CN113707255B

CN113707255B - Health guidance method, device, computer equipment and medium based on similar patients

Info

Publication number: CN113707255B
Application number: CN202111013016.2A
Authority: CN
Inventors: 赵婷婷; 孙行智; 徐卓扬; 刘卓
Original assignee: Ping An Technology Shenzhen Co Ltd
Current assignee: Ping An Technology Shenzhen Co Ltd
Priority date: 2021-08-31
Filing date: 2021-08-31
Publication date: 2023-09-26
Anticipated expiration: 2041-08-31
Also published as: CN113707255A

Abstract

The embodiment of the application belongs to the field of artificial intelligence, and is applied to the field of intelligent medical treatment, and relates to a health guidance method based on similar patients. The application also provides a health guiding device, computer equipment and a storage medium based on similar patients. Furthermore, the present application relates to blockchain technology in which the first patient characteristic may be stored. The application can clearly show the management target of the patient, so that the patient can intervene as early as possible, and the self-management capability is well cultivated.

Description

Health guidance method, device, computer equipment and medium based on similar patients

Technical Field

The application relates to the technical field of artificial intelligence and the technical field of digital medical treatment, in particular to a health guidance method, a device, computer equipment and a medium based on similar patients.

Background

The chronic diseases are also called chronic non-infectious diseases, mainly comprise cardiovascular and cerebrovascular diseases (hypertension, coronary heart disease, cerebral apoplexy), diabetes mellitus, chronic respiratory diseases and the like, are diseases closely related to bad behaviors and life style, and have the characteristics of long disease course, complex etiology, health damage, serious social hazard and the like. Along with the rapid development of the economy and the change of the life style of residents in China, the disease and death rate of chronic diseases are continuously increased, and the disease burden of the masses is increasingly heavy, so that the disease is one of the major public health problems which seriously threaten the health of residents in China and affect the development of the economy and society in China. Moreover, chronic diseases are difficult to cure radically, and rely mainly on long-term self-health management of patients.

Taking diabetes as an example, diabetes is a common chronic disease that currently jeopardizes the health and life of people. Diabetes has become the third greatest chronic disease threatening human health following cardiovascular and cerebrovascular diseases, malignant tumors. With the general improvement of the living standard of people in China and the acceleration of the living rhythm, the number of diabetics is increasing at a striking speed, and the progress is towards the reduction of age. Classical prevention and treatment strategies for diabetes are management modes taking diet therapy, exercise, reasonable medication, self-monitoring and diabetes teaching as main contents, and the aims of preventing chronic complications, improving the life quality of patients and prolonging the service life are achieved through good blood sugar and metabolism control.

However, the difficulty of self-management of patients is very great, and there are problems of lack of knowledge about diabetes risk, low importance, poor compliance and the like, so that a diabetes health management system capable of helping patients to improve self-management ability is highly demanded. The existing diabetes slow disease management system mostly focuses on blood sugar monitoring, such as early warning of abnormal blood sugar values or providing of blood sugar fluctuation reports in a certain period of time before, and similar forms can only summarize the state of a patient in a past period of time, can not clearly prompt and guide the subsequent behaviors of the patient, weakens the culture of the self-management capability of the patient, and is not beneficial to the prevention and control of illness state.

Disclosure of Invention

The embodiment of the application aims to provide a health guiding method, a device, computer equipment and a storage medium based on similar patients, so as to solve the technical problem that the patients in the related art cannot realize self health management pertinently because of not knowing the health condition and management targets, thereby preventing and controlling chronic diseases.

In order to solve the above technical problems, the embodiment of the present application provides a health guidance method based on similar patients, which adopts the following technical scheme:

Acquiring a first patient characteristic of a patient to be detected, and inputting the first patient characteristic into a preset saccharification prediction model to obtain a prediction result;

determining a crowd opposite to the prediction result as a target crowd, and acquiring target patient characteristics of each target patient in the target crowd;

searching according to the first patient characteristics and the target patient characteristics to obtain a similar patient similar to the patient to be detected;

and carrying out health guidance on the patient to be tested based on the similar patient.

Further, the step of retrieving, according to the first patient feature and the target patient feature, a similar patient similar to the patient to be tested includes:

according to the first patient characteristics and the target patient characteristics, calculating the similarity between the patient to be tested and each target patient;

sequencing the similarity to obtain a sequencing result;

and selecting a preset number of target patients from the sorting result as the similar patients.

Further, the step of conducting health guidance on the patient to be tested based on the similar patient includes:

obtaining similar patient characteristics of the similar patient, and comparing differences between the similar patient characteristics and the first patient characteristics to obtain distinguishing characteristics;

Generating a guiding suggestion according to the distinguishing characteristics, and guiding the patient to be tested according to the guiding suggestion.

Further, before the step of acquiring the first patient characteristic of the patient to be tested and inputting the first patient characteristic into the preset glycation prediction model, the method further comprises:

collecting a disease data set, and obtaining a second patient characteristic and saccharification standard condition corresponding to each patient according to the disease data set;

marking the characteristics of the second patient by taking the saccharification meeting the standard as a label to obtain disease characteristic data;

training the pre-constructed initial prediction model according to the disease characteristic data to obtain the saccharification prediction model.

Further, the step of training the pre-constructed initial prediction model according to the disease characteristic data to obtain the saccharification prediction model includes:

obtaining training data and verification data according to the disease characteristic data;

adjusting model parameters of the initial prediction model based on the training data to obtain a model to be verified;

and inputting the verification data into the model to be verified for verification to obtain a verification result, and determining the model to be verified as the saccharification prediction model when the verification result is greater than or equal to a preset threshold value.

Further, the step of adjusting model parameters of the initial predictive model based on the training data includes:

inputting the training data into the initial prediction model, and outputting a predicted saccharification result;

and calculating a loss function according to the predicted saccharification result, and adjusting model parameters of the initial prediction model based on the loss function.

Further, before the step of labeling the second patient characteristic with the saccharification meeting standard as a label, the method further includes:

and normalizing the second patient characteristic.

In order to solve the technical problems, the embodiment of the application also provides a health guidance device based on similar patients, which adopts the following technical scheme:

the prediction module is used for acquiring first patient characteristics of a patient to be detected, inputting the first patient characteristics into a preset saccharification prediction model and obtaining a prediction result;

the acquisition module is used for determining a crowd opposite to the prediction result as a target crowd and acquiring target patient characteristics of each target patient in the target crowd;

the retrieval module is used for retrieving according to the first patient characteristics and the target patient characteristics to obtain a similar patient similar to the patient to be detected;

And the guiding module is used for guiding the health of the patient to be tested based on the similar patient.

In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical schemes:

the computer device includes a memory having stored therein computer readable instructions which when executed by the processor implement the steps of the similar patient based health instruction method described above.

In order to solve the above technical problems, an embodiment of the present application further provides a computer readable storage medium, which adopts the following technical schemes:

the computer readable storage medium has stored thereon computer readable instructions which when executed by a processor implement the steps of the similar patient based health instruction method as described above.

Compared with the prior art, the embodiment of the application has the following main beneficial effects:

according to the application, the first patient characteristics of the patient to be detected are acquired, the first patient characteristics are input into a preset saccharification prediction model, a prediction result is obtained, a crowd opposite to the prediction result is determined as a target crowd, target patient characteristics of each target patient in the target crowd are acquired, retrieval is carried out according to the first patient characteristics and the target patient characteristics, a similar patient similar to the patient to be detected is obtained, and health guidance is carried out on the patient to be detected based on the similar patient; according to the application, the patient is predicted by the saccharification prediction model, so that the patient can predict the saccharification standard condition of the patient in the next stage according to the current state, the patient can know the health condition of the patient, meanwhile, the health guidance is provided for the patient to be tested according to the condition of the similar patient with opposite prediction results, the health help can be provided for the patient more accurately, meanwhile, the health guidance can clearly show the patient management target, the patient can intervene as early as possible, and the good self-management capability is cultivated.

Drawings

In order to more clearly illustrate the solution of the present application, a brief description will be given below of the drawings required for the description of the embodiments of the present application, it being apparent that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without the exercise of inventive effort for a person of ordinary skill in the art.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

FIG. 2 is a flow chart of one embodiment of a similar patient based health guidance method according to the present application;

FIG. 3 is a flow chart of another embodiment of a similar patient based health guidance method according to the present application;

FIG. 4 is a schematic structural view of one embodiment of a similar patient-based health instruction device according to the present application;

FIG. 5 is a schematic structural diagram of one embodiment of a computer device in accordance with the present application.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

In order to make the person skilled in the art better understand the solution of the present application, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to the accompanying drawings.

The application provides a health guidance method based on similar patients, which can be applied to a system architecture 100 shown in fig. 1, wherein the system architecture 100 can comprise terminal devices 101, 102 and 103, a network 104 and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.

The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving Picture Experts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.

It should be noted that, the method for guiding health based on similar patients provided in the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the device for guiding health based on similar patients is generally disposed in the server/terminal device.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.

Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.

With continued reference to fig. 2, a flowchart of one embodiment of a similar patient based health guidance method according to the present application is shown, comprising the steps of:

step S201, obtaining a first patient characteristic of a patient to be tested, and inputting the first patient characteristic into a preset saccharification prediction model to obtain a prediction result.

In this embodiment, the first patient characteristic includes basic information of the patient to be tested, data automatically acquired by using a wearable device or the like in the chronic disease management process, or data manually uploaded by the patient, and a blood glucose value in a preset period. The basic information is information filled after the patient registers the diabetes health management system, and comprises gender, age, height, weight, education level, smoking history, drinking history, past medical history, diabetes years, medication information and the like; data automatically collected using a wearable or the like device or data manually uploaded by a patient includes blood glucose data (e.g., fasting blood glucose, postprandial blood glucose, hypoglycemic events), exercise conditions, sleep conditions, heart rate, blood pressure, etc.; blood glucose levels in a predetermined cycle, for example, a glycation level of less than 7 for every three months in a patient, is indicated as glycation-up to 7 for a glycation-up (a diabetic patient needs to go to a hospital for a test of glycation every three months).

In addition, the first patient characteristics include new characteristics generated from the collected information, such as the number of blood glucose uploads per week, the average of blood glucose per week, etc.

In this embodiment, the saccharification prediction model specifically adopts an XGBoost (eXtreme Gradient Boosting, full gradient descent tree) model, which is a machine learning model for classification and regression problems, and the main idea is to integrate more weak classifiers (such as decision trees) to realize the function of a strong classifier. That is, the XGBoost model is composed of a plurality of weak classifiers, one input data is input to the plurality of weak classifiers, respectively, to obtain a plurality of output results, and the plurality of data results are superimposed to obtain final output data.

Prior to training the XGBoost model, a number of super parameters are determined, such as learning_rate, max_depth (the ratio of samples randomly sampled per tree), subsamples (the ratio of the number of columns used to control each random sample), num_round (the number of iterations), max_leaf_nodes (the number of tree maximum leaf nodes), and so forth.

In specific implementation, the first patient characteristic is input into a preset saccharification prediction model XGBoost model, and a prediction result is obtained through calculation, wherein the prediction result is a saccharification standard condition and comprises saccharification standard and saccharification non-standard.

The XGBoost model is trained by the following specific implementation steps:

step A, initializing, namely giving the same initialization weight to all sample data sets in a training set;

and B, performing iterative computation for m times, classifying each iteration by adopting a weak classifier algorithm, and calculating the error rate of the weak classifier: e, e _m ＝∑w _i I(y _i ≠G _m x _i )/∑w _i ，w _i Represents the weight of the ith sample, G _m Represents the mth weak classifier, I represents the transformation matrix of the weak classifier, x _i A row vector representing the ith sample, y _i Column vector representing the ith sample, e _m Representing the error rate of the weak classifier;

step C, calculating an objective function, introducing a regularization term, and optimizing a loss function by adopting a gradient descent method in an iteration process;

step D, updating the weight of the weak classifier, iterating the m+1st time, and updating the weight of the ith sample to w _inew ；

E, after the weak classifier iterative computation is completed, obtaining a predicted value W of each data sample by adopting a voting mode _C Representing the condition of meeting the standard of saccharification.

In this example, the saccharification standard condition is denoted as Y, the saccharification standard is y=1, the saccharification is not standard, and y=0.

It is emphasized that to further ensure the privacy and security of the first patient characteristics, the first patient characteristics may also be stored in a blockchain node.

The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.

Step S202, determining a crowd opposite to the prediction result as a target crowd, and acquiring target patient characteristics of each target patient in the target crowd.

In this embodiment, when the saccharification reaches the standard, the crowd opposite to the predicted result is the people who reach the standard, and when the predicted result is the people who reach the standard, the crowd opposite to the predicted result is the people who reach the standard.

After the target crowd is determined, the target patient characteristics of each target patient in the target crowd are obtained, wherein the target patient characteristics also comprise basic information of a patient to be detected, data automatically acquired by using wearable equipment in a chronic disease management process or data manually uploaded by the patient and blood glucose values in a preset period, and new characteristics generated according to the collected information are also similar to the first patient characteristics, and are not repeated herein.

Step S203, searching according to the first patient characteristics and the target patient characteristics to obtain a similar patient similar to the patient to be tested.

Specifically, according to the first patient characteristics and the target patient characteristics, calculating the similarity between the patient to be tested and each target patient, and sequencing the similarity to obtain a sequencing result, wherein a preset number of target patients are selected from the sequencing result to serve as similar patients.

In this embodiment, the similarity algorithm may adopt a cosine similarity algorithm, a pearson correlation coefficient algorithm, a Jaccard similarity coefficient algorithm, a euclidean distance algorithm, or the like.

As a specific implementation mode, the similarity between the patient to be detected and the target patient is calculated by adopting the Euclidean distance algorithm, and the calculation formula is as follows:

wherein X represents a first patient feature vector of a patient to be tested, Y represents a target patient feature vector of a target patient, and n represents the feature number.

It should be understood that the smaller the calculated Euclidean distance, the more similar the patient to be tested and the target patient, i.e. the greater the similarity.

Calculating the similarity, sorting the target patients according to the sequence from the big similarity to the small similarity to obtain a sorting result, and selecting the target patients with the preset number in front of the sorting result as similar patients.

It should be noted that searching for a similar patient that is opposite to the patient's own outcome to be tested is desirable to achieve the desired outcome with minimal changes, and to increase the aggressiveness of the patient's ability to develop good self-management.

Step S204, health guidance is conducted on the patient to be tested based on the similar patients.

In this embodiment, similar patient characteristics of similar patients are obtained, differences between the similar patient characteristics and the first patient characteristics are compared to obtain distinguishing characteristics, a guiding suggestion is generated according to the distinguishing characteristics, and the patient to be tested is guided according to the guiding suggestion.

Specifically, when the saccharification predicted result of the next stage of the patient to be tested is that the saccharification reaches the standard, that is, y=1, the current self-health management state of the patient to be tested is good, the similar patient most similar to the patient to be tested is retrieved from the target crowd with the saccharification not reaching the standard, and the guiding suggestion is given by comparing the difference (distinguishing feature) between the first patient feature and the similar patient feature.

For example, the similar patient features and the first patient features are listed separately, the differences between the corresponding features are calculated, the similar patient features are listed in order of the differences from large to small, and the similar patient features with the differences of 0 are not listed. Assuming that the patient under test and similar patient a have three different characteristics, the three characteristics are listed as follows:

Number of blood glucose uploads per week: 1 time;

weight of: 60kg;

motion conditions: 20 minutes/week.

These characteristics indicate that the patient to be tested should subsequently take care of the control of these characteristic measures and in particular to what extent saccharification would otherwise be likely to become substandard.

When the saccharification predicted result of the next stage of the patient to be measured is not up to standard, namely Y=0, which means that the current self-health management state of the patient to be measured is poor, the behavior of the patient to be measured needs to be changed in a targeted manner, the similar patient most similar to the patient to be measured is searched from the target crowd with the saccharification up to standard, and the guidance suggestion is given by comparing the difference (distinguishing characteristic) between the characteristics of the first patient and the characteristics of the similar patient.

Similarly, similar patient features and first patient features are listed separately, differences between the corresponding features are calculated, similar patient features are listed in order of differences from greater to lesser, and similar patient features with differences of 0 are not listed. Assuming that the patient under test and similar patient a have three different characteristics, the three characteristics are listed as follows:

number of blood glucose uploads per week: 10 times;

smoking cessation;

mean weekly blood glucose: 6.9.

the patient to be tested changes in a targeted manner according to the instruction advice, and tries to reach the standard.

The guiding advice generated in the embodiment can clearly show the patient management target and provide accurate assistance related to health, so that the compliance of patient management is improved.

According to the application, the patient is predicted by the saccharification prediction model, so that the patient can predict the saccharification standard condition of the patient in the next stage according to the current state, the patient can know the health condition of the patient, meanwhile, the health guidance is provided for the patient to be tested according to the condition of the similar patient with opposite prediction results, the health help can be provided for the patient more accurately, meanwhile, the health guidance can clearly show the patient management target, the patient can intervene as early as possible, and the good self-management capability is cultivated.

In some optional implementations of this embodiment, before the step of acquiring the first patient characteristic of the patient to be tested and inputting the first patient characteristic into the preset glycation prediction model, the method further includes:

step S301, collecting a disease data set, and obtaining a second patient characteristic and saccharification condition corresponding to each patient according to the disease data set.

In this embodiment, the disease data set may be obtained from a diabetes health management system in which the patient is registered, or may be obtained from data automatically acquired by using a wearable device or manually uploaded by the patient during the chronic disease management process, or may be obtained from clinical medical data.

A second patient characteristic corresponding to each patient may be obtained from the patient dataset and saccharification is up to standard.

In some alternative implementations of the present embodiment, the acquired second patient characteristic is normalized. Specifically, each second patient characteristic is processed in the same range, the prediction effect is prevented from being influenced by overlarge difference between the second patient characteristics, and the characteristics are standardized by using the following formula:

wherein x' represents a second patient characteristic after normalization, x represents an untreated second patient characteristic,representing the mean value of the second patient characteristic, and S represents the standard deviation of the feature vector of the second patient characteristic.

And step S302, marking the characteristics of the second patient by taking the saccharification meeting the standard as a label, and obtaining the disease characteristic data.

In this embodiment, the second patient characteristic is denoted as X, the saccharification standard condition is denoted as Y, the saccharification standard condition Y is used as a label, the second patient characteristic X is labeled, and the saccharification prediction model is trained using X as an independent variable and Y as a dependent variable.

Step S303, training the pre-constructed initial prediction model according to the disease characteristic data to obtain a saccharification prediction model.

Specifically, training data and verification data are obtained according to the disease characteristic data, model parameters of an initial prediction model are adjusted based on the training data to obtain a model to be verified, the verification data are input into the model to be verified for verification to obtain a verification result, and when the verification result is greater than or equal to a preset threshold value, the model to be verified is determined to be a saccharification prediction model.

In this embodiment, the disease feature data is proportionally and randomly divided into training data for training a model and verification data for verifying the trained model.

In some optional implementations, the step of adjusting the model parameters of the initial prediction model based on the training data to obtain the model to be verified includes:

and inputting the training data into an initial prediction model, outputting a predicted saccharification result, calculating a loss function according to the predicted saccharification result, and adjusting model parameters of the initial prediction model based on the loss function.

In this embodiment, the model parameters are adjusted according to the loss function, the iterative training is continued, the model is trained to a certain extent, at this time, the performance of the model reaches an optimal state, and the loss function cannot be continuously reduced, i.e., converged. The convergence judging mode only needs to calculate the loss function in the front and back iteration, if the loss function is still changing, training data can be continuously selected and input into the model to be verified so as to continuously carry out iteration training on the model; if the loss function does not significantly change, the model is considered to converge, and the final model is outputted as a saccharification prediction model.

The saccharification prediction model in the present embodiment can be denoted as F (X), and if the prediction result y=f (X), the saccharification prediction model uses the XGBoost model, and has an advantage of high accuracy and capability of automatically processing the missing value.

According to the application, the saccharification prediction model is trained to be used for predicting the saccharification standard condition of the patient in the next stage, so that the model precision can be improved, and the accuracy of a prediction result can be further improved.

In some alternative implementations, to accurately verify the predicted results for the saccharification compliance situation and to comprehensively analyze each performance of the saccharification prediction model, the predicted results may be evaluated using three evaluation indicators commonly used in machine learning: accuracy P (Precision), recall R (Recall), and F value (F-Score).

The application is operational with numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The application can be applied to the field of intelligent medical treatment, thereby promoting the construction of intelligent cities.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by computer readable instructions stored in a computer readable storage medium that, when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).

It should be understood that, although the steps in the flowcharts of the figures 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 in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.

With further reference to fig. 4, as an implementation of the method shown in fig. 2 described above, the present application provides an embodiment of a similar patient-based health guidance device, which corresponds to the method embodiment shown in fig. 2, and which is particularly applicable to a variety of electronic devices.

As shown in fig. 4, the health guidance device 400 based on the similar patient according to the present embodiment includes: a prediction module 401, an acquisition module 402, a retrieval module 403, and a guidance module 404. Wherein:

the prediction module 401 is configured to obtain a first patient characteristic of a patient to be tested, and input the first patient characteristic into a preset saccharification prediction model to obtain a prediction result;

the obtaining module 402 is configured to determine a crowd opposite to the prediction result as a target crowd, and obtain a target patient characteristic of each target patient in the target crowd;

the retrieval module 403 is configured to retrieve the first patient feature and the target patient feature to obtain a similar patient similar to the patient to be tested;

the guiding module 404 is configured to conduct health guidance on the patient to be tested based on the similar patient.

According to the health guidance device based on the similar patients, the prediction of the patients is predicted through the saccharification prediction model, so that the patients can predict the saccharification standard condition of the patients at the next stage according to the current state, the patients can know the health condition of the patients, meanwhile, health guidance is provided for the patients to be tested according to the conditions of the similar patients with opposite prediction results, health help can be provided for the patients more accurately, meanwhile, the health guidance can clearly show the patient management target, the patients can intervene as early as possible, and good self-management capacity is cultivated.

In this embodiment, the retrieving module 403 is further configured to:

sequencing the similarity to obtain a sequencing result;

The present embodiment improves the aggressiveness of patient culture for good self-management by looking for similar patients with opposite patient's own outcomes to be tested, hopefully with minimal changes to achieve the desired outcome.

In some alternative implementations of the present embodiment, the guidance module 404 is further configured to:

The guiding advice generated by the embodiment can clearly show the patient management target and provide accurate assistance related to health, so that the compliance of patient management is improved.

In some optional implementations of this embodiment, the apparatus 400 further includes: the training module comprises an acquisition sub-module, a labeling sub-module and a training sub-module, wherein the acquisition sub-module is used for acquiring a disease data set, and obtaining the corresponding second patient characteristics and saccharification standard conditions of each patient according to the disease data set; the labeling submodule is used for labeling the characteristics of the second patient by taking the saccharification meeting the standard as a label to obtain diseased characteristic data; and the training sub-module is used for training the pre-constructed initial prediction model according to the disease characteristic data to obtain the saccharification prediction model.

According to the embodiment, the saccharification prediction model is trained to be used for the saccharification standard condition prediction of the next stage of the patient, so that the model precision can be improved, and the accuracy of a prediction result can be further improved.

In this embodiment, the training sub-module includes an acquiring unit, an adjusting unit, and a verification unit, where the acquiring unit is configured to acquire training data and verification data according to the disease feature data; the adjusting unit is used for adjusting the model parameters of the initial prediction model based on the training data to obtain a model to be verified; the verification unit is used for inputting the verification data into the model to be verified for verification, obtaining a verification result, and determining the model to be verified as the saccharification prediction model when the verification result is greater than or equal to a preset threshold value.

In this embodiment, the adjusting unit is further configured to:

In this embodiment, the training module further comprises a standard processing sub-module for performing a standardized processing of the second patient characteristic.

The embodiment can avoid the influence of overlarge difference among features on the prediction effect through standardization processing.

In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 5, fig. 5 is a basic structural block diagram of a computer device according to the present embodiment.

The computer device 5 comprises a memory 51, a processor 52, a network interface 53 which are communicatively connected to each other via a system bus. It should be noted that only the computer device 5 with components 51-53 is shown in the figures, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.

The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.

The memory 51 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 51 may be an internal storage unit of the computer device 5, such as a hard disk or a memory of the computer device 5. In other embodiments, the memory 51 may also be an external storage device of the computer device 5, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 5. Of course, the memory 51 may also comprise both an internal memory unit of the computer device 5 and an external memory device. In this embodiment, the memory 51 is typically used to store an operating system and various types of application software installed on the computer device 5, such as computer readable instructions based on a similar patient's health guidance method, and the like. Further, the memory 51 may be used to temporarily store various types of data that have been output or are to be output.

The processor 52 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 52 is typically used to control the overall operation of the computer device 5. In this embodiment, the processor 52 is configured to execute computer readable instructions stored in the memory 51 or process data, such as computer readable instructions for executing the similar patient based health instruction method.

The network interface 53 may comprise a wireless network interface or a wired network interface, which network interface 53 is typically used to establish communication connections between the computer device 5 and other electronic devices.

According to the method, the steps of the method for guiding health of the similar patient based on the embodiment are realized when the processor executes the computer readable instructions stored in the memory, and the prediction of the patient is predicted through the saccharification prediction model, so that the patient can predict the saccharification standard reaching condition of the next stage of the patient according to the current state, the patient can know the health condition of the patient, meanwhile, health guidance is provided for the patient to be tested according to the condition of the similar patient with opposite prediction results, health help can be provided for the patient more accurately, meanwhile, the health guidance can clearly show the management target of the patient, the patient can intervene as early as possible, and good self-management capability is cultivated.

The application also provides another embodiment, namely provides a computer readable storage medium, wherein the computer readable storage medium stores computer readable instructions, the computer readable instructions can be executed by at least one processor, so that the at least one processor executes the steps of the health guidance method based on similar patients, the patient is predicted through a saccharification prediction model, the patient can predict the saccharification standard condition of the patient at the next stage according to the current state, the patient can know the health condition of the patient, meanwhile, health guidance is provided for the patient to be tested according to the condition of the similar patient with opposite prediction results, health help can be provided for the patient more accurately, meanwhile, the health guidance can clearly show the patient management target, the patient can intervene early, and the patient has good self-management capability.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.

It is apparent that the above-described embodiments are only some embodiments of the present application, but not all embodiments, and the preferred embodiments of the present application are shown in the drawings, which do not limit the scope of the patent claims. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a thorough and complete understanding of the present disclosure. Although the application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing description, or equivalents may be substituted for elements thereof. All equivalent structures made by the content of the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the scope of the application.

Claims

1. A method of health guidance based on similar patients, comprising the steps of:

Training a pre-constructed initial prediction model according to the disease characteristic data to obtain a saccharification prediction model;

performing health guidance on the patient to be tested based on the similar patient;

the step of retrieving according to the first patient feature and the target patient feature to obtain a similar patient similar to the patient to be tested includes:

sequencing the similarity to obtain a sequencing result;

selecting a preset number of target patients from the sorting result as the similar patients;

the step of training the pre-constructed initial prediction model according to the disease characteristic data to obtain the saccharification prediction model comprises the following steps:

2. The method of claim 1, wherein the step of conducting health guidelines for the patient under test based on the similar patient comprises:

3. The method of claim 1, wherein the step of adjusting model parameters of the initial predictive model based on the training data comprises:

4. The method of claim 1, further comprising, prior to the step of labeling the second patient characteristic with the saccharification achievement of standard as a label:

and normalizing the second patient characteristic.

5. A similar patient-based health guidance device, comprising:

the training module comprises an acquisition sub-module, a marking sub-module and a training sub-module;

the acquisition sub-module is used for acquiring a disease data set, and acquiring the corresponding second patient characteristics and saccharification standard conditions of each patient according to the disease data set;

the labeling submodule is used for labeling the characteristics of the second patient by taking the saccharification meeting the standard as a label to obtain diseased characteristic data;

the training sub-module is used for training the pre-constructed initial prediction model according to the disease characteristic data to obtain a saccharification prediction model;

the guiding module is used for conducting health guidance on the patient to be tested based on the similar patient;

the retrieval module is further to:

sequencing the similarity to obtain a sequencing result;

the training sub-module comprises an acquisition unit, an adjustment unit and a verification unit,

the acquisition unit is used for acquiring training data and verification data according to the disease characteristic data;

the adjusting unit is used for adjusting the model parameters of the initial prediction model based on the training data to obtain a model to be verified;

and the verification unit is used for inputting the verification data into the model to be verified for verification to obtain a verification result, and determining the model to be verified as the saccharification prediction model when the verification result is greater than or equal to a preset threshold value.

6. A computer device comprising a memory having stored therein computer readable instructions which when executed implement the steps of the similar patient based health instruction method of any of claims 1 to 4.

7. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the similar patient based health instruction method of any of claims 1 to 4.