AI technology-based whole course rehabilitation management system and management method

Abstract

The invention discloses a whole course rehabilitation management system and management method based on an AI technology, and relates to the technical field of medical health management. The system mainly comprises a medical record file module, a rehabilitation management module and a doctor-patient communication module. Personal medical records of a patient are stored and managed through the structured information storage technology, free interaction between the system and a doctor-patient terminal is achieved through the doctor-patient communication module, a channel for timely consultation and feedback is provided for the patient, and doctor-patient communication is promoted. Rehabilitation schemes of different disease course stages are provided for each patient through the rehabilitation management module, a rehabilitation file of the next stage is adjusted according to execution data of the patient in each stage, and the whole disease course of the patient is throughout until complete rehabilitation is achieved. Personalized whole course rehabilitation management for different patients is realized, and the overall efficiency and the service quality of medical services are improved.

Description

Technical Field

[0001] The present invention relates to the field of medical health management technology, and in particular to a full-course rehabilitation management system and management method based on AI technology. Background Art

[0002] The chronic disease management system in the existing related technologies focuses on the in-hospital treatment stage, lacking systematicity, continuity, and integrity, while relatively neglecting the period after discharge and at home. However, the time for patients with chronic diseases and serious illnesses to recover at home is longer than that for hospitalization. During this period, due to the lack of convenient communication channels and mechanisms, patients often cannot maintain effective communication with the medical team, and it is difficult for patients to promptly feedback key information to doctors, including symptom changes, medication reactions, side effects, and living habits. This information asymmetry lacks systematicity, continuity, and integrity, and cannot adjust and intervene in rehabilitation plans in a timely manner, delaying recovery from illness, and the patient's personal unscientific living habits and behaviors will affect the treatment and rehabilitation effects.

[0003] Taking tumors as an example, the current disease management system has the following defects: 1. Lack of medical data monitoring after discharge and at home, resulting in patients being unable to obtain comprehensive and continuous rehabilitation guidance; 2. Many APPs have many deficiencies in "patient education content", such as: patient education content is relatively superficial, and cannot deeply explain the disease mechanism, treatment plan and its side effects, etc., making it difficult for patients to fully understand their condition and treatment plan, and not pay enough attention to their condition; 3. The specific conditions of patients vary, and many patient education contents cannot be personalized according to the patient's condition, treatment progress and individual needs, and lack pertinence; 4. Some APPs lack interactivity, and patients can only passively receive information and cannot ask questions or get personalized answers; 5. Most management systems can only provide template rehabilitation suggestions, and do not provide personalized and executable plans for patients' symptoms, medication, nutrition, exercise, functional rehabilitation, psychology, etc.; 6. Existing patient management methods mainly rely on doctors' manual judgment and processing, resulting in low efficiency in problem response and processing, and increasing the burden on doctors.

[0004] Cancer treatment is a continuous and complex process, with side effects from surgery, radiotherapy, chemotherapy, and various complications that vary from person to person. Therefore, home rehabilitation support after discharge is essential. Summary of the invention

[0005] The present invention provides a full-course rehabilitation management system and method based on AI technology to solve the problems that the existing disease management system lacks rehabilitation intervention management during home stay and the rehabilitation program cannot adapt to the personalized conditions of different patients.

[0006] The present invention is achieved through the following technical solutions:

[0007] The first aspect of the present invention provides a full-course rehabilitation management system based on AI technology, comprising:

[0008] A medical record module is used to store and manage the patient's medical records in structured data, wherein the medical records include the patient's basic information, disease information, treatment information, and examination information;

[0009] A rehabilitation management module, which is used to generate an assessment scale based on the medical record file, send the assessment scale to the patient to fill in, and generate a rehabilitation plan after comprehensive evaluation based on the filled-in result and the medical record file information;

[0010] The rehabilitation management module is also used to send the rehabilitation plan to medical staff for review, send the reviewed rehabilitation plan to the patient for execution, receive the patient's execution data, generate the next stage of rehabilitation plan according to the execution data, and repeat this process throughout the patient's entire course of illness;

[0011] The doctor-patient communication module is used for information interaction between the patient side and the rehabilitation management module, information interaction between the medical staff side and the rehabilitation management module, and information interaction between the patient side and the medical staff side.

[0012] The full-course rehabilitation management system based on AI technology proposed in the present invention stores and manages the patient's personal medical records through information storage technology, covering comprehensive information. The structured storage and management method facilitates the retrieval and update of target data. The doctor-patient communication module realizes free interaction between the system and the doctor-patient end, provides patients with timely consultation and feedback channels, and promotes doctor-patient communication. The rehabilitation management module relies on AI technology to generate rehabilitation plans for each patient at different stages of the disease, adjusts the rehabilitation files for the next stage based on the execution data of the patient at each stage until complete recovery, and realizes full-course rehabilitation management for individual patients. Each patient obtains targeted rehabilitation suggestions based on his or her medical records and assessment scales to achieve personalized rehabilitation management. The main rehabilitation suggestions are automatically generated by AI, and medical staff review and check them to improve the efficiency of medical services and ensure the quality of medical care.

[0013] In some embodiments, the rehabilitation management module includes a sports rehabilitation module, a functional rehabilitation module, a nutritional rehabilitation module, and a psychological rehabilitation module;

[0014] The exercise rehabilitation module is used to evaluate the patient's physical condition according to the Borg dyspnea scoring scale combined with the patient's medical records, and recommend an exercise rehabilitation program based on the evaluation results;

[0015] The functional rehabilitation module is used to evaluate the patient's physical function status and rehabilitation needs based on the mMRC scale and stair climbing test combined with the patient's medical records, and recommend a functional rehabilitation plan based on the evaluation results;

[0016] The nutritional rehabilitation module is used to evaluate the nutritional status of the patient based on the nutritional risk screening scale combined with the patient's medical records, and recommend a nutritional rehabilitation plan based on the evaluation results;

[0017] The psychological rehabilitation module is used to evaluate the patient's psychological state according to the psychological assessment scale and recommend a psychological rehabilitation program based on the assessment results.

[0018] In some embodiments, the rehabilitation management module also includes a monitoring module for obtaining the patient's physical data through a wearable device and sending early warning notifications to the patient and medical staff according to changes in the physical data.

[0019] In some embodiments, the rehabilitation management module further includes a medication management module, a symptom management module, and a follow-up management module;

[0020] The medication management module is used to provide medication time reminders and medication guidance according to the patient's condition and current medication status, as well as receive adverse reactions reported by the patient after medication, and associate the adverse reactions with the corresponding drugs;

[0021] The symptom management module is used to generate a symptom scale according to the patient's disease type and disease stage, send the symptom scale to the patient for filling in, and provide intervention guidance or early warning according to the symptom scale filled in by the patient;

[0022] The follow-up management module is used to generate a follow-up plan according to the patient's condition, and the follow-up plan includes tracking follow-up and examination follow-up. In some embodiments, the rehabilitation management module sends the assessment scale, medication instructions, symptom scale or follow-up plan to the patient for review before sending it to the medical staff, and then sends it to the patient after review or modification by the medical staff.

[0023] In some embodiments, the system further includes an intelligent patient education module, which recommends patient education content based on the patient's medical records, and the patient education content includes interactive materials in the form of text, pictures or videos.

[0024] In some embodiments, the intelligent patient education module includes a content management module and a patient education content recommendation module;

[0025] The content management module is used to store, classify, mark and update patient education content;

[0026] The patient education content recommendation module is used to obtain patient label vectors and patient education content label vectors, match and sort the patient education contents according to the patient education content label vectors and the patient label vectors, and output the sorting results.

[0027] In some embodiments, the system further includes an early warning intervention module, which is used to determine whether the patient's indicators are abnormal based on the data fed back by the patient; if abnormal, an early warning is issued for the abnormal indicators, and intervention measures are generated and sent to the patient and medical staff.

[0028] In some embodiments, the system also includes a rehabilitation report generation management module, which periodically generates a phased rehabilitation report based on the data of the rehabilitation management module. The phased rehabilitation report can be queried by medical staff and patients. The rehabilitation stage report includes the patient's symptom changes, adverse drug reactions, indicator changes, follow-up status, functional rehabilitation status, psychological rehabilitation status, early warning status, and rehabilitation plan implementation status over a period of time.

[0029] A second aspect of the present invention provides a full-course rehabilitation management method based on AI technology, which is applied to the full-course rehabilitation management system based on AI technology described in any embodiment of the first aspect of the present invention, and the method comprises:

[0030] Establish the patient's medical record based on the patient's basic information, disease information, treatment information and examination information;

[0031] Generate an assessment scale based on the medical record file, send the assessment scale to the patient to fill in, and generate a rehabilitation plan after comprehensive evaluation based on the filled-in result and the medical record file information;

[0032] The rehabilitation program is sent to medical staff for review, the reviewed rehabilitation program is sent to the patient for execution, and the patient's execution data is received. The rehabilitation program for the next stage is generated based on the execution data, and this process is repeated throughout the patient's entire illness.

[0033] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0034] Through the comprehensive application of artificial intelligence technology and innovative management models, the continuity, integrity and personalization of disease treatment have been improved, and the overall efficiency and quality of medical services have been improved, providing patients with a comprehensive, efficient and intelligent full-course disease management solution.

[0035] Through multi-faceted rehabilitation plans involving exercise, function, nutrition, and psychology, we provide patients with comprehensive assessments, personalized program development, and supervision and implementation to promote their overall recovery and improve their quality of life.

[0036] Improve the efficiency of doctor-patient communication: Through the doctor-patient instant messaging module, patients and doctors can achieve two-way seamless communication, promptly resolve questions and problems during the treatment process, and enhance patient confidence and satisfaction.

[0037] The medication management module tracks patients' medication use, provides personalized medication guidance and warnings of adverse drug reactions, and ensures medication safety and treatment effectiveness; the follow-up management module improves the efficiency and quality of follow-up work through automated follow-up planning and execution monitoring, ensuring continuous care for patients during their recovery.

[0038] The intelligent patient education module provides personalized educational content based on the patient's specific situation, helping patients better understand their condition, treatment plan and self-management.

[0039] The early warning intervention module tracks changes in patients’ symptoms in real time, notifies doctors and patients promptly, and ensures rapid response and necessary intervention measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:

[0041] Figure 1 This is a structural block diagram of a full-course rehabilitation management system based on AI technology in an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of a process for establishing an AI intelligent question-answering module according to an embodiment of the present invention;

[0043] Figure 3 is a schematic diagram of a question-answering process of an intelligent question-answering module according to an embodiment of the present invention;

[0044] Figure 4 It is a schematic diagram of a process of establishing a patient medical record file according to an embodiment of the present invention;

[0045] Figure 5 It is a schematic diagram of a process of generating a personalized rehabilitation program according to an embodiment of the present invention;

[0046] Figure 6 It is a schematic diagram of a process of establishing a patient recommendation library according to an embodiment of the present invention;

[0047] Figure 7 It is a schematic diagram of a process of establishing a patient education content recommendation library according to an embodiment of the present invention;

[0048] Figure 8 It is a schematic diagram of a process of personalized recommendation of patient education content according to an embodiment of the present invention;

[0049] Fig. 9It is a schematic diagram of a process of establishing a warning trigger rule according to an embodiment of the present invention;

[0050] Fig.10 It is a schematic diagram of a process of intelligent early warning intervention according to an embodiment of the present invention;

[0051] Fig.11 It is a schematic diagram of a process of generating an intelligent rehabilitation report according to an embodiment of the present invention;

[0052] Fig.12 It is a flowchart of a full-course rehabilitation management method based on AI technology in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0054] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to or inherent to other steps or units of the device.

[0055] The terms used in the various embodiments of the application are only used to describe the purpose of specific embodiments and are not intended to limit the various embodiments of the application. As used herein, the singular form is intended to also include the plural form, unless the context clearly indicates otherwise. Unless otherwise limited, all terms used here (including technical terms and scientific terms) have the same meaning as the meanings commonly understood by ordinary technicians in the field of the various embodiments of the application. The terms (such as the terms defined in the dictionary generally used) will be interpreted as having the same meaning as the contextual meaning in the relevant technical field and will not be interpreted as having an idealized meaning or an overly formal meaning, unless clearly defined in the various embodiments of the application.

[0056] The embodiments of the present invention provide a full-course rehabilitation management system and management method based on AI technology, which is suitable for the full-course rehabilitation management of patients with chronic diseases, tumors and other serious diseases from diagnosis to rehabilitation, especially the rehabilitation management during home treatment after discharge from hospital, improving the continuity and integrity of treatment, providing patients with personalized rehabilitation plans, timely adjusting and intervening in treatment plans, which is conducive to the early recovery of patients and improving the overall efficiency and quality of medical services.

[0057] The AI-based full-course rehabilitation management system of the embodiment of the present invention is deployed in the cloud or on the server of the hospital, and is connected to various terminal devices through the internal network of the hospital. Patients can access the system through their personal terminal devices or hospital terminal devices to perform operations such as symptom reporting and medication records. Doctors can perform operations such as symptom identification, early warning, and treatment plan formulation through personal terminals or terminal devices provided by the hospital.

[0058] like Figure 1 As shown, Figure 1 It is a structural block diagram of a full-course rehabilitation management system based on AI technology in an embodiment of the present invention, including a medical record module, a rehabilitation management module and a doctor-patient communication module.

[0059] The medical record module is used to store and manage patients' medical records in the form of structured data. The medical records include the patient's basic information, disease information, treatment information, and examination information.

[0060] The rehabilitation management module is used to generate an assessment scale based on the patient's medical records, send the assessment scale to the patient to fill in, and generate a rehabilitation plan based on the comprehensive evaluation of the filled-in results and the medical record information;

[0061] In addition, the rehabilitation plan is sent to medical staff for review, the reviewed rehabilitation plan is sent to the patient for execution, and the patient's execution data is received. The next stage of rehabilitation plan is generated based on the execution data, and this process is repeated throughout the patient's entire illness until the patient is fully recovered.

[0062] The doctor-patient communication module is used for information interaction between patients and the rehabilitation management module, information interaction between medical staff and the rehabilitation management module, and information interaction between patients and medical staff.

[0063] Among them, the patient communication module is the core component of the rehabilitation system, which aims to promote effective communication and interaction between medical staff and patients. The module provides a secure and private instant messaging platform, enabling patients to communicate with doctors or medical teams in real time by text, voice or video. Patients can download the patient client of the medical communication through personal terminal devices, and interact with other modules of the system through the medical communication module in the server. Medical staff can download the medical client of the medical communication through hospital terminals or personal terminals, and interact with the medical module and other modules in the server. When patients are in the hospital, they can also use the patient client of the hospital terminal. In order to facilitate the patient's home rehabilitation management, the system provides an open patient client for personal download. The patient client and the medical client can communicate based on the medical communication module of the server.

[0064] The doctor-patient communication module includes an AI intelligent question-and-answer module and a one-to-one consultation module. Either the medical staff or the patient can initiate a one-to-one consultation. For some routine consultations initiated by patients, such as "How to upload a medical record report?", "What is the phone number of the medical team?", "What should I pay attention to after the operation?", the doctor-patient communication module uses "AI intelligent question-and-answer" to automatically reply to improve the efficiency and timeliness of the reply. The platform supports sending text messages, pictures, videos and voice messages, and sends text and image information such as patient education content, rehabilitation plans, intervention plans, follow-up forms, symptom forms, etc. through the message window of the doctor-patient communication module.

[0065] The Medical Communication Module includes a typesetting management module and an online clinic module. Medical staff can log in to the scheduling management module of the Medical Communication Module to schedule shifts. The scheduling is managed by department, expert, and clinic category. After the scheduling is released, patients can register, have online video consultations, and make offline consultation appointments through the online clinic module of the Medical Communication Module. Using video conferencing technology, patients can receive remote outpatient services from experts at home. This not only reduces the inconvenience of patients going to the hospital, but also enables doctors to intuitively understand the patient's physical condition through video.

[0066] The doctor-patient communication module also includes a multidisciplinary expert consultation module. For complex or rare tumor cases, the doctor-patient communication module allows patients to make an appointment for a multidisciplinary expert consultation (Multi-disciplinary Treatment, MDT). MDT is composed of experts in multiple related fields such as tumor surgery, oncology, radiology, pathology, nutrition, psychology, etc., who jointly discuss and formulate the best rehabilitation treatment plan.

[0067] Combination Figure 2 , Figure 3 As shown, an implementation example of "AI Intelligent Question and Answer" is described as follows: Figure 2 This is a schematic diagram of the AI ​​intelligent question-answering module establishment process, including steps S1-1 to S1-3. Figure 3 It is a schematic diagram of the question-answering process of the AI ​​intelligent question-answering module, including steps S2-2 to S2-3.

[0068] Step S1-1, prepare the “question / answer” dataset.

[0069] Question and answer data can be collected from previous medical records and existing medical databases, and categorized and organized according to general questions and specific disease questions.

[0070] Step S1-2: Use the Text Embedding model to perform vector operations on the question.

[0071] The Text Embedding model is a text vector extraction model that converts words into binary or high-dimensional real vectors and converts sentences and documents into numerical vectors.

[0072] Step S1-3, save the question vector and the corresponding answer to the milvus vector database.

[0073] Milvus is a cloud-native vector database for storing, indexing, and managing large-scale embedding vectors generated by deep neural networks and other machine learning models. It is highly available, high-performance, and easy to expand, and is used for real-time recall of massive vector data. Other vector databases can also be used, such as Elasticsearch, Faiss, Weaviate, etc.

[0074] When the doctor-patient communication module receives a text consultation question sent by the patient through the dialogue window, it first identifies the consultation type. If it is identified as a routine consultation, it will be imported into the AI ​​intelligent question and answer module to perform the AI ​​question and answer process as steps S2-1 to S2-3. If it is identified as an unconventional consultation, or the user feedback AI answer does not solve the problem, it will be forwarded to the one-to-one consultation module for manual processing by the back-end medical staff.

[0075] When the consultation question is of a general type, the AI ​​intelligent question and answer module processes it according to the following process to complete the question and answer.

[0076] S2-1, use the Text Embedding model to perform vector operations on the question to obtain question vector data.

[0077] S2-2, use the vector engine to search in the vector database and match the answer corresponding to the question vector data.

[0078] S2-3, return the answer to the dialogue window.

[0079] The medical record archive module is mainly used to obtain the patient's basic information, disease information, treatment information and examination information. Artificial intelligence technology is used to realize the automatic structured storage and management of medical record archive data to ensure the accuracy, completeness and traceability of the information. The medical record archive module supports patients to build their own archives. Patients or medical staff upload photos or PDF files of examination, inspection and discharge records to the medical record archive module. The medical record archive module performs structured management of hospital documents such as examination reports and medical records.

[0080] The system uses optical character recognition (OCR) technology and natural language process (NLP) information extraction technology to generate structured standard data from uploaded files and store them in the medical record database. The medical record database is the source of information for rehabilitation management decisions and plays a vital role in the full course rehabilitation management system. The medical record database includes the patient's personal medical records, including the patient's basic information, disease information, treatment information, and examination information.

[0081] The basic information of patients includes personal information such as the patient's name, gender, age, ethnicity, blood type, sports preference, contact information, home address, occupation, etc., to ensure that each patient can be uniquely identified through differentiated personal information. Disease information includes diagnostic information, such as comorbidities, complications, classification, targets, disease progression, tumor progression, etc., personal medical history, family medical history, etc. Diagnostic information describes the doctor's preliminary judgment or final diagnosis of the patient's condition, including the name of the disease, description of the condition, basis for diagnosis, etc. Treatment information includes the name of the operation, the time of the operation, adjuvant treatment methods, history of radiotherapy and chemotherapy, surgical history, medication information, etc. Medication information records the drug treatment plan received by the patient, including information such as drug name, dosage, frequency of medication, and duration of medication. Examination information covers various examination items received by the patient, such as blood tests, urine tests, imaging examinations (X-rays, CT, MRI, etc.), electrocardiograms, pathological examinations, etc. Each examination will generate a specific report, including the name of the examination, examination results, examination time, examination location, etc.

[0082] The medical record module also automatically generates intelligent diagnosis and treatment logs based on examination information, personal information and indicator data, and provides them to doctors and patients for review, so that they can quickly guide the patient's condition. Indicator data is formulated based on medical standardized data such as medical indicators and test indicators, or directly uses standardized data.

[0083] The medical record module uses OCR recognition technology and NLP information extraction technology to create a patient medical record file. Figure 4 As shown, Figure 4 It is a flowchart of establishing a patient medical record, which includes the following steps.

[0084] S3-1, convert the incoming medical record file into image data in base64 format.

[0085] S3-2, call PaddleOCR to recognize the text box in the image data.

[0086] S3-3, sorting the text box recognition results according to the x-coordinate of the upper left corner point of each text box.

[0087] S3-4, calculate the center point coordinates and slope of each text box.

[0088] S3-5, take the N longest text boxes and calculate the average slope.

[0089] S3-6, start detecting from the center point of the leftmost text box in each row. If the average slope passes through another text box, the two text boxes are considered to belong to the same row.

[0090] S3-7, recording the minimum y-axis distance and direction of each row between the row above and the row below (to facilitate the handling of the direction problem of merging rows when displaying multiple rows).

[0091] S3-8, using the large model, classify the OCR recognition results according to the prompt.

[0092] S3-9, obtain the predefined field results in the database and use the embedding model to perform semantic vector operations.

[0093] S3-10, use the cosine similarity algorithm to calculate the matching degree between the text box and the indicators predefined in the database, and if the similarity is greater than the specified threshold, it is considered a match.

[0094] S3-11. Calculate the subclass to be tested based on the total similarity of the match, provide the matching row to the big model, and extract multi-round dialogue data based on the prompt.

[0095] S3-12. Concatenate all JSON information obtained from multiple rounds of conversations according to the specified structure and return the result.

[0096] The original medical records of patients are mostly chart data, so OCR recognition is used to quickly extract relevant information and compare it with the predefined fields in the database. The predefined fields can be the basic information, disease information, treatment information, and examination information of the patients listed above. After extraction, it is converted into structured json data for storage. The patient's medical records can be updated and modified by medical staff or the patient himself.

[0097] In some embodiments, before sending the assessment scale to the patient, the rehabilitation management module first sends it to the medical staff for review, and then sends it to the patient after the medical staff reviews or modifies it.

[0098] In some embodiments, in combination Figure 1 As shown in the figure, the rehabilitation management module includes sports rehabilitation module, functional rehabilitation module, nutritional rehabilitation module and psychological rehabilitation module. It provides patients with personalized rehabilitation plans, covering rehabilitation suggestions in multiple aspects such as sports, function, nutrition and psychology.

[0099] The exercise rehabilitation module is used to assess the patient's physical condition based on the Borg dyspnea rating scale combined with the patient's medical records, and recommend an exercise rehabilitation plan based on the assessment results.

[0100] The exercise rehabilitation program provides patients with personalized exercise plans and video guidance to help them restore their physical fitness and enhance their immunity through appropriate exercise. Specifically, the Borg dyspnea scoring scale is used to conduct a comprehensive individualized assessment based on the scale results, patient age, surgery time, physical fitness level, disease status and other information. Based on the evaluation results, personalized exercise prescriptions are intelligently recommended, including aerobic exercise, anaerobic exercise, balance exercise and flexible exercise. The exercise prescription contains detailed information such as exercise mode, exercise intensity, exercise frequency and exercise duration to ensure that patients can exercise safely and effectively. According to the patient's recovery progress and feedback, the exercise prescription is intelligently adjusted to ensure that the exercise plan always meets the patient's current health status. In addition, patients can use wearable devices or mobile applications to monitor the patient's exercise status, including key indicators such as heart rate, exercise intensity, and number of steps, and upload the monitoring data to the exercise rehabilitation module. When the patient's exercise indicators are abnormal or there is a risk, the system will automatically issue an early warning to the patient and medical staff for timely intervention.

[0101] The functional rehabilitation module is used to evaluate the patient's physical function status and rehabilitation needs based on the mMRC scale and stair climbing test combined with the patient's medical records, and recommend a functional rehabilitation plan based on the evaluation results.

[0102] Functional rehabilitation programs include rehabilitation training for specific parts of the body to help patients restore functions in daily life, such as limb function training, language training, etc. The functional rehabilitation module uses a variety of assessment tools, such as the dyspnea (mMRC) scale and stair climbing test, to comprehensively assess the patient's physical function status and rehabilitation needs. Based on the patient's assessment results, the module's built-in intelligent algorithm will recommend suitable personalized rehabilitation programs, which are designed to improve the patient's respiratory function and overall physical fitness. The rehabilitation program includes a series of customized rehabilitation exercises, such as effective coughing, pursed lip breathing, patting the back to cough up phlegm, blowing balloons, abdominal breathing, chest expansion exercises, and home oxygen therapy. Each rehabilitation program comes with detailed implementation instructions, including frequency (number of times per day or week), number of sets, number of sets, or single duration, to ensure that patients can perform rehabilitation exercises correctly and effectively.

[0103] The nutritional rehabilitation module is used to assess the patient's nutritional status based on the nutritional risk screening scale and the patient's medical records, and recommend a nutritional rehabilitation plan based on the assessment results.

[0104] The nutritional rehabilitation program provides dietary advice and recipes to help patients take in enough nutrition through a reasonable diet and enhance physical strength and immunity. The nutritional rehabilitation module uses the industry-recognized Nutrition Risk Screening 2002 scale as the initial screening tool. Based on the screening results, combined with detailed information such as the patient's height, weight, labor intensity, and combined metabolic diseases, an in-depth personalized nutritional assessment is conducted. Based on the assessment results, a personalized dietary prescription is intelligently generated to recommend foods and nutritional supplements suitable for the patient's specific health conditions. Clear short-term and long-term nutritional goals are set for patients, including ideal weight, nutritional intake goals, and specific indicators for improving nutritional status. Customized dietary plans are provided, including the daily intake of nutrients such as calories, protein, fat, carbohydrates, vitamins, and minerals. Regularly monitor the patient's nutritional status and dietary intake, and adjust the dietary prescription according to the patient's condition changes and feedback. Provide patients with personalized nutritional guidance and advice.

[0105] The psychological rehabilitation module is used to assess the patient's psychological state according to the psychological assessment scale and recommend a psychological rehabilitation plan based on the assessment results.

[0106] The psychological rehabilitation program provides psychological counseling and relaxation training to help patients cope with psychological stress and emotional fluctuations during treatment. The psychological rehabilitation module uses the GAD-7 Generalized Anxiety Scale and the PHQ-9 Simple Self-Rating Depression Scale to assess the patient's psychological state. Based on the assessment results, it intelligently recommends personalized psychological rehabilitation programs, including sleep improvement, relaxation training, mindfulness training, psychological counseling and other methods. The program includes information such as the introduction of each psychological rehabilitation method, the use of media (such as video, audio, text guidance), the recommended frequency and duration, etc. If the patient's scale score reaches the standard or triggers certain risk issues, the system will automatically send an early warning to the doctor and the patient to avoid the occurrence of risk events.

[0107] like Figure 5 Shown is a flowchart of the generation of personalized rehabilitation programs. The four major rehabilitations are combined with standardized clinical assessment scales and online and offline consultations and tests, intelligent assistance + manual assessment of the functional integrity of the patient's lesions, movement risks, nutritional status, sleep status, health level, daily living ability, and current physical conditions. The system automatically generates phased plans; each plan includes: patient category, goal, assessment, implementation plan, guidance and suggestions, etc. The process of generating personalized rehabilitation programs specifically includes the following steps.

[0108] S4-1, generate and send the assessment scale to the patient.

[0109] S4-2, receive the assessment form filled out by the patient.

[0110] S4-3, generate sports rehabilitation plan, functional rehabilitation plan, nutritional rehabilitation plan and psychological method plan based on the filled-in information.

[0111] S4-4, send each rehabilitation plan to the patient and urge the patient to implement it.

[0112] S4-5, receiving the execution data fed back by the patient;

[0113] S4-6, generating a next stage rehabilitation plan based on the execution data.

[0114] In some embodiments, before sending the exercise rehabilitation plan, functional rehabilitation plan, nutritional rehabilitation plan or psychological rehabilitation plan to the patient, the rehabilitation management module first sends it to medical staff for review, and then sends it to the patient after review or modification by the medical staff.

[0115] In addition, the system also provides a check-in module to urge patients to implement various rehabilitation plans, collect patients' implementation data, monitor patients' compliance with rehabilitation instructions in real time, and issue early warnings and intervene in treatment based on implementation. The rehabilitation process data of the four major rehabilitation modules will be recorded in detail, and the system will generate an evaluation report based on the set rehabilitation cycle, and then formulate a personalized plan based on the evaluation report, repeating the above steps until a certain level of rehabilitation is achieved.

[0116] In some embodiments, the rehabilitation management module also includes a monitoring module for obtaining the patient's physical data, such as heart rate, exercise intensity, number of steps, etc., through wearable devices or mobile applications, and sending early warning notifications to patients and medical staff based on changes in physical data.

[0117] In some embodiments, see Figure 1 As shown, the rehabilitation management module also includes a medication management module, a symptom management module and a follow-up management module.

[0118] The medication management module is used to provide medication time reminders and medication guidance based on the patient's condition and current medication status, as well as receive adverse reactions reported by patients after medication, and associate the adverse reactions with corresponding drugs.

[0119] According to the patient's medical records and the filling of various scales, the current condition is judged, and the current medication is increased or decreased, and medication guidance is provided, including: contraindications, indications, usage and dosage, incompatibility, drug interactions, adverse reactions, doctor's advice, etc. Medication time reminders are set to remind patients to take medication on time. Patients can report adverse reactions according to the actual situation of medication, and the system will associate adverse reactions with the drugs used. According to the continuous adverse reactions, the system records the complete process of adverse reactions from severe to mild, and even disappearance during medication in real time.

[0120] The symptom management module is used to generate a symptom scale based on the patient's disease type and stage of illness, send the symptom scale to the patient for completion, and provide intervention guidance or early warning based on the symptom scale filled out by the patient.

[0121] The symptom management module mainly monitors high-frequency postoperative symptoms such as cough, pain, fatigue, shortness of breath, nausea, and vomiting. It also covers possible symptoms caused by patients during radiotherapy and chemotherapy, taking targeted drugs, and other diseases. The symptom management module combines the patient's disease type, disease stage, etc., and the AI ​​model automatically generates a symptom scale, and reminds patients to report symptoms every day or every week. After the patient fills in the scale, the system will continuously record and dynamically generate the complete process of symptom changes, detect early warnings, and provide intervention guidance to help doctors improve the timeliness and effectiveness of symptom management.

[0122] The follow-up management module generates a follow-up plan based on the patient's condition, which includes the follow-up time, follow-up hospital and examination items. The follow-up management module combines the patient's disease type, stage, etc., and the AI ​​model automatically generates a follow-up plan. The system will remind doctors or patients to execute the follow-up plan in a timely manner, including follow-up visits and feedback on the patient's condition; based on the follow-up results submitted by the patient, the system automatically generates a follow-up summary report and sends it to the patient after review by the doctor; after each follow-up is completed, the system will dynamically adjust the follow-up plan based on the follow-up results.

[0123] The follow-up management module improves the efficiency and quality of patient management through automated and personalized follow-up plans. The module intelligently generates personalized follow-up plans based on the patient's staging, surgical conditions, disease stage and other information. The plan includes the time point of follow-up, necessary medical examinations and evaluation items. Doctors can adjust the AI-generated follow-up plan to ensure the pertinence and effectiveness of the follow-up content. The module supports remote follow-up functions, and patients can participate in follow-up through online graphics, voice, video or online questionnaires to improve the accessibility and convenience of follow-up. Using artificial intelligence generated content (AIGC) technology, the module can automatically collect and organize data during the follow-up process and generate structured and focused follow-up reports.

[0124] In some embodiments, before sending the medication instructions, symptom scales, or follow-up plans to the patient, the rehabilitation management module first sends them to medical staff for review, and then sends them to the patient after review or modification by the medical staff.

[0125] In some embodiments, the AI-based full-course rehabilitation management system of the present invention also provides an intelligent patient education module, which recommends patient education content based on the patient's medical records, and the patient education content includes interactive materials in the form of text, pictures or videos. The intelligent patient education module is an innovative component in this system, which aims to enhance patients' understanding and management capabilities of their own disease status through education. The module has a built-in comprehensive content management system for storing, classifying, marking and updating various patient education materials. These materials include text, pictures, videos and interactive tutorials, covering the basics of tumors, surgery, radiotherapy, chemotherapy, targeted therapy, immunotherapy, nutrition, exercise, psychology, clinical trials and other aspects. Using advanced AI algorithms, the module can intelligently recommend personalized educational content based on patient staging, disease stage, medication regimen, current symptoms, test results and other information.

[0126] The intelligent patient education module is provided by a professional medical team with patient education content, including text, graphics, audio and video and other multimedia resources, to help patients intuitively understand complex medical concepts, and provide patient education content in multiple dimensions based on patient problem points + labels. Patient education content can directly address certain aspects of patients' concerns and enhance patients' understanding of their own disease status and self-management capabilities. The system converts the patient's own condition into a label, and then AI accurately matches to achieve personalized push of patient education content.

[0127] Furthermore, the patient education module includes a content management module and a patient education content recommendation module. The content management module is used to store, classify, mark and update patient education content; the patient education content recommendation module is used to obtain patient label vectors and patient education content label vectors, match and sort the patient education content according to the patient education content label vectors and patient label vectors, and output the sorting results.

[0128] An example of implementing the intelligent patient education module to push personalized patient education content can be found in Figure 6-Figure 8 As shown, it includes the establishment of a patient recommendation database, the establishment of a patient education content recommendation database, and the output of recommended content. Figure 6 The figure shows the process flow of establishing a patient recommendation database. Figure 7 The figure shows the process diagram of establishing the patient education content recommendation library. Figure 8 Shown is a flow chart of personalized recommendation of patient education content, which includes the following steps.

[0129] First, establish a “patient recommendation database”.

[0130] S5-1, Tag management: define tags for patient education content, including: tag name, tag matching conditions, tag business category, and tag type.

[0131] Label business categories include: patient education content recommendation labels, functional rehabilitation program labels, etc. Label types include: calculation by hour, calculation by day, calculation by month, calculation by year, real-time calculation, etc.

[0132] S5-2, load into the "offline calculation" engine or the "real monitoring calculation" engine according to the tag type.

[0133] S5-3, after the “offline calculation” and “real-time monitoring calculation” engines obtain patient data respectively, they match them according to the label conditions.

[0134] S5-4, after the computing engine successfully matches, the patient and label data are written into the database.

[0135] S5-5, the "vector calculator" extracts the patient label data and calculates the "patient label vector value".

[0136] S5-6, write the "patient label vector value" calculated in S5-5 into the "patient recommendation library".

[0137] Establish a “patient education content recommendation database”.

[0138] S6-1, in the recommended data management, configure the "patient education content" recommended data, including: recommended name, recommended category (patient education content), label, recommended patient education scope, etc.

[0139] S6-2, select a label list of recommended data.

[0140] S6-3, calculate the label vector value of the recommended data of “patient education content”.

[0141] S6-4, storing the "patient education content recommendation data label vector value" into the "patient education content recommendation library".

[0142] Patients obtain recommended patient education content through the APP.

[0143] S7-1, obtain the “patient education label vector value” from the “patient recommendation library”.

[0144] S7-2, obtain the “patient education content recommendation data label vector value” from the “patient education content recommendation library”.

[0145] S7-3, use the "patient education label vector value" and the "patient education content recommendation data label vector value" for matching and sorting.

[0146] S7-4, output the matching sorted results.

[0147] The existing patient education content is relatively superficial and cannot explain the disease mechanism, treatment plan and its side effects in depth, making it difficult for patients to fully understand their condition and treatment plan. The patient education content cannot be customized according to the patient's condition, treatment progress and individual needs. The intelligent patient education module of the present invention includes the storage, viewing and maintenance functions of patient education content, and customizes personalized education content for each patient through AI algorithms to improve patients' cognition of the disease and self-management ability.

[0148] In some embodiments, the AI-based full-course rehabilitation management system of the present invention also provides an early warning intervention module, which is used to determine whether the patient's execution indicators are abnormal based on the execution data fed back by the patient; if abnormal, the abnormal indicators are warned and intervention measures are generated and sent to patients and medical staff.

[0149] The early warning and intervention module monitors the execution data submitted by patients in real time through the big data platform, makes comprehensive judgments based on the patient's health records, medical history, diagnosis, treatment, medication and experimental data, and outputs risk warnings after feature analysis by the risk assessment model. The warnings are then returned to the doctor together with the set intervention plan, thus achieving real-time monitoring, early warning and intervention.

[0150] Patients perform rehabilitation tasks according to the intervention plan and evaluate their intervention plan after execution. The system collects and organizes data such as the effectively evaluated early warning intervention plan, patient condition, and patient basic information, and provides it to the AI ​​algorithm to train a new "early warning intervention model."

[0151] See also Fig. 9 , Fig.10 As shown in Figure 2, the intelligent early warning intervention process includes the establishment of early warning trigger rules and early warning notification, such as Fig. 9 The figure shows the process flow of establishing the warning triggering rules. Fig.10 It is a flowchart of intelligent early warning intervention, which includes the following implementation steps.

[0152] The process of establishing warning trigger rules is as follows.

[0153] S8-1, configure the “early warning intervention rule” data.

[0154] S8-2, according to the "patient portrait conditions" of the rule, first query the conditions in the portrait library and circle the people who meet the portrait conditions.

[0155] S8-3, convert the patient ID of the population into a patient portrait population bitmap and write it into the rule base together with the trigger condition.

[0156] S8-4: The “real-time streaming computing engine” pulls the patient portrait population bitmap and trigger conditions of the early warning rules.

[0157] The intelligent early warning intervention process is as follows.

[0158] S9-1, real-time monitoring of health data submitted by patients and pre-processing. For example, using Canal to monitor patient health data in real time and pre-processing.

[0159] S9-2, sends the processed data in the form of events to the Kafka warning event message queue.

[0160] S9-3, the “real-time streaming computing engine” consumes event messages and matches patient profiles with trigger conditions.

[0161] S9-4, when the warning triggering condition is met, the warning message is sent to the Kafka warning intervention message queue;

[0162] S9-5, after the early warning intervention service consumes the "early warning intervention message" from the early warning intervention message queue, it saves the patient's early warning information and calls the message push service to notify the patient.

[0163] In some embodiments, the AI-based full-course rehabilitation management system of the present invention also provides a rehabilitation report management module. The system automatically generates a phased rehabilitation report according to each rehabilitation module. The phased rehabilitation report can be queried by medical staff and patients. The content and information output by the health report include the patient's symptom changes over a period of time, adverse drug reactions, indicator changes, follow-up status, functional rehabilitation status, psychological rehabilitation status, early warning status, rehabilitation plan implementation status, etc.

[0164] Furthermore, before sending the phased rehabilitation report to the patient, the rehabilitation report management module first sends it to the medical staff for review, and then sends it to the patient after the medical staff reviews or modifies it.

[0165] like Fig.11 The figure shows a flow chart of intelligent rehabilitation report generation, which includes the following implementation steps.

[0166] S10-1, providing the patient's rehabilitation data content for a certain period of time.

[0167] S10-2, select the Few-Shot example template.

[0168] S10-3, call LLaMA3 or GEMMA2 large model to generate content.

[0169] The embodiment of the present invention also provides a full-course rehabilitation management method based on AI technology, which is applied to a full-course rehabilitation management system based on AI technology, such as Fig.12 Shown is a flow chart of the full-course rehabilitation management method based on AI technology, which includes the following steps.

[0170] S11-1, establish the patient's medical record file based on the patient's basic information, disease information, treatment information and examination information;

[0171] S11-2, generating an assessment scale based on the medical records, sending the assessment scale to the patient to fill in, and generating a rehabilitation plan based on the completed results and the medical records information after comprehensive evaluation;

[0172] S11-3, send the rehabilitation plan to the medical staff for review, and send the reviewed rehabilitation plan to the patient for execution,

[0173] S11-4, receiving the patient's execution data, generating a rehabilitation plan for the next stage according to the execution data, and repeating the process of S11-3 to S11-4 until the patient is fully recovered.

[0174] Among them, the information interaction between patients and medical staff and the rehabilitation management system, as well as the interaction between patients and medical staff are carried out through the doctor-patient communication module in the system.

[0175] In some embodiments, the rehabilitation program includes a motor rehabilitation program, a functional rehabilitation program, a nutritional rehabilitation program, and a psychological rehabilitation program.

[0176] Specifically, the patient's physical condition is assessed according to the Borg dyspnea rating scale combined with the patient's medical records, and an exercise rehabilitation program is recommended based on the assessment results. The patient's physical function status and rehabilitation needs are assessed according to the mMRC scale and stair climbing test combined with the patient's medical records, and a functional rehabilitation program is recommended based on the assessment results. The patient's nutritional status is assessed according to the nutritional risk screening scale combined with the patient's medical records, and a nutritional rehabilitation program is recommended based on the assessment results. The patient's psychological state is assessed according to the psychological assessment scale, and a psychological rehabilitation program is recommended based on the assessment results.

[0177] In some embodiments, the full-course rehabilitation management method based on AI technology also includes: obtaining the patient's physical data through wearable devices, and sending early warning notifications to the patient and medical staff based on changes in the physical data.

[0178] In some embodiments, the AI-based full-course rehabilitation management method further includes: providing medication time reminders and medication guidance based on the patient's condition and current medication status, and receiving adverse reactions reported by the patient after medication, and associating the adverse reactions with the corresponding drugs. Based on the continuous adverse reactions, the complete process of adverse reactions from severe to mild, or even disappearance during medication is recorded in real time and dynamically.

[0179] In some embodiments, the AI-based full-course rehabilitation management method further includes: generating a symptom scale according to the patient's disease type and stage of illness, sending the symptom scale to the patient for completion, and providing intervention guidance or early warning according to the symptom scale filled in by the patient. A follow-up plan is generated according to the patient's condition, and the follow-up plan includes a follow-up time, a follow-up hospital, and examination items.

[0180] In some embodiments, the full-course rehabilitation management method based on AI technology also includes: generating a follow-up plan according to the patient's condition, and the follow-up plan includes tracking follow-up and examination follow-up.

[0181] In some embodiments, before each assessment scale, rehabilitation program, medication guide, symptom scale or follow-up plan is sent to the patient, it is first sent to medical staff for review, and after review or modification by the medical staff, it is sent to the patient.

[0182] In some embodiments, the full-course rehabilitation management method based on AI technology also includes: recommending patient education content based on the patient's medical records, wherein the patient education content includes interactive materials in the form of text, pictures or videos.

[0183] In some embodiments, recommending patient education content based on patient medical records includes: storing, classifying, marking and updating patient education content through a content management module, obtaining patient label vectors and patient education content label vectors through a patient education content recommendation module, matching and sorting the patient education content according to the patient education content label vectors and patient label vectors, and outputting the sorting results.

[0184] In some embodiments, the full-course rehabilitation management method based on AI technology also includes: judging whether the patient's indicators are abnormal based on the data fed back by the patient; if abnormal, issuing an early warning for the abnormal indicators, and generating intervention measures to send to the patient and medical staff.

[0185] In some embodiments, the full-course rehabilitation management method based on AI technology also includes: periodically generating phased rehabilitation reports based on patient feedback data, and the phased rehabilitation reports are available for medical staff and patients to query. The rehabilitation stage report includes the patient's symptom changes, adverse drug reactions, indicator changes, follow-up status, functional rehabilitation status, psychological rehabilitation status, early warning status, and rehabilitation plan implementation status over a period of time.

[0186] An embodiment of the present invention provides a server, which includes a processor and a memory. The number of processors in the server can be one or more, and the memory can be used to store software programs, computer executable programs and modules. The processor executes various functional applications and data processing by running the software programs, instructions and modules stored in the memory to implement the full-course rehabilitation management method based on AI technology in any of the above embodiments of the present invention.

[0187] The embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the whole-course rehabilitation management method based on AI technology of any embodiment of the present invention is implemented. The storage medium can be ROM / RAM, a magnetic disk, an optical disk, etc.

[0188] An embodiment of the present invention further provides a computer program product. When the computer program product runs on a computer, the computer executes the full-course rehabilitation management method based on AI technology according to any one of the above-mentioned embodiments of the present invention.

[0189] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The whole course rehabilitation management system based on AI technology is characterized by: include: A medical record module is used to store and manage the patient's medical records in structured data, wherein the medical records include the patient's basic information, disease information, treatment information, and examination information; A rehabilitation management module, which is used to generate an assessment scale based on the medical record file, send the assessment scale to the patient to fill in, and generate a rehabilitation plan after comprehensive evaluation based on the filled-in result and the medical record file information; The rehabilitation management module is also used to send the rehabilitation plan to medical staff for review, send the reviewed rehabilitation plan to the patient for execution, receive the patient's execution data, generate the next stage of rehabilitation plan based on the execution data, and repeat this process throughout the patient's entire illness. The doctor-patient communication module is used for information interaction between the patient side and the rehabilitation management module, information interaction between the medical staff side and the rehabilitation management module, and information interaction between the patient side and the medical staff side.

2. The AI-based full-course rehabilitation management system according to claim 1 is characterized in that: The rehabilitation management module includes a sports rehabilitation module, a functional rehabilitation module, a nutritional rehabilitation module and a psychological rehabilitation module; The exercise rehabilitation module is used to evaluate the patient's physical condition according to the Borg dyspnea scoring scale combined with the patient's medical records, and recommend an exercise rehabilitation program based on the evaluation results; The functional rehabilitation module is used to evaluate the patient's physical function status and rehabilitation needs based on the mMRC scale and stair climbing test combined with the patient's medical records, and recommend a functional rehabilitation plan based on the evaluation results; The nutritional rehabilitation module is used to evaluate the nutritional status of the patient based on the nutritional risk screening scale combined with the patient's medical records, and recommend a nutritional rehabilitation plan based on the evaluation results; The psychological rehabilitation module is used to evaluate the patient's psychological state according to the psychological assessment scale and recommend a psychological rehabilitation program based on the assessment results.

3. The AI-based full-course rehabilitation management system according to claim 2 is characterized in that: The rehabilitation management module also includes a monitoring module, which is used to obtain the patient's physical data through a wearable device and send early warning notifications to the patient and medical staff according to changes in the physical data.

4. The AI-based full-course rehabilitation management system according to claim 2 is characterized in that: The rehabilitation management module also includes a medication management module, a symptom management module and a follow-up management module; The medication management module is used to provide medication time reminders and medication guidance according to the patient's condition and current medication status, as well as receive adverse reactions reported by the patient after medication, and associate the adverse reactions with the corresponding drugs; The symptom management module is used to generate a symptom scale according to the patient's disease type and disease stage, send the symptom scale to the patient for filling in, and perform monitoring-early warning-intervention-evaluation guidance according to the symptom scale filled in by the patient; The follow-up management module is used to generate a follow-up plan according to the patient's condition, and the follow-up plan includes tracking follow-up and examination follow-up.

5. The AI-based full-course rehabilitation management system according to claim 4 is characterized in that: Before sending the assessment scale, medication instructions, symptom scale or follow-up plan to the patient, the rehabilitation management module first sends it to medical staff for review, and then sends it to the patient after review or modification by the medical staff.

6. The AI-based full-course rehabilitation management system according to claim 1 is characterized in that: The system also includes an intelligent patient education module, which recommends patient education content based on the patient's medical history files. The patient education content includes interactive materials in the form of text, pictures or videos.

7. The AI-based full-course rehabilitation management system according to claim 6 is characterized in that: The intelligent patient education module includes a content management module and a patient education content recommendation module; The content management module is used to store, classify, mark and update patient education content; The patient education content recommendation module is used to obtain patient label vectors and patient education content label vectors, match and sort the patient education contents according to the patient education content label vectors and the patient label vectors, and output the sorting results.

8. The AI-based full-course rehabilitation management system according to claim 1 is characterized in that: The system also includes an early warning intervention module, which is used to determine whether the patient's indicators are abnormal based on the data fed back by the patient; if abnormal, it will warn of the abnormal indicators and generate intervention measures to send to the patient and medical staff.

9. The AI-based full-course rehabilitation management system according to claim 1 is characterized in that: The system also includes a rehabilitation report generation management module, which periodically generates a phased rehabilitation report based on the data of the rehabilitation management module. The phased rehabilitation report can be queried by medical staff and patients. The rehabilitation phase report includes the patient's symptom changes, adverse drug reactions, indicator changes, follow-up status, functional rehabilitation status, psychological rehabilitation status, early warning status, and rehabilitation plan implementation status over a period of time.

10. The whole-course rehabilitation management method based on AI technology is characterized by: The AI-based full-course rehabilitation management system according to any one of claims 1 to 9, wherein the method comprises: Establish the patient's medical record based on the patient's basic information, disease information, treatment information and examination information; Generate an assessment scale based on the medical record file, send the assessment scale to the patient to fill in, and generate a rehabilitation plan after comprehensive evaluation based on the filled-in result and the medical record file information; The rehabilitation program is sent to medical staff for review, the reviewed rehabilitation program is sent to the patient for execution, and the patient's execution data is received. The next stage of rehabilitation program is generated based on the execution data, and this process is repeated throughout the patient's entire illness.

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