CN111403038A

CN111403038A - AI-based constitution evaluation and health management system

Info

Publication number: CN111403038A
Application number: CN202010174134.0A
Authority: CN
Inventors: 张继亮; 许玲; 顾锡冬; 魏慧军; 董昌盛; 周迎春
Original assignee: Shanghai Luoshu Pharmaceutical Technology Co ltd
Current assignee: Shanghai Luoshu Pharmaceutical Technology Co ltd
Priority date: 2020-03-13
Filing date: 2020-03-13
Publication date: 2020-07-10

Abstract

The invention relates to an AI-based constitution evaluation and health management system, which at least comprises a first manager (1) and a processor (4), wherein the processor (4) can acquire the constitution evaluation result of a patient and can make a health management strategy for the patient based on the constitution evaluation result, and the processor (4) is configured to: generating expected health standards required to be met in a plurality of stages according to the health management strategy; the expected health criteria can be transmitted to the patient by the first manager (1) so that the patient can determine and feed back actual health management effect data to the first manager (1) based on the expected health criteria, wherein the actual health management effect data can be transmitted to the processor (4) and trigger the processor (4) to adjust the health management strategy of the set treatment course.

Description

AI-based constitution evaluation and health management system

Technical Field

The invention belongs to the technical field of medical systems, and particularly relates to a constitution evaluating and health managing system based on AI.

Background

With the development of society and the improvement of living standard of people, people pay more and more attention to the health status of people and strengthen physical exercises of people, but the physique of many people is in a sub-health status nowadays, so a powerful tool is needed to collect data of the health information of people, perform health assessment on the data, provide a reasonable health nursing plan and effectively improve and enhance the physique of people. There are many health management systems with different functions in the prior art.

For example, patent document CN110010247A discloses an artificial intelligence-based personalized physical health terminal service system, which collects parameters such as heart rate, blood pressure, body temperature, and pulse information of physical health related to a user, analyzes the collected parameters, compares the parameters with a physical model prestored in the system, and provides accurate health guidance information for the user. Meanwhile, data structure questioning is carried out on periodic physique parameters of a user, questioning and answering data analysis is carried out on the user through a physique health management AI, chart analysis and early warning are carried out on frequently answered problem points and problem points which are not improved of the user in a system blind area early warning module, personalized display information and early warning information are fed back through the physique health management AI, the interactivity of the system is improved, the user is detected and actively reminded of monitoring and improving the physique of the user, and the user can know the physique and the improvement of the user for a long time.

In summary, chronic diseases or physical conditions usually require a long period of time, and the patient cannot be precisely informed of specific improvement in a short period of time. In the prior art, the determination is usually carried out by means of periodic review. Therefore, it is necessary for the patient to know whether the treatment is effective or not, and in the case of an ineffective trend, the user can feed back in time, so that the system can adjust the treatment strategy in time. The present application aims to provide a constitution evaluation and health management system capable of overcoming the above-mentioned drawbacks.

Furthermore, on the one hand, due to the differences in understanding to the person skilled in the art; on the other hand, since the inventor has studied a lot of documents and patents when making the present invention, but the space is not limited to the details and contents listed in the above, however, the present invention is by no means free of the features of the prior art, but the present invention has been provided with all the features of the prior art, and the applicant reserves the right to increase the related prior art in the background.

Disclosure of Invention

The word "module" as used herein describes any type of hardware, software, or combination of hardware and software that is capable of performing the functions associated with the "module".

Aiming at the defects of the prior art, the invention provides an AI-based physique evaluation and health management system, which at least comprises: the first manager can acquire identity data, facial image data, tongue image data and/or medical record data of a patient and store the identity data, the facial image data, the tongue image data and/or the medical record data in a data management library; a processor capable of obtaining a physical fitness evaluation result of a patient based on the facial image data, tongue image data and/or medical record data stored in the data management base, and formulating a health management strategy for the patient based on the physical fitness evaluation result and pushing the health management strategy to the first manager, the processor being configured to: generating expected health criteria for a number of phases that need to be met in accordance with the health management policy generated by the processor based on data collected by the first manager; the expected health criteria generated via the processor can be transmitted to a patient by a first manager before the patient executes a health management policy, so that during the course of the patient executing the health management policy for a set course of treatment, the patient can determine and feed back to the first manager actual health management effect data based on the expected health criteria, wherein the actual health management effect data can be transmitted to the processor by the first manager and trigger the processor to adjust the health management policy for the set course of treatment. Through the mode, the following technical effects can be achieved: doctors generally prescribe the medicine for the cold for two days. The cold medicine is affected by the difference of constitutions or the diagnosis process, so that the condition that the cold of a patient cannot be cured can occur, the patient cannot know the treatment effect exactly, the effect of the medicine can be determined to a certain extent only according to the actual condition of the patient, but the effect that the cold cannot be cured is not known, and therefore the patient can choose to take all the medicines completely, if the medicine is still ineffective, the patient can conduct treatment repeatedly, and the optimal treatment time is missed. Similarly, extending to the treatment of chronic diseases or the maintenance of constitutions, it usually requires a long period of time, and the patient cannot be exactly informed of specific improvement in a short period of time. Through dividing a plurality of stages, the patient can be prevented from excessively depending on ineffective medicines, and then can go to the hospital again for diagnosis and treatment according to the health management strategy even before the medicines in the current course are not taken completely.

According to a preferred embodiment, the data management base can be used for storing at least symptom data of a plurality of diseases and treatment data and diagnosis method data corresponding to the plurality of diseases, wherein the health management policy at least includes treatment data for different diseases stored in the data management base, and the health management policy is configured to be adjusted according to the following steps: acquiring popular disease data within a set area range centered on the residence of the specific patient identified based on the identity data of the first manager, based on the residence information of the patient acquired by the first manager; the processor determines the similarity between the medical record data of the specific patient and the popular disease data based on the comparison between the medical record data and the popular disease data of the specific patient, wherein when the similarity is greater than a set threshold and the actual health management effect data is obtained because the diagnosis method data corresponding to the suspected disease type with the largest matching degree in the data management base cannot confirm the specific patient, the processor pushes the treatment measure data corresponding to the popular disease data stored in the data management base to the first manager so as to provide reference for the health management of the specific patient. Through the mode, the following technical effects can be at least achieved: the common outbreak of the epidemic disease can cause different users in the same area to suffer from the epidemic disease in sequence, and the relevant treatment data generated during the treatment process of the patient suffering from the epidemic disease in advance can provide reliable reference for the subsequent patients. For example, a specific drug for the epidemic disease can be obtained by referring to treatment data of a previous patient, and the specific drug can be used for treatment of a subsequent patient, thereby achieving the purpose of shortening the treatment period for an individual patient.

According to a preferred embodiment, the health management policy is configured to be adjusted according to the following steps: acquiring at least one second patient of which the distance between the residence and the residence of the first patient is smaller than a set threshold value based on the residence information of the first patient acquired by the first manager, and acquiring at least one third patient of which the coincidence degree with the medical record data of the first patient, which is stored in the data management base, is larger than the set threshold value based on the medical record data of the second patient, which is stored in the data management base; in the case that the disease type corresponding to the diagnosis data stored in the data management base of the third patient is the same as at least one suspected disease type, determining at least one drug category for the disease type based on the therapeutic measure data of the third patient and pushing the drug category to the first manager through the processor, so that in the case that the actual health management effect data of the first patient cannot meet the expected health standard, the corresponding drug involved in the therapeutic measure data can be replaced based on the at least one drug category. Through the mode, the following technical effects can be at least achieved: in the prior art, the physical fitness evaluating and health management system does not count the residence information of the patient. In the residential area, the drinking conditions, eating habits and environmental factors are slightly different, so that a plurality of people can suffer from the same disease. For example, in a residential area, drinking water conditions are the same, which, when water quality is affected, can cause multiple people in the residential area to suffer from digestive tract diseases. Alternatively, many people in the area of residence may suffer from upper respiratory illness due to environmental factors. For diseases such as cancer, it is usually characterized by strong latency and unobvious symptoms, which makes its diagnosis and discovery difficult. For example, in actual clinical cases, symptoms of lung cancer may include leg pain. Since the site involved in the symptom is not related to the disease focus, when a general doctor or a doctor with little experience performs diagnosis and treatment, for example, the general doctor or the doctor cannot take medicines according to the symptoms, the disease condition is further worsened, and finally, the cancer is in an advanced stage once found. Or require a number of diagnostic aids such as radiographs, CT examinations, etc. to confirm the disease type, thereby increasing the cost of medical care and increasing the risk of missing the optimal treatment time. By analyzing the population around the residence of the patient, the invention can find the disease with unobvious symptoms in time based on the regional characteristics.

According to a preferred embodiment, the health management policy is configured to be adjusted according to the following steps: based on the processor, formulating at least a first grade at which the therapeutic effect meets the expected effect and a second grade at which the therapeutic effect does not meet the expected effect; according to the time sequence, a plurality of recovery data formed by a plurality of treatment processes of a patient aiming at the same disease are obtained through the first manager and fed back to the processor, so that the processor can evaluate the plurality of recovery data to obtain a plurality of evaluation results; and grading a plurality of evaluation results, wherein in the case that the grade of the evaluation results is gradually increased to cause that the actual health management effect data cannot meet the expected health standard, a prompt is sent out by the processor and/or the first manager to change the medication type and/or the medication amount.

According to a preferred embodiment, the processor is configured to vary the medication type and/or the medication amount as follows: counting medication data for a particular patient identified based on the identity data of the first manager for the same disease type, thereby obtaining a frequency and/or quantity of consecutive repeat administrations of the same or similar medication by the particular patient over a time period set by a second manager for use by a healthcare worker in accordance with the first manager; in the event that the processor, based on its AI analysis module, finds that the frequency and/or number of consecutive repeated administrations of the same or similar medication by the patient is greater than a set threshold such that the actual health management effect data fails to meet the expected health criteria, an alert is issued via the second and first managers to prompt a change in medication type and/or dosage.

According to a preferred embodiment, the processor is capable of comparing the medical record data stored in the data management base with the symptom data to determine at least one suspected disease type, and the processor is configured to perform an inquiry of a specific patient with reference to the diagnosis method data corresponding to the prevalent disease data, wherein: and under the condition that the disease type corresponding to the diagnosis data of the third patient is the same as at least one suspected disease type, providing a reference for the inquiry of the first patient based on the diagnosis method data of the third patient, or under the condition that the disease type corresponding to the diagnosis data of the third patient is different from all the suspected disease types, and the confirmed diagnosis of the first patient cannot be completed based on the diagnosis method data corresponding to the suspected disease type with the maximum matching degree, providing a reference for the inquiry of the first patient based on the diagnosis method data of the third patient.

According to a preferred embodiment, the physique evaluation and health management system is further configured with at least one cloud platform capable of performing data interaction with the processor, and the cloud platform is configured to perform the data interaction according to the following manner: in the case that the processor performs a data transfer operation to transfer data to the cloud platform, the processor generates a first permission request and a second permission request based on the data transfer operation, wherein the first permission request is transmitted to the first manager, and the second permission request is transmitted to the second manager; the first manager performs a first modification operation on data needing to be transmitted by the processor according to the first permission request, and the second manager performs a second modification operation on the data needing to be transmitted by the processor according to the second permission request, so that private data in the data needing to be transmitted by the processor can be partially deleted based on the first modification operation and the second modification operation.

According to a preferred embodiment, the first manager can establish a communication connection with a specified second manager in a manner of initiating an access request to the specified second manager, or in a case where the first manager sends an access request to the processor, the processor can send the access request to the second manager in an idle state, so that the first manager can establish a communication connection with the second manager.

The invention also provides an intelligent monitoring system, which at least comprises: the first manager can acquire identity data, facial image data, tongue image data and/or medical record data of a patient and store the identity data, the facial image data, the tongue image data and/or the medical record data in a data management library; a processor capable of obtaining a physical fitness evaluation result of a patient based on the facial image data, tongue image data and/or medical record data stored in the data management base, and formulating a health management strategy for the patient based on the physical fitness evaluation result and pushing the health management strategy to the first manager, wherein the processor is configured to: generating expected health criteria for a number of phases that need to be met in accordance with the health management policy generated by the processor based on data collected by the first manager; the expected health criteria generated via the processor can be transmitted to a patient by a first manager before the patient executes a health management policy, so that during the course of the patient executing the health management policy for a set course of treatment, the patient can determine and feed back to the first manager actual health management effect data based on the expected health criteria, wherein the actual health management effect data can be transmitted to the processor by the first manager and trigger the processor to adjust the health management policy for the set course of treatment.

According to a preferred embodiment, the data management library can be used for storing at least symptom data of several diseases and treatment data and diagnosis method data corresponding to the several diseases, wherein the health management policy at least includes the treatment data for different diseases, and the health management policy is configured to be adjusted according to the following steps: acquiring popular disease data within a set area range centered on the residence of the specific patient identified based on the identity data of the first manager, based on the residence information of the patient acquired by the first manager; the processor determines the similarity between the medical record data of the specific patient and the popular disease data based on the comparison between the medical record data and the popular disease data of the specific patient, wherein when the similarity is greater than a set threshold and the actual health management effect data is obtained because the diagnosis method data corresponding to the suspected disease type with the largest matching degree in the data management base cannot confirm the specific patient, the processor pushes the treatment measure data corresponding to the popular disease data stored in the data management base to the first manager so as to provide reference for the health management of the specific patient.

The invention has the beneficial technical effects that: doctors generally prescribe the medicine for the cold for two days. The cold medicine is affected by the difference of constitutions or the diagnosis process, so that the condition that the cold of a patient cannot be cured can occur, the patient cannot know the treatment effect exactly, the effect of the medicine can be determined to a certain extent only according to the actual condition of the patient, but the effect that the cold cannot be cured is not known, and therefore the patient can choose to take all the medicines completely, if the medicine is still ineffective, the patient can conduct treatment repeatedly, and the optimal treatment time is missed. Similarly, extending to the treatment of chronic diseases or the maintenance of constitutions, it usually requires a long period of time, and the patient cannot be exactly informed of specific improvement in a short period of time. In the prior art, the determination is usually carried out by means of periodic review. Therefore, the patient can know whether the treatment is effective or not, and the user can feed back the treatment in time under the condition of generating an ineffective trend, so that the system can adjust the treatment strategy in time.

Drawings

FIG. 1 is a schematic diagram of the modular architecture of a preferred fitness evaluation and health management system of the present invention;

FIG. 2 is a schematic diagram of a modular structure of another preferred fitness evaluation and health management system according to the present invention; and

fig. 3 is a schematic diagram of the working principle of the preferred cloud platform of the present invention.

List of reference numerals

1: the first manager 2: the second manager 3: data management library

4: the processor 5: the cloud platform 6: evaluation module

7: the data management module 8: the interaction module 9: communication module

3 a: first storage unit 3 b: second storage unit 3 c: third memory cell

3 d: fourth storage unit 3 e: the fifth memory cell

4 a: the data receiving module 4 b: the classification module 4 c: matching module

4 d: decision module

Detailed Description

The following detailed description is made with reference to the accompanying drawings.

Example 1

As shown in fig. 1 to 3, the present invention provides an AI-based fitness evaluation and health management system, which at least includes a first manager 1, a second manager 2, a data management database 3 and a processor 4. The first manager 1, the second manager 2 and the data management library 3 are each communicatively coupled to a processor 4. Alternatively, the first manager 1, the second manager 2, the data management library 3 and the processor 4 can be communicatively coupled to each other, thereby enabling data transmission between the first manager 1, the second manager 2, the data management library 3 and the processor 4. The first manager 1 is configured for use by patients. The patient can enter his identity data and medical record data through the first manager 1. The identity data is used to confirm its identity to facilitate differentiation from other patients. The identity data may include name, gender, age, identification number, etc. Medical record data refers to data that can be used to characterize the status of its condition. For example, medical record data can include data on the duration of physical discomfort, specific symptoms of discomfort, and the like. The identity data and the medical record data can be transmitted to the data management base 3 for classified storage. That is, for each patient, the data management library 3 can individually configure a storage space for each patient, and the storage space can store all relevant identity data and medical record data about a certain patient. The processor 4 is capable of analyzing the medical record data input by the first manager 1 to make an aid in medical decision making. The aid decision may include the type of disease the patient may suffer from and its corresponding probability. For example, when the specific discomfort symptom data in the medical record data is headache, the processor may search the data in the data management database 3 to find out the disease category corresponding to all patients with headache symptoms. The probability of each disease type can be calculated statistically according to the number of the diseases. The greater the number of occurrences, the greater the probability of occurrence of the disease. It is understood that auxiliary judgment data can be included in the medical record data to facilitate the processor 4 to adjust the occurrence probability of each disease. For example, the auxiliary judgment data may include image data of a tongue, image data of a face, B-ultrasonic data, CT data, and the like of the patient. The auxiliary judgment data can provide an additional judgment basis, so that the processor 4 can improve the judgment accuracy. The second manager 2 can establish a communication connection with the first manager 1 according to the assistant diagnosis and treatment decision generated by the processor 4, so that the medical staff can finally confirm the disease type of the patient in an inquiry form.

Preferably, it can be understood that the processor 4 may be embedded with an artificial intelligence algorithm such as machine learning or deep learning, and the processor 4 may further perform continuous self-learning to achieve the purpose of improving the determination accuracy. The artificial intelligence algorithm can receive medical record data recorded by the patient in the diagnosis process and the diagnosis confirmation data finally formed by the second manager 2, and the artificial intelligence algorithm can adjust the weight values of the parameters through the diagnosis confirmation data, so that the auxiliary diagnosis decision of the processor 4 is more and more accurate. For example, the artificial intelligence algorithm may be an artificial neural network algorithm. Artificial neural network algorithms include, but are not limited to, recurrent neural networks, bidirectional recurrent neural networks, deep neural networks, convolutional neural networks, and stochastic neural networks.

Preferably, referring again to fig. 1, the physical fitness evaluation and health management system can further comprise at least one cloud platform 5. Each hospital may be provided with several first managers 1, several second managers 2, at least one data management library 3 and at least one processor 4. The processor 4 and the cloud platform 5 are communicatively coupled. For example, the processor 4 can be communicatively coupled to the cloud platform 5 via a wireless network. The cloud platform 5 is used for realizing data sharing in the data management databases 3 of different hospitals. Specifically, medical personnel at the first hospital may place access requirements on the cloud platform 5 via the processor 4. The cloud platform 5 may transmit the access request to the processor 4 of the second hospital, and the medical staff of the first hospital can access the data in the data management library 3 of the second hospital when the processor 4 of the second hospital is permitted. Or, medical personnel in different hospitals can upload treatment data of patients to the cloud platform 5 through their respective processors 4, and then medical personnel in different hospitals can access the cloud platform 5 through their respective processors 4, thereby realizing data sharing.

Preferably, the data management library 3 includes at least a first storage unit 3a, a second storage unit 3b, and a third storage unit 3 c. The first storage unit 3a is used for storing medical record data and identity data of different patients. The second storage unit 3b is used to store symptom data of various diseases. For example, symptoms of a cold may include coughing, dry throat, runny nose, and the like. The third storage unit 3b is used for storing therapeutic measure data and diagnosis confirming method data corresponding to various diseases. The therapeutic data may refer to data of a formula capable of treating the corresponding disease. Diagnostic method data may refer to diagnostic measure data necessary to diagnose the type of disease. For example, when the medical record data of the patient is too small to confirm the disease type of the patient, the diagnosis confirming method data may be diagnostic data such as CT data that can assist the confirmation of the disease type. For example, the specialist doctor can accurately determine the focus of the patient by means of inquiry by virtue of abundant treatment experience, and then the medicine is administered according to the symptoms. Therefore, the diagnostic method data may be inquiry data formed based on the clinical experience of the specialist doctor in each field.

Preferably, as shown in fig. 2, the processor 4 comprises at least a data receiving module 4a, a classification module 4b, a matching module 4c and a decision module 4 d. The data receiving module 4a is used for receiving the identity data and the medical record data input by the first manager 1. The classification module 4b can extract keywords in the medical record data, classify the patient according to the extracted keywords, and store the medical record data corresponding to the extracted keywords into the corresponding first storage unit 3 a. The medical record data can be associated with the corresponding patient, and all the medical record data of the patient can be obtained only by inputting the identity data of the patient during retrieval. For example, medical record data can include keywords such as "headache, cough, limp limbs," and the like. The classification module 4b can preliminarily determine the disease type according to the medical record data, and then store the medical record data of the patient according to the disease type. The matching module 4c is used for comparing the medical record data input by the patient with the symptom data stored in the second storage unit 3b, so as to determine the matching degree of the medical record data and various diseases. The matching degree is the degree of coincidence between the symptom and the symptom data in the medical record data. For example, the disease a in the second storage unit 3B may include a specific symptoms, and the symptoms in the medical record data may include B symptoms. When the symptoms of the medical record data coincide with the symptoms in the symptom data by n, the matching degree can be determined by

And (4) performing representation.

A larger value of (a) indicates a larger degree of matching. Through the matching module 4c, a plurality of suspected disease types can be selected according to the mode that the matching degree is from large to small. For example, a set threshold value of the matching degree can be set, and then the disease type with the matching degree larger than the set threshold value can be selected for the reference of the medical staff. Decision makingThe module 4d is able to select the corresponding treatment data and/or diagnostic data from the third memory unit 3c depending on the type of disease selected by the matching module 4 c. The data of the treatment measures and/or the data of the methods of diagnosis selected by the decision module 4d can be transmitted to the second management device 2 for reference by the medical staff.

Preferably, the first manager 1 and the second manager 2 may each include an evaluation module 6, a data management module 7, an interaction module 8, and a communication module 9. The interaction module 8 is used for data interaction with a patient or a medical staff. Interaction module 8 can be time mouse, keyboard, image collector, pronunciation input ware etc. and then through interaction module 8, the patient can be with in its identity data and case history data input first manager 1, medical personnel can be with in the access demand input second manager 2. The data management module 7 can be used to temporarily store patient identity data and medical history data, and it can also be used to store access requirement data for medical personnel. The data management module 7 may be a computer readable storage medium including, but not limited to, an internal memory and an external memory. The evaluation module 6 can be used for processing the data it receives and for transmitting them to the processor 4 or the data management 3. The communication module 9 can be used for communication between the evaluation module 6, the data management module 7 and the interaction module 8.

For the sake of easy understanding, the working principle of the physical examination and health management system of the present invention will be explained.

The first manager 1 and the second manager 2 can be in communication connection with each other, and the integration of the processor 4 enables the patient at the site A to communicate with the medical staff at the site B in real time through the first manager 1. The patient can register with the first manager 1 by means of the identity data and thus obtain the right of use of the first manager 1. The medical staff may also register with the second administrator 2 by means of his identity data and thus obtain the right of use of the second administrator 2. The patient first uploads his medical record data to the first manager 1. The medical record data can be realized in the form of images, characters or voice. The first manager 1 can transmit the received medical record data to the processor 4 for analysis and processing, so as to obtain a plurality of suspected disease types and therapeutic measure data and/or diagnosis confirmation method data corresponding to the suspected disease types. The processor 4 can transmit the corresponding patient identity data, medical record data, treatment measure data and diagnosis confirming method data to the second manager 2 in an idle state in the corresponding department according to the suspected disease type, so that the second manager 2 can establish communication connection with the first manager 1, and finally, medical staff can confirm the focus of the patient in an inquiry form. Preferably, the patient can also initiate an access request to the designated second manager 2 through the first manager 1. That is, the patient may designate a particular healthcare worker for whom to treat.

Example 2

This embodiment is a further improvement of embodiment 1, and repeated contents are not described again.

Preferably, the first manager 1 is also able to acquire recovery data of the patient. Recovery data refers to information that can characterize a patient's treatment. The data management library 3 may include a fifth storage unit 3e so that the recovery data can be stored in the fifth storage unit 3 e. For example, the recovery data may be response information after the patient takes the medicine, time node information of disease weakening, time node data of disease recovery, and the like. By retrieving the data, the processor 4 can evaluate the patient's efficacy to produce an evaluation result for adjusting the therapeutic measure data. That is, each stage can generate recovery data, and the evaluation of the recovery data of each stage can result in several corresponding evaluation results. The evaluation result may be divided into a first grade and a second grade. The first grade indicates that the efficacy meets the expected requirements. The second level indicates that the efficacy does not meet the expected requirements. For example, when the time node of no adverse reaction or weakened disease condition of the patient after taking the medicine meets the expected requirement or the time node of disease rehabilitation meets the expected requirement, the evaluation result is classified into a first grade. And when the time node of the patient with adverse reaction and weakened disease condition does not meet the expected requirement or the time node of the patient with recovered disease condition does not meet the expected requirement after the patient takes the medicine, dividing the evaluation grade into a second grade. During the treatment of the next course of treatment of the patient, the medical staff can adjust the treatment measure data according to the evaluation result to obtain better treatment effect. It can be understood that, according to actual needs, those skilled in the art can subdivide the evaluation result into several levels, and make corresponding adjustment strategies for the therapeutic measure data for different levels.

Preferably, the data management library 3 can further include a fourth storage unit 3 d. The fourth storage unit 3d can be used to store medication data for each patient for a set disease. The medication data at least includes information such as the type of medication, the amount of medication, the time of medication, etc. of the patient. For example, a patient may experience several colds over their lifetime, and each time the patient undergoes a treatment, the healthcare worker may administer a different type or amount of medication. The fourth storage unit 3d can record the medication type and the medication amount of the medication of the patient in the order of the medication time. The processor 4 is configured to adjust the therapeutic measure data it generates as follows:

a1: the medication data of a specific patient identified based on the identification data of the first manager 1 for the same disease type is counted to obtain the frequency with which the same or similar medicine is continuously and repeatedly taken by the specific patient during the time period set by the second manager 2 according to the first manager 1.

Specifically, the set time period may be one year. Patients may suffer from disease a m times during a set time period of the year. For example, m may be 5 times, and when the B drug is taken in 5 times, the B drug is continuously and repeatedly taken at a frequency of 5 times/year. When the drug B is not taken at least once in 5 times, the frequency can be determined by calculating the average value. Specifically, the time for the patient to first develop disease a is 1 month. The time for the patient to have disease a second time is 3 months. The time for the patient to develop disease a for the third time is 8 months. The fourth time the patient had disease a was 11 months. The patient had disease A for a fifth time of 12 months, wherein the patient did not take drug B at the time of disease A for the third time. The frequency of continuous repeated taking of the medicine B is that the medicine B is continuously taken for a set period of 1 to 3 months

And (4) times/month, wherein the frequency of continuously and repeatedly taking the medicine B by the patient in a set time period from 11 months to 12 months is 1 time/month. Finally, the frequency of continuous repeated taking of the B medicament is as follows in a set time period of one year

Second/month.

A2: in the case of a gradual increase in the grade of the evaluation result, or in the case where the processor 4, based on its AI analysis module, finds that the frequency and/or number of consecutive repeated administrations of the same or similar drug by the patient is greater than a set threshold value, the type and/or amount of medication is changed.

Specifically, the gradual increase of the grade of the evaluation result, or the frequency of the same or similar medicines being continuously and repeatedly taken by the patient is greater than the set threshold value, indicates that the patient has already developed a certain resistance to the medicines, and if the medicines are continuously taken again, the curative effect is further reduced, that is, the actual health management effect data of the patient cannot meet the expected health standard. Thus, a reminder may be issued by the processor 4 and/or the first manager 1, which in turn may prompt a change in medication type or a reduction in medication amount. For example, a change in drug class may select a drug that has negative cross-resistance with the current drug to replace the current drug. The AI analysis module may be an operator having a data operation function. It is possible to obtain the frequency with which the medicine is continuously taken by the patient according to the number of times the patient takes a particular medicine within a set time.

Through the mode, the following technical effects can be at least achieved: in the prior art, the physique evaluation and health management system does not count the medication data of patients aiming at the same disease, and in practical situations, the patients do not select a large hospital to see a doctor firstly for all diseases, for example, aiming at cold, and the patients can be treated for multiple times in a small clinic. The sub-diagnosis system is not usually provided in the small clinic, so that the medication data used by the patient in the small clinic is lost. Meanwhile, in practical cases, since the physician has limited experience, the small clinic may repeatedly use the same prescription for the same disease. After a patient is treated for multiple times, the patient can gradually generate resistance to the medicine in the prescription, so that the prescription completely loses the treatment efficacy, and finally, after the condition of an illness deteriorates, the patient can choose to go to a large hospital configured with a physique evaluation and health management system for treatment. According to the invention, the first manager 1 is configured, so that the patient can input the medication data of the patient, and further all the medication data of the patient aiming at the same disease can be stored, so that the treatment measure data finally given by the physique evaluation and health management system is more effective. Meanwhile, through the analysis of the medication data of the patient, the same drug can be prevented from being reused in a short time, and the risk of the disease generating resistance to the drug can be reduced.

Example 3

This embodiment is a further improvement on

embodiments

1 and 2, and repeated details are not repeated.

Preferably, the identity data comprises at least residence information of the patient. In case the first administrator 1 transmits the patient's identity data and medical record data it has acquired to the processor 4, the processor 4 also obtains the treatment data and/or the method of determination data as follows:

b1: at least one second patient whose residence is less than a set threshold from the residence of the first patient is acquired based on the residence information of the first patient.

In particular, the classification module 4b of the processor 4 is able to extract and place into its built-in map information according to the first patient's residence. The residence information for each patient can be placed in the map in the form of points. The classification module 4b will establish a circular coverage area with a radius R centered on the first patient's residence, and a falling into the circular coverage area indicates that the distance between the residence and the first patient's residence is less than the set threshold. The set threshold may be adjusted based on the R value. That is, one skilled in the art may adjust the R value to enable the at least one second patient to fall within the circular coverage area as the case may be.

B2: and acquiring at least one third patient with the coincidence degree of the medical record data of the first patient larger than a set threshold value based on the medical record data of the second patient.

In particular, the matching module 4c can compare the medical record data of the second patient with the medical record data of the first patient. The degree of coincidence can be determined by the amount of specific discomfort symptom data that the first patient and the second patient coincide with each other. The person skilled in the art can set the threshold value for the degree of overlap according to the actual requirements.

B3: in the case where the treatment data and/or method of determination data is obtained based on a match of the medical record data and the symptom data of the first patient, the method of determination data of the first patient is adjusted based on the method of determination data of the third patient, or the treatment data of the first patient is adjusted based on the treatment data of the third patient.

Specifically, based on the comparison between the medical record data of the first patient and the symptom data stored in the second storage unit 3b, the matching degree between the medical record data and various diseases can be determined, and then a plurality of suspected disease types can be screened out. The decision module 4d can preliminarily determine the corresponding therapeutic measure data and/or diagnosis confirming method data from the third storage unit 3c according to the screened plurality of suspected disease types. According to the matching degree, the suspected disease types can be prioritized. That is, the greater the degree of matching, the greater the priority. The smaller the degree of matching, the smaller the priority. Adjusting the method of determining data for the first patient based on the method of determining data for the third patient comprises at least the steps of:

c1: and under the condition that the disease type corresponding to the diagnosis data of the third patient is the same as the at least one suspected disease type, at least completing the inquiry of the first patient based on the diagnosis method data of the third patient.

C2: and under the condition that the disease type corresponding to the diagnosis data of the third patient is different from all the suspected disease types, and the confirmed diagnosis cannot be completed for the first patient based on the diagnosis method data corresponding to the suspected disease type with the maximum matching degree, at least the inquiry of the first patient is completed based on the diagnosis method data of the third patient.

Preferably, in case that the disease type corresponding to the diagnosis data of the third patient is the same as the at least one suspected disease type, at least one drug category for the disease type is determined based on the therapeutic measure data of the third patient and pushed to the first manager 1 and/or the second manager, wherein the therapeutic measure data is replaced based on the at least one drug category. For example, at least one drug in the therapeutic measure data may be replaced with a drug corresponding to the at least one drug category.

Through the mode, the following technical effects can be at least achieved: in the prior art, the physical fitness evaluating and health management system does not count the residence information of the patient. In the residential area, the drinking conditions, eating habits and environmental factors are slightly different, so that a plurality of people can suffer from the same disease. For example, in a residential area, drinking water conditions are the same, which, when water quality is affected, can cause multiple people in the residential area to suffer from digestive tract diseases. Alternatively, many people in the area of residence may suffer from upper respiratory illness due to environmental factors. For diseases such as cancer, it is usually characterized by strong latency and unobvious symptoms, which makes its diagnosis and discovery difficult. For example, in actual clinical cases, symptoms of lung cancer may include leg pain. Since the site involved in the symptom is not related to the disease focus, when a general doctor or a doctor with little experience performs diagnosis and treatment, for example, the general doctor or the doctor cannot take medicines according to the symptoms, the disease condition is further worsened, and finally, the cancer is in an advanced stage once found. Or require a number of diagnostic aids such as radiographs, CT examinations, etc. to confirm the disease type, thereby increasing the cost of medical care and increasing the risk of missing the optimal treatment time. By analyzing the population around the residence of the patient, the invention can find the disease with unobvious symptoms in time based on the regional characteristics.

Example 4

This embodiment is a further improvement of the foregoing embodiment, and repeated contents are not described again.

The invention also provides an intelligent monitoring system, which at least comprises: a first manager 1 capable of acquiring patient identity data, facial image data, tongue image data and/or medical record data and storing in a data management repository 3. A processor 4 capable of obtaining a physical fitness evaluation result of the patient based on the facial image data, tongue image data and/or medical record data and formulating a health management policy for the patient based on the physical fitness evaluation result, wherein the processor 4 is configured to: expected health criteria to be achieved for several phases are generated according to a health management policy. The expected health criteria can be transmitted to the patient by the first manager 1 before the patient executes the health management policy, so that during the course of the patient executing the health management policy for a set treatment session, the patient can determine its actual health management effect data based on the expected health criteria, wherein the actual health management effect data can be transmitted to the processor 4 by the first manager 1 and trigger the processor 4 to adjust the health management policy for the set treatment session.

Preferably, the data management database 3 is at least capable of storing symptom data of a plurality of diseases and treatment data and diagnosis method data corresponding to the plurality of diseases, wherein the health management policy at least includes treatment data for different diseases, and the health management policy is configured to be adjusted according to the following steps: based on the residence information of the patient collected by the first manager 1, the epidemic disease data within the set regional range centered on the residence of the specific patient identified based on the identity data of the first manager 1 is acquired. The processor 4 determines the similarity between the medical record data and the popular disease data based on the comparison between the medical record data and the popular disease data of the specific patient stored in the data management base 3, wherein when the similarity is larger than a set threshold and the diagnosis method data corresponding to the suspected disease type with the largest matching degree in the data management base 3 cannot complete the confirmed diagnosis of the specific patient, the treatment of the specific patient is completed based on the treatment measure data corresponding to the popular disease data.

Example 5

Preferably, the cloud platform 5 is capable of acquiring prevalent disease data for the current time period. The epidemic disease data includes at least epidemic disease type information, its corresponding symptom information, and treatment information. The outbreak of different diseases often occurs under the influence of regions and seasons. For example, viral influenza is commonly found in spring and winter. Meanwhile, in a dense population region, influenza is more likely to outbreak. The cloud platform 5 can be networked with a disease monitoring center, and further can acquire popular disease data of each region from the disease monitoring center. Or, the cloud platform 5 can perform access analysis on the data management database 3 of each hospital, thereby acquiring the epidemic disease data. For example, the data management library 3 stores diagnosis data of all patients in a hospital visit. If a large number of patients have the same disease within a set period of time, the disease can be defined as a current stage of epidemic disease. Meanwhile, the cloud platform 5 may perform access analysis on the data management databases 3 of all hospitals in the set area. For example, the city or the district may be set as a set area, and the diagnosis confirmation data of all patients in all hospitals in the set area may be analyzed and counted to determine whether or not there is a large number of outbreaks of epidemic diseases at the present time.

Preferably, the processor 4 also obtains the treatment data and/or the diagnostic data as follows:

d1: acquiring popular disease data in a set area range with the residence of the patient as the center of a circle based on the residence information of the patient, wherein the popular disease data at least comprises popular disease type information, corresponding symptom information, diagnosis confirming information and treatment measure information.

D2: similarity of the medical record data and the epidemic disease data is determined based on comparison of the medical record data of the patient and the symptom information of the epidemic disease data.

D3: when the similarity is greater than the set threshold and the actual health management effect data cannot be obtained based on the diagnosis method data corresponding to the suspected disease type with the largest matching degree to confirm the patient, the processor 4 pushes the treatment measure data corresponding to the popular disease data stored in the data management base 3 to the first manager 1 and/or the second manager to provide a reference for completing the health management of the specific patient. That is, the inquiry of the patient can be completed based on the diagnosis information corresponding to the epidemic disease data, or the treatment of the patient can be completed based on the treatment measure data corresponding to the epidemic disease data.

Example 6

Preferably, the processor 4 performs data transmission with the cloud platform 5 as follows:

f1: in the case where the processor 4 performs a data interworking operation to transmit data to the cloud platform 5, the processor 4 generates a first permission request and a second permission request based on the data interworking operation, wherein the first permission request is transmitted to the first manager 1 and the second permission request is transmitted to the second manager 2.

Specifically, the data interaction operation refers to that the processors 4 transmit data which can be shared to the cloud platform 5 for storage and backup, so that the data can be shared among different processors 4. The first permission request is for acquiring permission of the first manager 1. The second permission request is for acquiring permission of the second manager 2. That is, only data permitted by the first manager 1 and the second manager 2 can be transferred into the cloud platform 5.

F2: the first manager 1 performs a first modification operation on the data which needs to be transmitted by the processor 4 according to the first permission request, and the second manager 2 performs a second modification operation on the data which needs to be transmitted by the processor 4 according to the second permission request, so that the private data in the data which needs to be transmitted by the processor 4 can be partially deleted based on the first modification operation and the second modification operation.

Specifically, the first modification operation and the second modification operation both refer to partial deletion of private data in the data. The private data may be identity data of the patient, picture data related to a patient specific symptom, etc.

F3: the processor 4 uploads the data to the cloud platform 5, which the cloud platform 5 merely performs analysis to determine whether the frequency and/or number of consecutive repeated administrations of the same or similar medication by the patient is greater than a set threshold.

F4: the processor 4 is configured to perform encryption processing on data in a data encryption manner.

Specifically, the data encryption may employ a symmetric encryption algorithm such as DES, AES, or an asymmetric encryption algorithm such as RSA, DSA, ECC. By the method, the private data of the patient can be protected, and further the private data of the patient can be prevented from being leaked.

Example 7

Preferably, the first manager 1 is also able to acquire facial image data and tongue image data of the patient. The processor 4 is capable of obtaining an assessment of the patient's physical fitness based on the facial image data, tongue image data, and/or medical record data. The results of the physical assessment can be used to characterize the type of disease that the patient is currently suffering from. For example, the results of the physical fitness evaluation may indicate that the patient has a disease such as cold, diabetes, hypertension, and the like. The processor 4 can formulate a health management strategy for the patient based on the physical fitness evaluation results. The health management policy may be pushed to the first manager 1 and/or the second manager. Health management strategies are used to treat the disease from which the patient suffers.

Preferably, the processor 4 is configured to be able to generate several phases of desired health criteria to be achieved according to the health management policy. For example, when aiming at chronic conditions such as diabetes, hypertension, coronary heart disease, etc., it usually has a long treatment period, i.e. has a plurality of treatment courses. Thus, the present invention can establish the desired health criteria to be achieved for a given course of treatment for each course of treatment. The expected health criteria are used to measure whether the treatment effect is expected at the end of a given treatment session. For example, in the case of coronary heart disease, the expected health criteria for the first course of treatment may be set as whether blood pressure is reduced or whether the level of mental stress is reduced. Alternatively, the expected health criteria may be altered by the healthcare worker via the second manager. Preferably, the expected health criteria for the different phases can be different from each other, which can be determined based on the number of sessions. For example, different patients or physicians may have different expected recovery times for different chronic diseases. When the recovery time of a patient is required to be shortened, the method can be realized by increasing the dosage, shortening the treatment time of a single course of treatment, increasing the diagnosis times and the like. Thus, the expected health criteria can be established based on the expected recovery time required by the patient. For example, the expected wellness standard for the first course of treatment can be changed from a single to multiple.

Preferably, the expected health criteria can be transmitted to the patient by the first manager 1 before the patient executes the health management policy. During the process of executing the health management policy for the set treatment course, the patient can determine the actual health management effect data based on the expected health standard, wherein the actual health management effect data can be transmitted to the processor 4 through the first manager 1, and trigger the processor to adjust the health management policy for the set treatment course. Through the mode, the following technical effects can be at least achieved: doctors generally prescribe the medicine for the cold for two days. The cold medicine is affected by the difference of constitutions or the diagnosis process, so that the condition that the cold of a patient cannot be cured can occur, the patient cannot know the treatment effect exactly, the effect of the medicine can be determined to a certain extent only according to the actual condition of the patient, but the effect that the cold cannot be cured is not known, and therefore the patient can choose to take all the medicines completely, if the medicine is still ineffective, the patient can conduct treatment repeatedly, and the optimal treatment time is missed. Similarly, extending to the treatment of chronic diseases or the maintenance of constitutions, it usually requires a long period of time, and the patient cannot be exactly informed of specific improvement in a short period of time. In the prior art, the determination is usually carried out by means of periodic review. Therefore, the patient can know whether the treatment is effective or not, and the user can feed back the treatment in time under the condition of generating an ineffective trend, so that the system can adjust the treatment strategy in time.

Example 8

Preferably, the physique evaluation and health management system further comprises an online database. The online database can be communicatively coupled with the cloud platform 5. Historical literature findings of various diseases can be stored in an online database. Or various historical literature research results related to the constitution evaluation results. For example, for patients with cold constitution, various research institutes will study their pathogenesis, treatment precautions, recommended treatment regimens, etc., and finally form records through published papers. Therefore, the online database of the application can acquire historical documents aiming at different physique evaluation results or different diseases from each document system in an online crawling manner, and acquire research results of the historical documents. Finally, the health management policy recommended by the processor 4 can be adjusted by comparison with the results of the studies in the historical literature. The adjustment can be adding a certain medicine, adding a certain rehabilitation exercise mode and the like.

It should be noted that the above-mentioned embodiments are exemplary, and that those skilled in the art, having benefit of the present disclosure, may devise various arrangements that are within the scope of the present disclosure and that fall within the scope of the invention. It should be understood by those skilled in the art that the present specification and figures are illustrative only and are not limiting upon the claims. The scope of the invention is defined by the claims and their equivalents.

Claims

1. The AI-based physique evaluation and health management system at least comprises a first manager (1) and a processor (4), wherein the processor (4) can acquire the physique evaluation result of a patient and can make a health management strategy for the patient based on the physique evaluation result,

it is characterized in that the preparation method is characterized in that,

the processor (4) is configured to:

generating expected health standards required to be met in a plurality of stages according to the health management strategy;

the expected health criteria can be transmitted to the patient by the first manager (1) so that the patient can determine and feed back actual health management effect data to the first manager (1) based on the expected health criteria, wherein the actual health management effect data can be transmitted to the processor (4) and trigger the processor (4) to adjust the health management strategy of the set treatment course.

2. The system for physical fitness evaluation and health management according to claim 1, wherein the database (3) is at least capable of storing symptom data of a plurality of diseases and treatment data and diagnosis method data corresponding to the plurality of diseases, and the health management strategy is configured to be adjusted according to the following steps:

acquiring popular disease data within a set area range with the residence of a specific patient as the center of a circle based on the residence information of the patient acquired by the first manager (1);

the processor (4) determines the similarity between the medical record data of the specific patient and the epidemic disease data based on the comparison between the medical record data and the epidemic disease data of the specific patient, wherein when the similarity is larger than a set threshold, the processor (4) pushes the therapeutic measure data corresponding to the epidemic disease data to the first manager (1).

3. The fitness evaluation and health management system of claim 2, wherein the health management strategy is configured to be adjusted according to the following steps:

acquiring at least one second patient of which the distance between the residence and the residence of the first patient is smaller than a set threshold value based on the residence information of the first patient, and acquiring at least one third patient of which the coincidence degree with the medical record data of the first patient is larger than the set threshold value based on the medical record data of the second patient;

in case the disease type corresponding to the diagnosis data of the third patient is the same as the at least one suspected disease type, at least one drug category for the disease type is determined and pushed to the first manager (1) based on the treatment data of the third patient.

4. The fitness evaluation and health management system of claim 3, wherein the health management strategy is configured to be adjusted according to the following steps:

-formulating, based on the processor (4), at least a first and a second grade;

acquiring a number of recovery data of a patient via the first manager (1) and feeding back to the processor (4) such that the processor (4) can evaluate the number of recovery data to obtain a number of evaluation results;

and dividing the grades of a plurality of evaluation results, wherein in the case that the grades of the evaluation results are gradually increased, a prompt is sent out through the processor (4) and/or the first manager (1).

5. The fitness evaluation and health management system according to claim 4, wherein the processor (4) is configured to:

counting the medication data of a specific patient aiming at the same disease type, so as to obtain the frequency and/or the quantity of the same or similar medicines which are continuously and repeatedly taken by the specific patient in a set time period;

and sending out a reminder through the second manager (2) and the first manager (1) when the processor (4) obtains that the frequency and/or the number of the same or the same type of medicines continuously and repeatedly taken by the patient is larger than a set threshold value based on the AI analysis module.

6. The fitness evaluation and health management system of claim 5, wherein the processor (4) is configured to compare the medical record data with the symptom data to determine at least one suspected disease type, and the processor (4) is configured to perform an inquiry of a specific patient with reference to the determination method data corresponding to the prevalent disease data, wherein:

and under the condition that the disease type corresponding to the diagnosis data of the third patient is the same as the at least one suspected disease type, providing reference for the inquiry of the first patient based on the diagnosis method data of the third patient.

7. The fitness evaluation and health management system according to claim 6, further configured with at least one cloud platform (5) capable of data interaction with the processor (4), the cloud platform (5) being configured to perform the data interaction as follows:

in the case that the processor (4) performs a data transfer operation to transfer data to the cloud platform (5), the processor (4) generates a first permission request and a second permission request based on the data transfer operation, wherein the first permission request is transmitted to the first manager (1), and the second permission request is transmitted to the second manager (2);

the first manager (1) performs a first modification operation on the data needing to be transmitted by the processor (4) according to the first permission request, and the second manager (2) performs a second modification operation on the data needing to be transmitted by the processor (4) according to the second permission request, so that the private data in the data needing to be transmitted by the processor (4) can be partially deleted based on the first modification operation and the second modification operation.

8. The fitness evaluation and health management system according to claim 7, wherein the first manager (1) is capable of establishing a communication connection with a designated second manager (2) by initiating an access request to the designated second manager (2), or in case the first manager (1) sends an access request to the processor (4), the processor (4) is capable of sending the access request to the second manager (2) in an idle state, so that the first manager (1) is capable of establishing a communication connection with the second manager (2).

9. An intelligent monitoring system, characterized in that the intelligent monitoring system comprises at least:

a first manager (1) capable of acquiring identity data, facial image data, tongue image data and/or medical record data of a patient and storing the data in a data management library (3);

a processor (4) capable of obtaining a physical assessment result of a patient and formulating a health management strategy for the patient based on the physical assessment result, wherein the health management strategy at least comprises the therapeutic measure data for different diseases, the processor (4) is configured to:

10. The intelligent monitoring system according to claim 9, wherein the data management database (3) is at least capable of storing symptom data of a plurality of diseases and treatment data and diagnosis determination method data corresponding to the plurality of diseases, and the health management policy is configured to be adjusted according to the following steps: