrenic patient according to the first data model.
In particular, the second data model is a further refinement of the first data model, and due to different clinical characteristics of the schizophrenic patients, for example, by electroencephalogram detection, some patient-related parameters are high and others are low; by blood detection, some patient-related indexes are high, and some patient-related indexes are low, so that by corresponding clinical characteristics of patients to different therapeutic drugs for schizophrenia, a second data model of the medication condition of the schizophrenic patients under different clinical characteristics is obtained.
Specifically, in one embodiment of the present application, the obtaining, according to the first data model, a second data model after classifying different clinical characteristics of a patient with schizophrenia includes:
selecting a second machine learning module from the group consisting of at least one second machine learning module, performing a clinical feature extraction on the first data model of the schizophrenic patient;
acquiring second data of a schizophrenic patient after different clinical feature classifications;
Repeating the steps until a plurality of second data of all the schizophrenic patients after different clinical feature classifications are obtained;
the plurality of second data is aggregated into a second data model.
Specifically, in the embodiment of the present application, the second machine learning module may be any algorithm that performs the second data processing, such as a support vector machine, simple fusion, collaborative training fusion, neural network, and the like.
In some embodiments of the present application, the second machine learning module includes a second neural network, where the second neural network may employ various common neural network architectures such as a convolutional neural network CNN, a deep neural network DNN, a cyclic neural network RNN, a self-coding neural network AENN, a sparse self-coder SAE, or employ neural network products implemented based on the common neural network architectures, for example, alexNet, VGGNet, google Inception Net, res net, etc., and may also design a neural network structure according to the principle of the neural network.
Those skilled in the art will readily appreciate that the second neural network has its corresponding structure, e.g. when the second data processing is to be classified, a classifier is provided at the output layer of the second neural network, as appropriate for the purpose of the image processing.
Specifically, in some embodiments of the present application, the second machine learning module may be the same as or different from a certain first machine learning module of the group including at least one first machine learning module, and the specific network structure may be pre-constructed and trained according to specific requirements.
For example, taking the second neural network as the second machine learning module, the second neural network structure may be designed as a convolutional layer-full-connection layer-LR device, and classified by the first data, or may be designed as a convolutional layer-pooling layer-full-connection layer-SVM classifier to classify the first data, and may be specifically designed according to the requirement of the first data processing purpose.
And 203, acquiring a third data model after clustering the medication condition of the schizophrenic patient according to the second data model.
Specifically, the third data model is further refined on the second data model, different treatment effects can be obtained by different medication conditions of the schizophrenic patient under different clinical characteristics, and accurate medication of the schizophrenic patient is realized by reintroducing data of the treatment effects into the clinical characteristics and medication conditions of the schizophrenic patient.
Specifically, in one embodiment of the present application, the obtaining, according to the second data model, a third data model after clustering medication situations of a patient with schizophrenia includes:
selecting a third machine learning module from a group comprising at least one third machine learning module, and performing medication feature extraction on a second data model of the schizophrenic patient;
acquiring third data after clustering medication features of a patient suffering from schizophrenia;
repeating the steps until third data after drug clustering of all the schizophrenic patients are obtained;
and the plurality of third data are statistically calculated as a third data model.
Specifically, in some embodiments of the present application, the third machine learning module may be the same as a certain first machine learning module including a set of at least one first machine learning module, or a certain second machine learning module including a set of at least one second machine learning module, or may be different, and the specific network structure may be pre-constructed and trained according to specific requirements to obtain a cluster that may be designed as a convolution layer-pooling layer-K-means, so as to cluster the second data, and may also be designed as a convolution layer-full-connection layer-deconvolution layer, or the like, and may be specifically designed according to the data processing purpose.
Specifically, in one embodiment of the present application, as shown in fig. 3, the obtaining, according to the data model, optimal medication data required by a patient suffering from schizophrenia includes:
step 301, obtaining demographic parameters and clinical parameters of a patient with schizophrenia;
step 302, obtaining different medication condition data of the patient with schizophrenia through the first data model according to the demographic parameters and clinical parameters of the patient with schizophrenia;
step 303, obtaining clinical data of the diseases of the schizophrenic patient through the second data model according to the different medication condition data of the schizophrenic patient;
step 304, according to the clinical data of the schizophrenic patient, acquiring the medication data of the schizophrenic patient through the third data model.
Specifically, the training a data model of the therapeutic effect of the drug according to the sample data comprises:
and performing feature extraction on the input data by using a plurality of neural networks in a plurality of machine learning modules, and training a data model of the drug efficacy.
In the above-described embodiment, by acquiring sample data of the medication effect of a plurality of schizophrenia; training a data model of medication of the schizophrenia according to the sample data; according to the data model, optimal medication data required by the schizophrenic patient are acquired, the optimal medication data can be automatically acquired according to the clinical condition of the schizophrenic patient, the problem that the medication of the schizophrenic patient is not scientific in the prior art is effectively solved, and the medication of the schizophrenic patient can be optimized so as to improve the treatment effect of the schizophrenic patient.
It should be understood that, although the steps in the flowcharts of fig. 1-3 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 1-3 may include multiple sub-steps or phases that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the sub-steps or phases are performed necessarily occur sequentially, but may be performed alternately or alternately with at least a portion of the sub-steps or phases of other steps or other steps.
In one embodiment, as shown in fig. 4, there is provided a medication intake analysis device based on schizophrenia, comprising: the system comprises an acquisition module, a modeling module and a generation module.
The acquisition module is used for acquiring sample data of a plurality of medication effects;
the modeling module is used for training a data model of the curative effect of the medicine according to the sample data;
and the generating module is used for generating optimal medication data required by the patient according to the data model.
Specifically, in another embodiment of the present application, the modeling module is further configured to count medication data of patients with schizophrenia, and obtain a first data model of a plurality of different medication situations of a plurality of patients with schizophrenia; acquiring a second data model after classifying different clinical characteristics of the schizophrenic patient according to the first data model; and acquiring a third data model after clustering the medication condition of the schizophrenic patient according to the second data model.
The modeling module is further used for selecting a first machine learning module from a group comprising at least one first machine learning module and extracting characteristics of clinical data and medication data of the schizophrenic patient;
acquiring first data of different medication situations of a patient suffering from schizophrenia; repeating the steps until a plurality of first data of different medication conditions corresponding to the clinical data of the schizophrenic patient are obtained; the plurality of first data is aggregated into a first data model.
The modeling module is further configured to select a second machine learning module from a group consisting of at least one second machine learning module, and perform clinical feature extraction on the first data model of the schizophrenic patient; acquiring second data of a schizophrenic patient after different clinical feature classifications; repeating the steps until a plurality of second data of all the schizophrenic patients after different clinical feature classifications are obtained; the plurality of second data is aggregated into a second data model.
The modeling module is further configured to select a third machine learning module from a group consisting of at least one third machine learning module, and perform medication feature extraction on the second data model of the schizophrenic patient; acquiring third data after clustering medication features of a patient suffering from schizophrenia; repeating the steps until third data after drug clustering of all the schizophrenic patients are obtained; and the plurality of third data are statistically calculated as a third data model.
The generation module is also used for acquiring demographic parameters and clinical parameters of the schizophrenic patient; acquiring different medication condition data of the patient with the schizophrenia through the first data model according to the demographic parameters and the clinical parameters of the patient with the schizophrenia; according to the different medication condition data of the schizophrenic patient, acquiring the clinical disease data of the schizophrenic patient through the second data model; and acquiring medication data of the schizophrenic patient according to the clinical data of the schizophrenic patient through the third data model.
The modeling module is also used for performing feature extraction on the input data by using a plurality of neural networks in the machine learning modules and training a data model of the drug efficacy.
In the above embodiment, the obtaining module obtains the sample data of the medication effect of the plurality of schizophrenia; the modeling module trains a data model of medication of the schizophrenia according to the sample data; the generation module acquires optimal medication data required by the schizophrenic patient according to the data model, can automatically acquire the optimal medication data according to the clinical condition of the schizophrenic patient, effectively solves the problem of unscientific medication of the schizophrenic patient in the prior art, and can optimize the medication of the schizophrenic patient so as to improve the treatment effect of the schizophrenic patient.
In one embodiment, an electronic device is provided, the internal structure of which may be as shown in FIG. 5. The electronic device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the electronic device is used for conducting wired or wireless communication with an external terminal, and the wireless communication can be achieved through WIFI, an operator network, near Field Communication (NFC) or other technologies. The computer program is executed by a processor to implement a medication intake analysis method based on schizophrenia. The display screen of the electronic equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the electronic equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and is not limiting of the electronic device to which the present inventive arrangements are applied, and that a particular electronic device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, the schizophrenia-based medication intake analysis apparatus provided by the present application may be implemented in the form of a computer program that is executable on an electronic device as shown in fig. 5. The memory of the electronic device may store therein various program modules constituting the medication intake analysis device based on schizophrenia, such as the acquisition module, the modeling module, and the generation module shown in fig. 4. The computer program constituted by the respective program modules causes the processor to execute the steps in the image-based motion detection method of the respective embodiments of the present application described in the present specification.
In one embodiment, the processor, when executing the computer program, performs the steps of: acquiring sample data of medication effects of a plurality of schizophrenia; training a data model of medication of the schizophrenia according to the sample data; and obtaining optimal medication data required by the schizophrenic patient according to the data model.
In one embodiment, the processor when executing the computer program further performs the steps of: counting medication data of patients with schizophrenia, and obtaining a first data model of a plurality of different medication conditions of a plurality of patients with schizophrenia; acquiring a second data model after classifying different clinical characteristics of the schizophrenic patient according to the first data model; and acquiring a third data model after clustering the medication condition of the schizophrenic patient according to the second data model.
In one embodiment, the processor when executing the computer program further performs the steps of: selecting a first machine learning module from a group comprising at least one first machine learning module, performing feature extraction on clinical data and medication data of the schizophrenic patient; acquiring first data of different medication situations of a patient suffering from schizophrenia; repeating the steps until a plurality of first data of different medication conditions corresponding to the clinical data of the schizophrenic patient are obtained; the plurality of first data is aggregated into a first data model.
In one embodiment, the processor when executing the computer program further performs the steps of: selecting a second machine learning module from the group consisting of at least one second machine learning module, performing a clinical feature extraction on the first data model of the schizophrenic patient; acquiring second data of a schizophrenic patient after different clinical feature classifications; repeating the steps until a plurality of second data of all the schizophrenic patients after different clinical feature classifications are obtained; the plurality of second data is aggregated into a second data model.
In one embodiment, the processor when executing the computer program further performs the steps of: selecting a third machine learning module from a group comprising at least one third machine learning module, and performing medication feature extraction on a second data model of the schizophrenic patient; acquiring third data after clustering medication features of a patient suffering from schizophrenia; repeating the steps until third data after drug clustering of all the schizophrenic patients are obtained; and the plurality of third data are statistically calculated as a third data model.
In one embodiment, the processor when executing the computer program further performs the steps of: acquiring demographic parameters and clinical parameters of a patient suffering from schizophrenia; acquiring different medication condition data of the patient with the schizophrenia through the first data model according to the demographic parameters and the clinical parameters of the patient with the schizophrenia; according to the different medication condition data of the schizophrenic patient, acquiring the clinical disease data of the schizophrenic patient through the second data model; and acquiring medication data of the schizophrenic patient according to the clinical data of the schizophrenic patient through the third data model.
In one embodiment, the processor when executing the computer program further performs the steps of: and performing feature extraction on the input data by using a plurality of neural networks in a plurality of machine learning modules, and training a data model of the drug efficacy.
When the processor executes the computer program, the acquisition module acquires sample data of the medication effect of a plurality of schizophrenia; the modeling module trains a data model of medication of the schizophrenia according to the sample data; the generation module acquires optimal medication data required by the schizophrenic patient according to the data model, can automatically acquire the optimal medication data according to the clinical condition of the schizophrenic patient, effectively solves the problem of unscientific medication of the schizophrenic patient in the prior art, and can optimize the medication of the schizophrenic patient so as to improve the treatment effect of the schizophrenic patient.
In one embodiment, a computer readable storage medium is provided, a computer program can be stored in a non-volatile computer readable storage medium, the computer program realizing the following steps when executed by a processor: acquiring sample data of medication effects of a plurality of schizophrenia; training a data model of medication of the schizophrenia according to the sample data; and obtaining optimal medication data required by the schizophrenic patient according to the data model.
In one embodiment, the computer program when executed by the processor further performs the steps of: counting medication data of patients with schizophrenia, and obtaining a first data model of a plurality of different medication conditions of a plurality of patients with schizophrenia; acquiring a second data model after classifying different clinical characteristics of the schizophrenic patient according to the first data model; and acquiring a third data model after clustering the medication condition of the schizophrenic patient according to the second data model.
In one embodiment, the computer program when executed by the processor further performs the steps of: selecting a first machine learning module from a group comprising at least one first machine learning module, performing feature extraction on clinical data and medication data of the schizophrenic patient; acquiring first data of different medication situations of a patient suffering from schizophrenia; repeating the steps until a plurality of first data of different medication conditions corresponding to the clinical data of the schizophrenic patient are obtained; the plurality of first data is aggregated into a first data model.
In one embodiment, the computer program when executed by the processor further performs the steps of: selecting a second machine learning module from the group consisting of at least one second machine learning module, performing a clinical feature extraction on the first data model of the schizophrenic patient; acquiring second data of a schizophrenic patient after different clinical feature classifications; repeating the steps until a plurality of second data of all the schizophrenic patients after different clinical feature classifications are obtained; the plurality of second data is aggregated into a second data model.
In one embodiment, the computer program when executed by the processor further performs the steps of: selecting a third machine learning module from a group comprising at least one third machine learning module, and performing medication feature extraction on a second data model of the schizophrenic patient; acquiring third data after clustering medication features of a patient suffering from schizophrenia; repeating the steps until third data after drug clustering of all the schizophrenic patients are obtained; and the plurality of third data are statistically calculated as a third data model.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring demographic parameters and clinical parameters of a patient suffering from schizophrenia; acquiring different medication condition data of the patient with the schizophrenia through the first data model according to the demographic parameters and the clinical parameters of the patient with the schizophrenia; according to the different medication condition data of the schizophrenic patient, acquiring the clinical disease data of the schizophrenic patient through the second data model; and acquiring medication data of the schizophrenic patient according to the clinical data of the schizophrenic patient through the third data model.
In one embodiment, the computer program when executed by the processor further performs the steps of: and performing feature extraction on the input data by using a plurality of neural networks in a plurality of machine learning modules, and training a data model of the drug efficacy.
The computer program is executed by the processor, and sample data of the medication effect of a plurality of schizophrenia are obtained through the obtaining module; the modeling module trains a data model of medication of the schizophrenia according to the sample data; the generation module acquires optimal medication data required by the schizophrenic patient according to the data model, can automatically acquire the optimal medication data according to the clinical condition of the schizophrenic patient, effectively solves the problem of unscientific medication of the schizophrenic patient in the prior art, and can optimize the medication of the schizophrenic patient so as to improve the treatment effect of the schizophrenic patient.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static random access memory (Static Random Access Memory, SRAM), dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (6)
1. A method of medication analysis based on schizophrenia, the method comprising:
acquiring sample data of medication effects of a plurality of schizophrenia;
training a data model of medication of the schizophrenia according to the sample data;
obtaining optimal medication data required by a patient with schizophrenia according to a data model;
Training a data model for medication therapy for schizophrenia based on the sample data includes:
counting medication data of patients with schizophrenia, and obtaining a first data model of a plurality of different medication conditions of a plurality of patients with schizophrenia;
acquiring a second data model after classifying different clinical characteristics of the schizophrenic patient according to the first data model;
acquiring a third data model after clustering the medication condition of the schizophrenic patient according to the second data model;
statistics are carried out on medication data of patients with schizophrenia, and a first data model of a plurality of different medication conditions of a plurality of patients with schizophrenia is obtained, wherein the first data model comprises the following components:
selecting a first machine learning module from a group comprising at least one first machine learning module, and performing feature extraction on clinical data and medication data of a patient suffering from schizophrenia;
acquiring first data of different medication situations of a patient suffering from schizophrenia;
repeating the steps until a plurality of first data of different medication conditions corresponding to clinical data of all schizophrenic patients are obtained;
summarizing a plurality of first data into a first data model;
Obtaining a second data model classified by different clinical characteristics of the schizophrenic patient according to the first data model, wherein the second data model comprises the following components:
selecting a second machine learning module from the group consisting of at least one second machine learning module, performing clinical feature extraction on the first data model of the schizophrenic patient;
acquiring second data of a schizophrenic patient after different clinical feature classifications;
repeating the steps until a plurality of second data of all schizophrenic patients after different clinical characteristics are classified are obtained;
unifying the plurality of second data into a second data model;
obtaining a third data model after clustering the medication condition of the schizophrenic patient according to the second data model, wherein the third data model comprises the following components:
selecting a third machine learning module from the group consisting of at least one third machine learning module, and performing medication feature extraction on a second data model of the schizophrenic patient;
acquiring third data after clustering medication features of a patient suffering from schizophrenia;
repeating the steps until third data after drug clustering of all schizophrenic patients are obtained;
and the plurality of third data are statistically calculated as a third data model.
2. The method of claim 1, wherein obtaining optimal medication data for a schizophrenic patient based on the data model comprises:
acquiring demographic parameters and clinical parameters of a patient suffering from schizophrenia;
acquiring different medication condition data of the patient with the schizophrenia through the first data model according to the demographic parameters and the clinical parameters of the patient with the schizophrenia;
according to different medication condition data of the schizophrenic patient, acquiring clinical disease data of the schizophrenic patient through the second data model;
and acquiring medication data of the schizophrenic patient according to the clinical data of the schizophrenic patient through the third data model.
3. The method of claim 1, wherein training a data model of drug efficacy based on the sample data comprises:
and performing feature extraction on the input data by using a plurality of neural networks in a plurality of machine learning modules, and training a data model of the drug efficacy.
4. A medication intake analysis device based on schizophrenia, characterized by comprising:
the acquisition module is used for acquiring sample data of the medication effect of the multiple schizophrenia;
The modeling module is used for training a data model of the curative effect of the medicine according to the sample data;
the generation module is used for generating optimal medication data required by the patient with the schizophrenia according to the data model;
training a data model of a drug efficacy based on the sample data comprises:
counting medication data of patients with schizophrenia, and obtaining a first data model of a plurality of different medication conditions of a plurality of patients with schizophrenia;
acquiring a second data model after classifying different clinical characteristics of the schizophrenic patient according to the first data model;
acquiring a third data model after clustering the medication condition of the schizophrenic patient according to the second data model;
statistics are carried out on medication data of patients with schizophrenia, and a first data model of a plurality of different medication conditions of a plurality of patients with schizophrenia is obtained, wherein the first data model comprises the following components:
selecting a first machine learning module from a group comprising at least one first machine learning module, and performing feature extraction on clinical data and medication data of a patient suffering from schizophrenia;
acquiring first data of different medication situations of a patient suffering from schizophrenia;
repeating the steps until a plurality of first data of different medication conditions corresponding to clinical data of all schizophrenic patients are obtained;
Summarizing a plurality of first data into a first data model;
obtaining a second data model classified by different clinical characteristics of the schizophrenic patient according to the first data model, wherein the second data model comprises the following components:
selecting a second machine learning module from the group consisting of at least one second machine learning module, performing clinical feature extraction on the first data model of the schizophrenic patient;
acquiring second data of a schizophrenic patient after different clinical feature classifications;
repeating the steps until a plurality of second data of all schizophrenic patients after different clinical characteristics are classified are obtained;
unifying the plurality of second data into a second data model;
obtaining a third data model after clustering the medication condition of the schizophrenic patient according to the second data model, wherein the third data model comprises the following components:
selecting a third machine learning module from the group consisting of at least one third machine learning module, and performing medication feature extraction on a second data model of the schizophrenic patient;
acquiring third data after clustering medication features of a patient suffering from schizophrenia;
repeating the steps until third data after drug clustering of all schizophrenic patients are obtained;
And the plurality of third data are statistically calculated as a third data model.
5. A computer device, comprising: one or more processors;
the one or more processors being configured to perform the method of any of claims 1 to 3 when executing computer instructions.
6. A computer readable storage medium storing computer instructions for execution by a processor, the computer instructions when executed by the processor being operable to perform the method of any one of claims 1 to 3.
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