Multi-mode pain early warning method for chronic pain

Abstract

The invention belongs to the field of chronic pain early warning, and particularly relates to a multi-mode pain early warning method for chronic pain. Multi-dimensional and multi-level patient information is used as a chronic disease diagnosis starting point, hospitals and home personal mobile devices of all layers are used as information collection media, an internet chronic disease cloud database is combined, chronic disease patient archive management is carried out, and AI auxiliary screening and health risk intelligent analysis and prediction are achieved. According to the invention, a novel chronic pain management mode is established by means of new-generation information technologies such as mobile internet, cloud computing and big data; meanwhile, self-management of the patient is assisted through a novel chronic pain management mode, and pre-judgment of chronic pain is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of chronic pain early warning, and particularly relates to a multi-modal pain early warning method for chronic pain. BACKGROUND

[0002] The causes of chronic diseases, especially chronic pain, are extremely complex, and it is difficult to determine the pain caused by the causes. In the current diagnosis and treatment situation of different departments, when chronic pain occurs, it is often necessary to go through multiple department examinations to obtain a certain answer. Chronic pain diseases are often accompanied by long-term anti-disease treatment, which prolongs the suffering of chronic pain. Therefore, after determining the inducing causes, it is necessary to determine whether the behaviors in life are related to the inducing causes of chronic pain through multi-modal data, so as to realize timely early warning and solve the problem of chronic pain from the root cause. SUMMARY

[0003] The purpose of the present application is to provide a diagnosis method and prediction system for recognizing part of chronic pain based on AI, to solve one or more technical problems existing in the prior art, and to at least provide a beneficial choice or create conditions.

[0004] A multi-modal pain early warning method for chronic pain, the method comprising the following steps: Obtaining diagnosis and treatment data of hospital chronic pain; Classifying the pain area according to the diagnosis and treatment data of chronic pain; classifying the pain type according to the pain condition; constructing a pain area and pain type classification matrix; Determining the pain classification through the pain type classification matrix, and determining the pain cause according to the pain classification through the cause.

[0005] Further, taking multi-dimensional and multi-level patient information as the starting point of chronic disease diagnosis, taking each level of hospital and personal mobile devices at home as information collection media, combining with the Internet chronic disease cloud database, managing the chronic disease patient files, realizing AI assisted screening, health risk intelligent analysis and prediction.

[0006] The system comprises a chronic disease database, a regional chronic disease support platform, a hospital chronic disease business subsystem, a community hospital chronic disease business subsystem, an offline chronic disease service center and an online home health service system, wherein the chronic disease database and the regional chronic disease support platform realize data intercommunication; The hospital chronic disease business subsystem, the community hospital chronic disease business subsystem and the online home health service system realize data intercommunication with the regional chronic disease support platform respectively; The hospital chronic disease business subsystem is controlled and managed by the regional center hospital; The community hospital chronic disease business subsystem is controlled and managed by the community health service center; The offline chronic disease service center and the online home health service system provide services for home patients.

[0007] Further, the regional chronic disease support platform is internally provided with a remote consultation module, a remote electrocardiogram module, a remote image module, a decision analysis module, an artificial intelligence AI engine, a two-way referral module and a data integration platform.

[0008] Further, the hospital chronic disease business subsystem is internally provided with an online prescription module, an online diagnosis and treatment module, a treatment plan module and a specialist follow-up module.

[0009] Further, the early warning module comprises a monitoring module, an alarm module and a data recording module, the monitoring module is connected with the community hospital chronic disease business subsystem, has the permission to read the patient's physical data, reads the patient's physical data and sets a data monitoring threshold, when the threshold is reached, transmits a warning signal to the alarm module, and the alarm module sends a warning to the patient, the alarm module is connected with the patient's mobile phone app, the patient's physical information is obtained through the monitoring module and arranged as visual data and transmitted to the mobile phone app, and the data recording module records and stores the patient's physical data obtained by the monitoring module.

[0010] Further, the method comprises the following steps: S1, establishing a chronic disease database: collecting basic patient information, coordinating data of various primary hospitals, completing data platform construction and computing platform construction through a big data center and a computing center, providing data services for a data warehouse, and providing computing power support for an algorithm model; S2, building a regional chronic disease support platform, and setting a remote consultation module, a remote electrocardiogram module, a remote image module, a decision analysis module, an artificial intelligence AI engine module, a two-way referral module, and a data integration platform in the regional chronic disease support platform; S3, establishing a hospital chronic disease business subsystem in a regional center hospital, integrating resources of medical units in the region, providing a new management mode for the medical units, establishing a chronic pain management plan including auxiliary diagnosis, treatment and follow-up, realizing data standardization of pain specialist medical centers and spatial vector conversion of pain specialist clinical phenotype data, quantifying the pain level of patients through the spatial vector conversion of pain specialist clinical phenotype data, and classifying the chronic pain of patients according to the pain level; S4, establishing a community hospital chronic disease business subsystem in a community health service center, collecting fragmented information of primary patients, and forming a chronic pain diagnosis case library for reference by general practitioners lacking technology and equipment in the community; S5, establishing an online home health service system, collecting information of chronic disease patients including personal information, personal medical cases and treatment history, and recording the information, and encrypting the collected patients to ensure the safety of patient information.

[0011] Further, other data of the patient is acquired, which includes an information acquisition module acquiring temperature, pulse, respiration, and blood pressure data of the face of the patient; An image acquisition module acquires a dynamic image sequence of the state of the face of the patient, and the dynamic image sequence of the state of the face of the patient corresponds to the chronic pain condition; An image processing module extracts a key frame static image from the dynamic image sequence, processes light brightness and a background, and extracts a key parameter for comparison with a large database; A detection and tracking module detects a face region from an original picture or a video sample for subsequent feature extraction and classification; A feature extraction module extracts a face state feature of the face state after preprocessing by using a corresponding feature extraction algorithm, and compares the face state with a patient condition in a database; A classification and recognition module uses a pain classification algorithm and vital signs to recognize an analysis and processing model obtained by the collected sample, and obtains a comprehensive analysis conclusion of a pain level.

[0012] The system comprises a data arrangement module configured to process medical data of a person to obtain medical feature data of the person, wherein the medical feature data comprises blood routine feature data and partial blood biochemical feature data; A data analysis module configured to process the medical feature data by using a preset analysis model to determine a disease prediction result of the person, wherein the preset analysis model at least comprises a gradient boosting decision tree model; and a data management module configured to manage data in the disease prediction system.

[0013] The patient can understand the development of the chronic disease by the chronic pain management mode, and the self-management ability is improved. Meanwhile, the medical institution can directly understand the examination result and the medication condition of the patient through the Internet, which not only helps the real-time communication between the medical staff and the patient, but also promotes the communication between the patients. BRIEF DESCRIPTION OF DRAWINGS

[0014] The above and other features of the present application will become more apparent from the following detailed description of the embodiments taken in conjunction with the accompanying drawings, in which like reference characters indicate the same or similar elements throughout the drawings, and in which: Figure 1 A flowchart of a multimodal pain early warning method for chronic pain is shown. DETAILED DESCRIPTION

[0015] The concept, specific structure and generated technical effects of the present application will be described clearly and completely in combination with the embodiments and the drawings below, so as to fully understand the purposes, schemes and effects of the present application. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0016] A multimodal pain warning method for chronic pain, the method comprising the steps of: Obtaining diagnosis and treatment data of chronic pain in a hospital; Classifying pain areas according to the diagnosis and treatment data of chronic pain; classifying pain types according to pain conditions; constructing a pain area and pain type classification matrix; Determining pain classification through the pain type classification matrix, and determining pain causes according to the pain classification, through the causes.

[0017] Further, taking multi-dimensional and multi-level patient information as the starting point of chronic disease diagnosis, using each level of hospital and personal mobile devices at home as information collection media, combining with the Internet chronic disease cloud database, managing chronic disease patient files, and realizing AI-assisted screening, intelligent analysis and prediction of health risks.

[0018] It comprises a chronic disease database, a regional chronic disease support platform, a hospital chronic disease business subsystem, a community hospital chronic disease business subsystem, an offline chronic disease service center and an online home health service system, wherein the chronic disease database and the regional chronic disease support platform realize data intercommunication; The hospital chronic disease business subsystem, the community hospital chronic disease business subsystem and the online home health service system realize data intercommunication with the regional chronic disease support platform, respectively; The hospital chronic disease business subsystem is controlled and managed by the regional center hospital; The community hospital chronic disease business subsystem is controlled and managed by the community health service center; The offline chronic disease service center and the online home health service system provide services for home patients.

[0019] Further, the regional chronic disease support platform is provided with a remote consultation module, a remote electrocardiogram module, a remote image module, a decision analysis module, an artificial intelligence AI engine, a two-way referral module and a data integration platform.

[0020] Further, the hospital chronic disease business subsystem is provided with an online prescription module, an online diagnosis and treatment module, a treatment plan module and a specialist follow-up module.

[0021] Further, the early warning module comprises a monitoring module, an alarm module and a data recording module, the monitoring module is connected with the chronic disease business subsystem of the community hospital, obtains the permission of reading the patient's physical data, reads the patient's physical data and sets the data monitoring threshold, transmits the early warning signal to the alarm module when the threshold is reached, and the alarm module sends an early warning to the patient, the alarm module is connected with the patient's mobile phone app, the patient's physical information is obtained through the monitoring module and arranged as visual data and transmitted to the mobile phone app, and the data recording module records and stores the patient's physical data obtained by the monitoring module.

[0022] Further, as shown in the embodiment, the method comprises the following steps: Figure 1 S1, establishing a chronic disease database: collecting basic patient information, coordinating data of various primary hospitals, completing data platform construction and computing platform construction through a big data center and a computing center, providing data services for a data warehouse, and providing computing power support for an algorithm model; S2, building a regional chronic disease support platform, setting a remote consultation module, a remote electrocardiogram module, a remote image module, a decision analysis module, an artificial intelligence AI engine module, a two-way referral module, and a data integration platform in the regional chronic disease support platform; S3, establishing a hospital chronic disease business subsystem in the regional center hospital, integrating resources of medical units in the region, providing a new management mode for the medical units, establishing a chronic pain management plan including auxiliary diagnosis, treatment and follow-up, realizing data standardization of pain specialist medical centers and spatial vector conversion of pain specialist clinical phenotype data, quantifying the pain level of patients through the spatial vector conversion of pain specialist clinical phenotype data, and classifying the chronic pain of patients according to the pain level; S4, establishing a community hospital chronic disease business subsystem in the community health service center, collecting fragmented information of primary patients, and forming a chronic pain diagnosis case library for reference by general practitioners lacking technology and equipment in the community; S5, establishing an online home health service system, collecting information of chronic disease patients including personal information, personal medical cases and treatment history, and recording the information, and encrypting the collected patients to ensure the safety of patient information.

[0023] Further, other data of the patient is obtained, which comprises an information acquisition module, which acquires the face temperature, pulse, respiration and blood pressure data of the patient; An image acquisition module acquires a dynamic image sequence of the patient's face state, and corresponds the dynamic image sequence of the patient's face state to the chronic pain condition; An image processing module acquires key frame static images according to the dynamic image sequence, processes light brightness and background at the same time, and compares the key parameters with the large database; A detection and tracking module detects a face region from an original picture or video sample for subsequent feature extraction and classification; A feature extraction module extracts face state features of the preprocessed face state using a corresponding feature extraction algorithm, and compares the face state with patient conditions in a database; A classification and recognition module uses a pain classification algorithm and vital signs to identify the analysis and processing model obtained by collecting samples, and obtains a comprehensive analysis conclusion of the pain level.

[0024] The system comprises a data arrangement module configured to process medical data of a person to obtain medical feature data of the person, wherein the medical feature data comprises blood routine feature data and partial blood biochemical feature data; A data analysis module configured to process the medical feature data by using a preset analysis model to determine a disease prediction result of the person, wherein the preset analysis model comprises at least a gradient boosting decision tree model; and a data management module configured to manage data in the disease prediction system.

[0025] The implementation path of the present application is as follows: 1) initially complete the construction of the basic platform: collect basic patient information, coordinate the data of various primary hospitals, and then complete the construction of the data platform and the computing platform through the big data center and the computing center, provide data services for the data warehouse, and provide computing power support for the algorithm model.

[0026] 2) Medium-term data modeling, realize the unified management of data, and complete the standardization of modeling and data management through standard Schema management; knowledge graph as a strong explanatory decision model provides services for the business layer; the algorithm model module is responsible for building various standardized algorithm models, which can provide various machine learning models such as classification, regression and optimization.

[0027] 3) Finally, design various business product modules, and build various AI applications through the data+model method.

[0028] In order to construct the chronic pain internet + management platform, the research content of the application mainly includes several aspects: patients: through the personal information, personal medical history, treatment history and other information of the patients, the online reservation, online consultation and auxiliary health management are provided; doctors: more chronic pain diagnosis cases are provided for general practitioners who lack technology and equipment, so that they can learn and refer, and more scattered information of grassroots patients can be collected through doctors; hospitals and related third parties: the resources of medical related units in the region are integrated, the new management mode of each unit is provided, the chronic pain management scheme including auxiliary diagnosis, treatment and follow-up is established, and the work to be carried out includes: 1) information management of hospital data; 2) data standardization of pain specialist multi-medical center; 3) spatial vector conversion of pain specialist clinical phenotype data; 4) matching database combined with patient whole phenotype clinical data, and giving personalized diagnosis and treatment; 5) establishing chronic pain patient archives, providing self-management strategy and chronic pain management follow-up support and the like.

[0029] Based on the above, the application further provides a working method of the chronic pain internet+ management platform, comprising the following steps: S101, resident health record establishment and target population chronic disease intelligent screening; based on the resident health record and chronic pain high-risk population screening standard, an internet of things collection system is established, the resident health information is improved, data support is provided for hierarchical diagnosis and treatment, high-risk condition corresponding questionnaire data, sample data set whether diagnosed as chronic pain are collected in the sample population, the resident health record is combined to form (resident health record, high-risk condition questionnaire, whether diagnosed as chronic pain) sample data; S102, internet analysis and diagnosis platform-regional medical information system sharing; by constructing an internet hierarchical diagnosis and treatment platform, the resident health record, electronic medical record and the like are interconnected, and regional medical information sharing and medical service collaboration are further realized; the hierarchical diagnosis and treatment platform is interconnected with the existing regional health information platform, medical institutions at all levels, regional medical centers and the like, the doctor can conveniently review the patient's past medical history and specific conditions of diagnosis and treatment by means of the platform, data support is provided for the implementation of hierarchical diagnosis and treatment; a standard knowledge system and service process, unified diagnosis and treatment standards and operation specifications are established, and the service quality of primary medical institutions is improved; by the support of new generation information technology, the original one-to-one service mode of large medical institutions and primary medical institutions is changed into a many-to-many service mode, so that the large medical institutions focus on solving difficult and complicated diseases and critical illness, the primary medical institutions focus on developing common diseases, chronic diseases, frequently-occurring diseases and rehabilitation services, and the hierarchical diagnosis and treatment mode of “acute and chronic separation, primary diagnosis in primary medical institutions, two-way referral, upper and lower collaboration” is constructed; S103, platform interconnection assists chronic pain diagnosis and treatment; on the basis of constructing the patient information platform and the internet diagnosis platform, intelligent diagnosis, appointment registration, appointment diagnosis and treatment, remote outpatient service, remote consultation, two-way referral / inspection, remote teaching, doctor-patient and doctor-doctor communication and the like are realized, and by means of the information platform, intelligent recommendation, process specification design and the like are used to assist the doctor in task management, task recommendation, automatic distribution and the like, and the standardization diagnosis and treatment ability of the primary doctor for chronic pain is improved; the primary doctor is assisted in accurate diagnosis and treatment; based on the patient's condition evaluation, the disease grouping to which the patient belongs is evaluated, the disease grade and treatment scheme are comprehensively evaluated in combination with the platform database information, the drug recommendation is given in combination with the hospital situation according to the suggestion given by the platform, and the data is submitted; the information of the patients of each primary medical institution including the health record, electronic medical record, clinical manifestation, disease grouping, drug recommendation, whether acute exacerbation, whether complication is established into a file, a systematic treatment diagnosis report is formed, an acute exacerbation risk model and a complication occurrence risk model based on the patient information and different drug schemes are constructed, and a drug scheme is given; in combination with the patient resident health record data, drug condition and drug effect accumulated in each region, in combination with the abnormal data outside the drug specification, the abnormal data is modeled, a hybrid model supporting difficult disease grouping and abnormal modeling model is established, and drug recommendation is accurately carried out.S104, chronic pain intelligent medication guidance and auxiliary follow-up is based on constructing the follow-up information containing the resident health record, clinical manifestation, medication, whether acute exacerbation or not, combining with the database information, giving the best medication scheme through cloud computing, giving the patient medication guidance at any time, and regularly assisting follow-up according to the cycle of different chronic pain diseases, and recording the patient information at the same time, uploading to the cloud, and giving reasonable follow-up plan suggestion.

[0030] Although the description of the present application has been quite detailed and particularly described for several described embodiments, it is not intended to be limited to any of these details or embodiments or any special embodiment, so as to effectively cover the intended scope of the present application. In addition, the present application is described above in the embodiments that the inventors can foresee, the purpose is to provide a useful description, and those non-essential modifications of the present application that have not been foreseen at present can still represent equivalent modifications of the present application.

Claims

1. A multimodal pain early warning method for chronic pain, characterized in that, The method includes the following steps: Obtain information on chronic pain symptoms through multi-dimensional pathways; Obtain patient symptom information and analyze the type of chronic pain in patients based on this information; Determine the pain classification and monitor patients according to the pain classification to provide early warning when chronic pain may occur.

2. The multimodal pain early warning method for chronic pain according to claim 1, characterized in that, Information on chronic pain symptoms is obtained through multiple pathways, including: chronic disease database, regional chronic disease support platform, hospital chronic disease business subsystem, community hospital chronic disease business subsystem, offline chronic disease service center and online home health service system; A disease database is constructed by acquiring chronic disease information through multimodal pathways. Patient disease information is obtained, and sensitive patient information is encrypted while acquiring the patient disease information. The patient disease information is combined with the disease database to classify the corresponding pain areas, determine the patient's pain type, and analyze the causes of the patient's pain type through the disease database.

3. The multimodal pain early warning method for chronic pain according to claim 1, characterized in that, The method for monitoring patients includes: patients need to upload specific physical data related to the causes of their pain type to the cloud diagnostic system at regular intervals every day; the system analyzes changes in the patient's physical data to determine chronic pain triggers; and when the changes in the patient's physical data reach the chronic pain trigger threshold, the system issues an early warning to the patient.

4. The multimodal pain early warning method for chronic pain according to claim 1, characterized in that, By regularly measuring and uploading specific physical data related to the patient's pain type to the cloud diagnostic system, the system generates a fluctuation chart of the patient's physical data. By comparing the fluctuation chart with the patient's previous chronic pain, the specific physical data that triggered the patient's illness is determined. The system then guides the patient to control the physical data through medication or exercise therapy. When the set value is reached, the system observes whether the patient still experiences chronic pain. If chronic pain still occurs, the corresponding data is controlled.

5. The multimodal pain early warning method for chronic pain according to claim 1, characterized in that, The community hospital chronic disease business subsystem includes an electronic record creation module, a treatment plan execution module, an outpatient referral module, and a chronic disease management module.

6. The multimodal pain early warning method for chronic pain according to claim 1, characterized in that, The early warning module includes a monitoring module, an alarm module, and a data recording model. The monitoring module is connected to the chronic disease management subsystem of the community hospital, obtains permission to read patient body data, reads the patient body data and sets data monitoring thresholds. When the threshold is reached, an early warning signal is transmitted to the alarm module, which issues an early warning to the patient. The alarm module is connected to the patient's mobile app, obtains the patient's body information through the monitoring module, organizes it into visual data, and transmits it to the mobile app. The data recording module records and stores the patient body data obtained from the monitoring module.

7. The multimodal pain early warning method for chronic pain according to claim 1, characterized in that, The steps include: S1. Establishing a chronic disease database: collecting patient information at the grassroots level, coordinating data from various grassroots hospitals, and completing the construction of a data platform and computing power platform through a big data center and computing center to provide data services for the data warehouse and computing power support for algorithm models; S2. Build a regional chronic disease support platform, and set up a remote consultation module, a remote electrocardiogram module, a remote imaging module, a decision analysis module, an artificial intelligence AI engine module, a two-way referral module, and a data integration platform within the regional chronic disease support platform; S3. Establish a chronic disease business subsystem in the regional central hospital, integrate the resources of medical units in the region, provide medical units with a new management model, establish a chronic pain management solution that includes auxiliary diagnosis, treatment and follow-up, realize the standardization of data from multiple medical centers in pain specialty and the spatial vector transformation of clinical phenotype data in pain specialty, quantify the patient's pain level through the spatial vector transformation of clinical phenotype data in pain specialty, and classify the patient's chronic pain according to the patient's pain level; S4. Establish a community hospital chronic disease business subsystem in community health service centers to collect fragmented information from primary care patients and form a chronic pain diagnosis case database for reference by general practitioners in the community who lack the technology and equipment. S5. Establish an online home health service system to collect and record information on patients with chronic diseases, including personal information, medical records, and treatment history. At the same time, the collected patient information will be encrypted to ensure patient information security.

8. The multimodal pain early warning method for chronic pain according to claim 1, characterized in that, Acquire other patient data: This includes an information acquisition module that acquires data on the patient's facial temperature, pulse, respiration, and blood pressure. The image acquisition module includes a dynamic image sequence of the patient's facial state, and the dynamic image sequence of the patient's facial state is correlated with chronic pain symptoms. The image processing module captures key frame static images from dynamic image sequences, processes light intensity and background, and captures key parameters for comparison with a large database. The detection and tracking module detects facial regions from raw image or video samples for subsequent feature extraction and classification. The feature extraction module uses the corresponding feature extraction algorithm to extract facial features from the preprocessed facial state and compares the facial state with the patient's symptoms in the database. The classification and recognition module uses pain classification algorithms and vital signs to identify the analysis and processing model obtained from the collected samples and draw a comprehensive analysis conclusion on the pain level. The system includes: a data processing module, used to process the acquired medical data of the personnel to obtain the medical characteristic data of the personnel, wherein the medical characteristic data includes routine blood test characteristic data and some blood biochemical characteristic data; The data analysis module is used to process the medical feature data through a preset analysis model to determine the disease prediction results of the person, wherein the preset analysis model includes at least a gradient boosting decision tree model; and the data management module is used to manage the data in the disease prediction system.

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