CN113707255A

CN113707255A - Health guidance method, device, computer equipment and medium based on similar patients

Info

Publication number: CN113707255A
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: 2021-11-26
Anticipated expiration: 2041-08-31
Also published as: CN113707255B

Abstract

The embodiment of the application belongs to the field of artificial intelligence, 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 guidance device, a computer device and a storage medium based on similar patients. Further, the present application also relates to blockchain techniques in which a first patient characteristic may be stored. The patient management target can be clearly displayed, so that the patient can intervene as early as possible, and good self-management capability is developed.

Description

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

Technical Field

The present application relates to the field of artificial intelligence and digital medical technology, and in particular, to a health guidance method, apparatus, computer device and 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, chronic respiratory system diseases and the like, and the chronic diseases are diseases closely related to bad behaviors and life styles and have the characteristics of long disease course, complex etiology, serious health damage, serious social hazard and the like. With the rapid development of the economy and the change of the life style of residents in China, the morbidity and mortality of chronic diseases are continuously increased, and the burden of diseases of the masses is increasingly heavy, so that the disease becomes one of the major public health problems which seriously threaten the health of residents in China and influence the development of the national economy and society. Moreover, chronic diseases are difficult to cure radically and mainly depend on the long-term self-health management of patients.

Taking diabetes as an example, diabetes is a relatively common chronic disease which endangers the healthy life of people at present. Diabetes has become the third major chronic disease after cardiovascular and cerebrovascular diseases and malignant tumors, threatening human health. With the general improvement of the living standard and the acceleration of the life rhythm of people in China, the number of patients with diabetes is increasing at a striking speed and developing towards the low age. The classical prevention strategy of diabetes is a management mode 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 life are achieved through good blood sugar and metabolism control.

However, the self-management difficulty of the patient is very high, the problems of lack of understanding on the diabetes risk, low attention degree, poor compliance and the like exist, and therefore, a diabetes health management system capable of helping the patient to improve the self-management capability is very needed. Most of the existing diabetes chronic disease management systems focus on blood sugar monitoring, such as early warning of abnormal blood sugar values or providing blood sugar fluctuation reports within a certain period of time before, and similar forms can only summarize the state of a patient in a certain period of time, and cannot clearly prompt and guide the next behaviors of the patient, so that the self-management capability of the patient is weakened, and the prevention and control of the disease condition are not facilitated.

Disclosure of Invention

The embodiment of the application aims to provide a health guidance method, a health guidance device, a computer device and a storage medium based on similar patients, so as to solve the technical problem that patients in the related art cannot know their own health conditions and management targets, so that self health management cannot be performed in a targeted manner, and chronic diseases are prevented and controlled.

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

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

retrieving according to the first patient characteristic and the target patient characteristic to obtain a similar patient similar to the patient to be detected;

and performing health guidance on the patient to be tested based on the similar patients.

Further, the step of retrieving according to the first patient characteristic and the target patient characteristic to obtain 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 detected and each target patient;

sequencing the similarity to obtain a sequencing result;

and selecting a preset number of target patients from the sequencing results as the similar patients.

Further, the step of performing health guidance on the patient to be tested based on the similar patients comprises:

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

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

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

acquiring a diseased data set, and acquiring a second patient characteristic and a saccharification standard reaching condition corresponding to each patient according to the diseased data set;

marking the second patient characteristic by using the saccharification standard reaching condition as a label to obtain diseased characteristic data;

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

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

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

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

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 the model parameters of the initial prediction model based on the training data comprises:

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 the model parameters of the initial prediction model based on the loss function.

Further, the step of tagging the second patient characteristic with the glycation achievement compliance may be preceded by the step of tagging the second patient characteristic with a glycation compliance status comprising:

normalizing the second patient characteristic.

In order to solve the technical problem, an embodiment of the present application further provides a health guidance device based on similar patients, which adopts the following technical solutions:

the prediction module is used for 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;

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

the retrieval module is used for retrieving according to the first patient characteristic and the target patient characteristic 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 detected based on the similar patients.

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

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

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

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 guidance method as described above.

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

according to the method, a first patient characteristic of a patient to be tested is obtained, the first patient characteristic is input into a preset saccharification prediction model to obtain a prediction result, a crowd opposite to the prediction result is determined as a target crowd, the target patient characteristic of each target patient in the target crowd is obtained, retrieval is carried out according to the first patient characteristic and the target patient characteristic to obtain similar patients similar to the patient to be tested, and health guidance is carried out on the patient to be tested based on the similar patients; this application predicts the prediction of patient through saccharification prediction model for the patient can be according to the saccharification standard reaching situation of current state prediction patient next stage, lets the patient know the health condition of self, simultaneously, provides healthy guidance for the patient that awaits measuring according to the similar patient's that the prediction result is opposite, can more accurately provide healthy help for the patient, and simultaneously, healthy guidance can clearly show patient management target, lets the patient intervene as early as possible, cultivates good self-management ability.

Drawings

In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.

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 diagram of one embodiment of a similar patient based health guidance device according to the present application;

FIG. 5 is a schematic block diagram of one embodiment of a computer device according to 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 application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase 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. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

The present application provides a health guidance method based on similar patients, which can be applied to a system architecture 100 as shown in fig. 1, wherein the system architecture 100 can include

terminal devices

101, 102, 103, a network 104 and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture Experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, 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 similar patient based health guidance method provided in the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the similar patient based health guidance apparatus 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 related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.

The artificial intelligence infrastructure generally includes 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 the like.

With continued reference to fig. 2, a flow chart 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 detected, 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 wearable devices 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. Wherein the basic information is information filled after the patient registers the diabetes health management system, and comprises sex, age, height, weight, education level, smoking history, drinking history, past medical history, diabetes age, medication information and the like; data automatically collected using a wearable or like device or manually uploaded by a patient includes blood glucose data (such as fasting blood glucose, postprandial blood glucose, hypoglycemic events), exercise status, sleep status, heart rate, blood pressure, and the like; the blood glucose value of the preset period, for example, the glycation value of the patient every three months, a glycation value less than 7 is recorded as glycation standard, and a glycation value greater than or equal to 7 is recorded as glycation not standard (a diabetic needs to go to a hospital for detection once every three months).

In addition, the first patient characteristics also include new characteristics generated from the collected information, such as weekly blood glucose uploads, weekly blood glucose averages, and the like.

In this embodiment, the saccharification prediction model specifically adopts an XGBoost (eXtreme Gradient Boosting) model, which is a machine learning model for classification and regression, and the main idea is to integrate many 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, and inputs one input data to the plurality of weak classifiers to obtain a plurality of output results, and superimposes the plurality of data results to obtain final output data.

Before the XGBoost model is trained, a number of hyper-parameters are determined, such as learning _ rate, max _ depth, subsample (the proportion of randomly sampled samples per tree), colomple _ byte (the ratio used to control the number of columns per random sample), num _ round (the number of iterations), max _ leaf _ nodes, and so on.

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

The XGboost model is trained by the following specific steps:

step A, initializing, and endowing the same initialization weight value to all sample data sets in a training set;

step B, iterative computation m times, each iterative computation adopts weak classifier algorithm to classify, and calculate the error rate of the weak classifier: e.g. of the type_m＝∑w_iI(y_i≠G_mx_i)/∑w_i，w_iRepresents the weight of the ith sample, G_mRepresents the m-th weak classifier, I represents the conversion matrix of the weak classifier, x_iA row vector, y, representing the ith sample_iA column vector representing the ith sample, e_mRepresenting the error rate of the weak classifier;

step C, calculating a target function, introducing a regular 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 for the (m + 1) th time, and updating the weight of the ith sample into w_inew；

E, after the iterative computation of the weak classifier is completed, obtaining the predicted value W of each data sample by adopting a voting mode_CAnd representing the saccharification standard reaching condition.

In this example, the achievement of saccharification is denoted as Y, where Y is 1, saccharification is not achieved, and Y is 0.

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

The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

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

In this embodiment, when the prediction result is a saccharification standard reaching result, the population opposite to the prediction result is a saccharification substandard population, and when the prediction result is a saccharification substandard population, the population opposite to the prediction result is a saccharification standard reaching population.

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

And step S203, retrieving according to the first patient characteristics and the target patient characteristics to obtain similar patients similar to the patient to be detected.

Specifically, according to the first patient characteristics and the target patient characteristics, the similarity between the patient to be tested and each target patient is calculated, the similarities are sorted to obtain a sorting result, and a preset number of target patients are selected from the sorting result to serve as similar patients.

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

As a specific implementation manner, the similarity between the patient to be detected and the target patient is calculated by using an 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 number of features.

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

And calculating the similarity, sequencing the target patients according to the sequence of the similarity from big to small to obtain a sequencing result, and selecting a preset number of target patients sequenced in front from the sequencing result as similar patients.

It should be noted that finding similar patients with outcomes opposite to the outcome of the patient to be tested is desirable to improve the motivation of the patient to develop good self-management ability by making the expected outcomes with minimal changes.

And step S204, carrying out health guidance 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 guidance suggestion is generated according to the distinguishing characteristics, and the patient to be tested is guided according to the guidance suggestion.

Specifically, when the result of the prediction of glycation of the patient to be tested in the next stage is that glycation reaches the standard, that is, Y is 1, it indicates that the current self-health management state of the patient to be tested is good, a similar patient most similar to the patient to be tested is retrieved from a target population with the non-standard glycation, and a guidance suggestion is given by comparing the difference (distinguishing characteristic) between the first patient characteristic and the similar patient characteristic.

For example, similar patient features and first patient features are listed separately, differences between corresponding features are calculated, similar patient features are listed in order of magnitude difference, and similar patient features with a difference of 0 are not listed. Assuming that the patient to be tested and the similar patient A have three characteristics which are different, the three characteristics are listed as follows:

number of blood glucose uploads per week: 1 time;

weight: 60 kg;

the motion condition is as follows: 20 minutes/week.

These characteristics indicate that the patient to be tested should subsequently be aware of the control of these characteristics, and to what extent, glycation may otherwise become substandard.

When the result of the saccharification prediction of the patient to be tested does not reach the standard in the next stage, namely Y is 0, the result shows that the current self health management state of the patient to be tested is poor, the self behavior needs to be changed in a targeted manner, a similar patient most similar to the patient to be tested is searched from a target group with the standard saccharification, and a guidance suggestion is given by comparing the difference (distinguishing characteristic) between the first patient characteristic and the similar patient characteristic.

Similarly, similar patient characteristics and first patient characteristics are listed separately, differences between corresponding characteristics are calculated, similar patient characteristics are listed in descending order of difference, and similar patient characteristics with a difference of 0 are not listed. Assuming that the patient to be tested and the similar patient A have three characteristics which are different, the three characteristics are listed as follows:

number of blood glucose uploads per week: 10 times;

smoking cessation;

weekly mean blood glucose: 6.9.

the patient to be tested changes the guide suggestion in a targeted manner subsequently, and tries to make the patient reach the standard.

The guidance suggestion generated in the embodiment can clearly show the management target of the patient and provide accurate help related to health, thereby improving the compliance of patient management.

This application predicts the prediction of patient through saccharification prediction model for the patient can be according to the saccharification standard reaching situation of current state prediction patient next stage, lets the patient know the health condition of self, simultaneously, provides healthy guidance for the patient that awaits measuring according to the similar patient's that the prediction result is opposite, can more accurately provide healthy help for the patient, and simultaneously, healthy guidance can clearly show patient management target, lets the patient intervene as early as possible, cultivates good self-management ability.

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

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

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

A second patient characteristic for each patient and glycation achievement status may be obtained from the pathology data set.

In some optional implementations of this embodiment, the obtained 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 the overlarge difference between the second patient characteristics, and the characteristics are standardized by using the following formula:

wherein x' represents the normalized second patient characteristic, x represents the untreated second patient characteristic,

represents the mean of the second patient feature and S represents the standard deviation of the feature vector of the second patient feature.

And step S302, marking the second patient characteristic by taking the saccharification standard reaching condition as a label to obtain diseased characteristic data.

In this embodiment, the second patient characteristic is labeled as X, the glycation achievement is labeled as Y, the glycation achievement Y is used as a label, and the glycation prediction model is trained by using X as an independent variable and Y as a dependent variable.

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

Specifically, training data and verification data are obtained according to the diseased characteristic data, model parameters of the 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 to obtain a verification result, and when the verification result is larger than or equal to a preset threshold value, the model to be verified is determined to be the saccharification prediction model.

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

In some optional implementation manners, 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:

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, iterative training is continued, the model is trained to a certain extent, at this time, the performance of the model reaches the optimal state, and the loss function cannot be continuously decreased, that is, converged. The convergence judgment mode only needs to calculate the loss function in the two iterations before and after, if the loss function is still changed, the training data can be continuously selected and input into the model to be verified so as to continuously carry out iterative training on the model; if the loss function does not change significantly, the model can be considered to be converged, and the final model is output as a saccharification prediction model.

The saccharification prediction model in this embodiment may be denoted as f (x), and the prediction result Y is denoted as f (x), and the saccharification prediction model uses an XGBoost model, which has the advantages of high precision and capability of automatically processing missing values.

The method and the device can be used for predicting the saccharification standard-reaching condition of the patient at the next stage by training the saccharification prediction model, so that the model precision can be improved, and the accuracy of the prediction result is further improved.

In some optional implementations, in order to accurately verify the predicted result of the saccharification standard reaching condition and comprehensively analyze various performances of the saccharification prediction model, three evaluation indexes commonly used in machine learning can be used for evaluating the predicted result: accuracy P (precision), recall R (Recall) and F-Score.

The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type 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, and therefore the construction of a smart city is promoted.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware associated with computer readable instructions, which can be stored in a computer readable storage medium, and when executed, the processes of the embodiments of the methods described above can be included. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a 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, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

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 apparatus, which corresponds to the method embodiment shown in fig. 2, and which is particularly applicable to various electronic devices.

As shown in fig. 4, the similar patient based health guidance device 400 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 measured, and input the first patient characteristic into a preset saccharification prediction model to obtain a prediction result;

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

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

the guidance module 404 is configured to perform health guidance on the patient to be tested based on the similar patients.

Above-mentioned health guidance device based on similar patient, through the prediction of saccharification prediction model prediction patient for the patient can be according to the saccharification standard reaching situation of current state prediction patient next stage, let the patient know the health condition of self, simultaneously, according to the similar patient's that the prediction result is opposite condition provides health guidance for the patient that awaits measuring, can more accurately provide health help for the patient, and simultaneously, health guidance can clearly show patient management target, let the patient intervene as early as possible, cultivate good self-management ability.

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

sequencing the similarity to obtain a sequencing result;

This embodiment is intended to improve the motivation of the patient to develop good self-management ability by finding similar patients whose outcomes are opposite to the outcome of the patient to be tested, and by making the least changes to obtain the expected outcomes.

In some optional implementations of this embodiment, the guidance module 404 is further configured to:

The guidance suggestion generated by the embodiment can clearly show the management target of the patient and provide accurate help related to health, thereby improving the compliance of patient management.

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

According to the method, the saccharification standard reaching condition of the patient at the next stage is predicted by training the saccharification prediction model, so that the model precision can be improved, and the accuracy of the prediction result is further improved.

In this embodiment, the training submodule includes an obtaining unit, an adjusting unit, and a verifying unit, and the obtaining unit is configured to obtain training data and verifying data according to the diseased feature data; the adjusting unit is used for adjusting 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 to obtain a verification result, and when the verification result is larger than or equal to a preset threshold value, the model to be verified is determined to be the saccharification prediction model.

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 on the second patient characteristic.

According to the embodiment, through standardization processing, the prediction effect can be prevented from being influenced by too large difference between the features.

In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 5, fig. 5 is a block diagram of a basic structure of a computer device according to the present embodiment.

The computer device 5 comprises a memory 51, a processor 52, a network interface 53 communicatively connected to each other via a system bus. It is noted that only a computer device 5 having components 51-53 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.

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

The memory 51 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 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 memory Card (Flash Card), and the like, which are provided on the computer device 5. Of course, the memory 51 may also comprise both an internal storage unit of the computer device 5 and an external storage device thereof. In this embodiment, the memory 51 is generally used for storing 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 health guidance method. Further, the memory 51 may also 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 (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 guidance method.

The network interface 53 may comprise a wireless network interface or a wired network interface, and the network interface 53 is generally used for establishing communication connections between the computer device 5 and other electronic devices.

In the embodiment, the processor executes the computer readable instructions stored in the memory to realize the steps of the health guidance method based on similar patients according to the above embodiment, and the patient prediction is predicted by the glycation prediction model, so that the patient can predict the glycation standard-reaching situation of the patient at the next stage according to the current state, and the patient can know the self health condition, and meanwhile, the health guidance is provided for the patient to be tested according to the similar patient condition opposite to the prediction result, so that the health help can be provided for the patient more accurately, and meanwhile, the health guidance can clearly show the patient management target, so that the patient can intervene as early as possible, and the good self-management capability can be developed.

The present application further provides another embodiment, which is to provide a computer-readable storage medium storing computer-readable instructions executable by at least one processor, so that the at least one processor performs the steps of the similar patient-based health guidance method as described above, and predicts the prediction of the patient through a glycation prediction model, so that the patient can predict the glycation standard reaching situation of the patient at the next stage according to the current state, and know the health condition of the patient, and at the same time, provide health guidance for the patient to be tested according to the similar patient condition with the opposite prediction result, so as to provide health assistance for the patient more accurately, and at the same time, the health guidance can clearly show the patient management goal, so that the patient can intervene as early as possible, and develop good self-management ability.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.

It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.

Claims

1. A method of similar patient based health guidance comprising the steps of:

2. The similar patient based health guidance method of claim 1, wherein the step of retrieving similar patients similar to the patient to be tested based on the first patient characteristic and the target patient characteristic comprises:

sequencing the similarity to obtain a sequencing result;

3. The similar patient based health guidance method of claim 1, wherein the step of performing health guidance on the test patient based on the similar patient comprises:

4. The similar patient based health guidance method of claim 1, wherein the step of obtaining a first patient characteristic of a test patient and inputting the first patient characteristic into a pre-defined glycation prediction model is preceded by the step of:

5. The similar patient based health guidance method of claim 4, wherein the step of training the pre-constructed initial prediction model according to the disease characteristic data to obtain the glycation prediction model comprises:

6. The similar patient based health guidance method of claim 5, wherein the step of adjusting model parameters of the initial predictive model based on the training data comprises:

7. The similar patient based health guidance method of claim 4, further comprising, prior to the step of tagging the glycation attainment condition with the second patient characteristic:

normalizing the second patient characteristic.

8. A similar patient based health guidance device, comprising:

9. A computer device comprising a memory having computer readable instructions stored therein and a processor that when executed performs the steps of the similar patient based health guidance method of any one of claims 1-7.

10. A computer readable storage medium having computer readable instructions stored thereon which, when executed by a processor, implement the steps of the similar patient based health guidance method of any one of claims 1-7.