Pre-rehabilitation service auxiliary recommendation method based on large language model and related equipment

Abstract

The invention discloses a pre-rehabilitation service auxiliary recommendation method based on a large language model and related equipment. The method comprises the steps of extracting basic assessment data for pre-rehabilitation assessment of a target user in a medical case system based on target user information; according to the operation type of the target user and the basic evaluation data, determining lacked to-be-collected data to assist in generating a checklist to obtain supplementary evaluation data; obtaining an initial evaluation result of the target user through a pre-rehabilitation clinical knowledge base based on the basic evaluation data, the supplementary evaluation data and the operation type; and analyzing and generating a pre-rehabilitation intervention measure set according to the initial evaluation result of the target user and the pre-rehabilitation clinical knowledge base, so as to push the pre-rehabilitation intervention measure set to a client associated with the target user. The problems that a pre-operation question answering system does not exist, or the question answering content is simple, the system does not have an auxiliary recommendation function according to an evaluation result, and a multi-party interaction platform is lacked are solved.

Description

Technical Field

[0001] This application relates to the field of computers, and more specifically, to a method and related equipment for recommending pre-rehabilitation services based on a large language model. Background Technology

[0002] Preoperative rehabilitation is a new preoperative management strategy that refers to enhancing patients' preoperative physiological and psychological functional reserves in order to increase their tolerance to surgery, reduce the risk of perioperative complications and mortality, shorten hospital stay, and improve medical efficiency.

[0003] The clinical implementation of pre-operative rehabilitation generally includes four processes: screening, assessment, intervention, and evaluation. Preoperatively, medical staff conduct a systematic medical history inquiry and auxiliary examinations to assess the patient's surgical risk level, identify key pre-operative risk factors, and provide optimized pre-operative rehabilitation measures. Due to individual differences among patients, pre-operative rehabilitation optimization measures should be individualized based on the screening and assessment results, providing targeted pre-operative rehabilitation interventions. Medical staff continuously monitor and evaluate the interventions and adjust the intervention measures based on the evaluation results.

[0004] Under the traditional medical model, pre-rehabilitation faces numerous challenges, including: 1) The screening and assessment processes are complex, requiring medical staff to spend considerable time on consultations. Pre-operative laboratory and auxiliary examinations are also difficult to complete within a short timeframe, and the follow-up of assessment results lacks a systematic approach, resulting in very low efficiency in pre-operative screening and assessment; 2) Due to the variability in patients' physical conditions and illnesses, pre-rehabilitation optimization measures should be individualized based on screening and assessment results. However, clinical practice still relies primarily on traditional manual assessment and intervention measures, whose scientific validity and accuracy are easily limited by the knowledge level of medical staff, exhibiting a degree of subjectivity; 3) The implementation of pre-rehabilitation requires collaboration from a multidisciplinary team including doctors, nurses, and therapists specializing in sports, rehabilitation, nutrition, and psychotherapy. Furthermore, patient management spans both inside and outside the hospital, making an efficient multi-party collaborative platform for pre-rehabilitation lacking; 4) Monitoring and evaluating the effectiveness of pre-rehabilitation involves complex data collection, processing, and analysis. Medical institutions need to invest significant time and manpower in data collection, and manually processing large amounts of monitoring data and extracting useful information presents considerable challenges.

[0005] Currently, there is no preoperative question-and-answer system or the content of the questions and answers is relatively simple. The system does not yet have the function of providing auxiliary recommendations based on the assessment results, and it lacks a multi-party interactive platform, making it difficult to solve the above-mentioned key problems. Summary of the Invention

[0006] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. The summary section of this invention is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0007] To address the current issues of a lack of preoperative question-and-answer systems, overly simplistic question-and-answer content, insufficient system-based recommendation capabilities, and a lack of multi-party interactive platforms, this invention proposes, firstly, a pre-rehabilitation service recommendation method based on a large language model. This method includes:

[0008] Based on the target user information, basic assessment data for the pre-recovery assessment of the target user is extracted from the medical case system, wherein the basic assessment data includes historical case data and standardized inquiry result data;

[0009] Based on the target user's surgery type and the basic assessment data, identify the missing data to be collected to assist in generating an examination report to obtain supplementary assessment data;

[0010] Based on the basic assessment data, the supplementary assessment data, and the surgical type, the initial assessment results of the target user are obtained through the pre-rehabilitation clinical knowledge base;

[0011] Based on the initial assessment results of the target users and the analysis of the pre-rehabilitation clinical knowledge base, a set of pre-rehabilitation intervention measures is generated and pushed to the client associated with the target users.

[0012] Optionally, the pre-rehabilitation intervention set includes the implementation period, implementation cycle, and implementation type of the measures. The method further includes: pushing the implementation type of the measures associated with the target user to the client associated with the target user during the implementation period of each implementation cycle.

[0013] Optional, also includes:

[0014] During the implementation period of each implementation cycle of the aforementioned measures, the device status information of the smart terminal device to which the client associated with the target user belongs is collected;

[0015] The completion result of the implementation type of the measures associated with the target user is evaluated based on the device status information.

[0016] Optionally, the measure implementation type is a motion type, the device status information is mobile phone gyroscope information and acceleration information, and the evaluation of the completion result of the measure implementation type associated with the target user based on the device status information includes:

[0017] The evaluation of the target user's associated motion measures is based on information from the mobile phone's gyroscope and acceleration.

[0018] Optional, also includes:

[0019] Image data from the smart terminal device of the client associated with the target user during the implementation period of each implementation cycle of the measures;

[0020] The completion results of the implementation type of the measures associated with the target user are evaluated based on the image data.

[0021] Optionally, the measure implementation type is a nutritional supplement type, and the evaluation of the completion result of the measure implementation type associated with the target user based on the image data includes:

[0022] The image data is analyzed to determine the computational reference comparison image portion in the image data, wherein the computational reference comparison is an everyday standard item with a defined size;

[0023] If the computational reference comparison is present in the image data, the food category in the food image portion of the image data is determined;

[0024] The portion size data for each food category is determined based on the proportional relationship between the reference comparison image portion and the food image portion;

[0025] The completion results of the implementation type of the measures associated with the target user are evaluated based on the food category and the portion size data.

[0026] Optionally, the implementation type of the measure is a movement type, and the method further includes:

[0027] Generate an initial body temperature comparison measurement command to instruct the temperature management system of the smart terminal device associated with the target user's client to gradually increase the temperature of the smart terminal device so that the target user can bring the smart terminal device close to the human body temperature measurement location for temperature comparison until the temperature is close to the target user's current body temperature, and then receive the target user's request to stop increasing the temperature.

[0028] The temperature of the smart terminal device at the moment the request to stop heating is received is taken as the initial body temperature of the target user.

[0029] Based on the target user's initial body temperature and physical condition information, the target user's body temperature rise after completing the exercise type intervention is determined. The temperature management system of the smart terminal device is then controlled to ensure that the smart terminal device reaches the sum of the body temperature rise and the initial body temperature, so that the target user can self-test whether the exercise type intervention has been completed by comparing their post-exercise body temperature.

[0030] Secondly, this invention also proposes a pre-rehabilitation service assistance recommendation device based on a large language model, comprising:

[0031] The extraction unit is used to extract basic assessment data for the pre-recovery assessment of the target user from the medical case system based on the target user information, wherein the basic assessment data includes historical case data and standardized inquiry result data;

[0032] The data supplementation unit is used to determine the missing data to be collected based on the surgical type of the target user and the basic assessment data, so as to assist in generating an examination form to obtain supplementary assessment data.

[0033] An assessment unit is used to obtain the initial assessment results of the target user based on the basic assessment data, the supplementary assessment data, and the surgical type through a pre-rehabilitation clinical knowledge base.

[0034] The recommendation unit is used to generate a set of pre-rehabilitation intervention measures based on the initial assessment results of the target user and the analysis of the pre-rehabilitation clinical knowledge base, and to push them to the client associated with the target user.

[0035] Thirdly, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the pre-rehabilitation service assistance recommendation method based on a large language model as described in any of the first aspects above.

[0036] Fourthly, the present invention also proposes a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the pre-rehabilitation service assistance recommendation method based on a large language model as described above in the first aspect.

[0037] In summary, the pre-rehabilitation service assistance recommendation method proposed in this application extracts basic assessment data for the pre-rehabilitation assessment of the target user from the medical case system based on the target user's information. This basic assessment data includes historical case data and standardized inquiry result data. Based on the target user's surgical type and the basic assessment data, missing data to be collected is identified to assist in generating examination forms to obtain supplementary assessment data. Based on the basic assessment data, the supplementary assessment data, and the surgical type, an initial assessment result for the target user is obtained through a pre-rehabilitation clinical knowledge base. Based on the initial assessment result and the analysis of the pre-rehabilitation clinical knowledge base, a set of pre-rehabilitation intervention measures is generated and pushed to the client associated with the target user. This method is significant in improving the efficiency of pre-rehabilitation screening, assessment, intervention, and evaluation, and in enhancing the accuracy of personalized optimization measures. It also supports the integration of high-quality pre-rehabilitation medical resources and promotes the popularization of pre-rehabilitation medical services, helping to alleviate the current problems of strained medical and nursing resources and a shortage of pre-rehabilitation professionals. Furthermore, it improves preoperative preparation efficiency, shortens preoperative waiting time, and enhances the quality of perioperative medical and nursing care.

[0038] The pre-rehabilitation service assistance recommendation method based on a large language model of the present invention, other advantages, objectives and features of the present invention will be partly apparent from the following description, and partly understood by those skilled in the art through research and practice of the present invention. Attached Figure Description

[0039] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit this specification. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0040] Figure 1 A schematic diagram of a pre-rehabilitation service assistance recommendation method based on a large language model is provided for an embodiment of this application;

[0041] Figure 2 A schematic diagram showing the set of pre-rehabilitation intervention measures provided in the embodiments of this application;

[0042] Figure 3 A schematic diagram of a pre-rehabilitation service assistance recommendation device based on a large language model is provided for an embodiment of this application;

[0043] Figure 4 This is a schematic diagram of an electronic device structure for assisting in the recommendation of pre-rehabilitation services based on a large language model, provided as an embodiment of this application. Detailed Implementation

[0044] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this application will now be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0045] To address the current lack of a preoperative question-and-answer system, the simplistic nature of the questions and answers, the absence of supplementary recommendation functions based on assessment results, and the lack of a multi-party interactive platform, please refer to [link to relevant documentation]. Figure 1 This is a flowchart illustrating a pre-rehabilitation service assistance recommendation method based on a large language model, which may specifically include steps S110 to S140.

[0046] S110, Based on the target user information, extract basic assessment data for the pre-recovery assessment of the target user from the medical case system, wherein the basic assessment data includes historical case data and standardized inquiry result data.

[0047] For example, basic assessment data of patients can be collected by using dialogue questioning and automatic extraction from hospital case systems based on a pre-trained large language model. Typical dialogue questions include: (1) How old are you? (2) Have you been smoking frequently in the past year and have you quit smoking for less than 2 weeks? (3) Have you been drinking frequently in the past year and have you quit drinking for less than 2 weeks? (4) Do you need assistance with daily activities, such as bed-chair transfer, dressing, bathing, cooking, and shopping? (5) Do you have chronic respiratory diseases such as COPD or asthma, or experience shortness of breath without obvious cause? (6) Do you have heart disease such as heart failure or myocardial infarction, or experience mild to severe limitation of physical activity? (7) Do you have a lung infection, or experience coughing or sputum production? (8) What is your height? (9) What is your weight? (10) Have you recently experienced involuntary weight loss and decreased appetite? (11) Have you recently experienced anemia? (12) In the past two weeks, have you experienced a lack of interest and enjoyment in doing anything for more than half the time, feeling down, depressed, hopeless, tense, anxious, or impatient, and unable to stop or control your worries? (13) Do you have diabetes? (14) Do you have high blood pressure? (15) Do you have any other medical conditions? (16) Do you have cognitive impairment? (17) What medications are you taking?

[0048] For example, AI recognition can be used to automatically extract medical history data from the patient's medical record system, including previous medical records, current outpatient and emergency records, laboratory reports, examination reports, etc., as well as information on chronic diseases, medications, and other comorbidities and diseases, laboratory indicators such as hemoglobin and liver and kidney function, and auxiliary examination data such as first-second lung ventilation.

[0049] S120, based on the target user's surgery type and the basic assessment data, determine the missing data to be collected to assist in generating an examination report to obtain supplementary assessment data.

[0050] For example, for the medical history inquiry unit and the case system data extraction unit, any missing laboratory data and auxiliary examination data can be collected, and automatic prescription recommendations can be provided, distributed to the user's terminal, and linked to the case system to guide medical staff to prescribe examinations or for patients to prescribe them themselves, as shown in the following example:

[0051] The system asks: Do you suffer from chronic respiratory diseases such as COPD or asthma, or experience shortness of breath without any obvious cause? The target user replies: I've been having trouble breathing lately. The system automatically searches the system and finds no results for lung function monitoring. It then sends a self-service lung function monitoring application to the patient's end and sends a prescription reminder to the doctor's end.

[0052] S130, based on the basic assessment data, the supplementary assessment data, and the surgical type, the initial assessment results of the target user are obtained through the pre-rehabilitation clinical knowledge base.

[0053] For example, a pre-rehabilitation clinical knowledge base can be linked, and the knowledge base can automatically acquire and import the data extracted by the information extraction unit into the assessment form for evaluation, and provide the assessment results. The pre-rehabilitation assessment includes, but is not limited to, smoking history, smoking index, drinking history, physical activity capacity, lung function assessment, NRS2002, BMI, hemoglobin value, PQH-2, GAD-2, hospital anxiety and depression scale, blood pressure value, random blood glucose value, glycated hemoglobin value, history of chronic lung disease, history of chronic heart disease, history of renal insufficiency, history of anticoagulant medication use, etc.

[0054] S140, a set of pre-rehabilitation intervention measures is generated based on the initial assessment results of the target user and the analysis of the pre-rehabilitation clinical knowledge base, and then pushed to the client associated with the target user.

[0055] For example, it is possible to further link to the pre-rehabilitation clinical knowledge base, match the corresponding pre-rehabilitation intervention set based on the assessment results generated from various assessment forms, and recommend it to the patient's terminal. Figure 2 The format generates a set of intervention measures for the patient.

[0056] For example, target users can choose based on their personal health habits and preferences. Figure 2 In the window shown, you can select measures, and the system will generate feedback data and return it to the dataset.

[0057] In summary, the pre-rehabilitation service assistance recommendation method based on a large language model provided in this application extracts basic assessment data for the pre-rehabilitation assessment of the target user from a medical case system based on the target user's information. This basic assessment data includes historical case data and standardized inquiry result data. Based on the target user's surgical type and the basic assessment data, missing data to be collected is identified to assist in generating examination forms to obtain supplementary assessment data. Based on the basic assessment data, the supplementary assessment data, and the surgical type, an initial assessment result for the target user is obtained through a pre-rehabilitation clinical knowledge base. Based on the initial assessment result and the pre-rehabilitation clinical knowledge base analysis, a set of pre-rehabilitation intervention measures is generated and pushed to the client associated with the target user. This method is significant in improving the efficiency of pre-rehabilitation screening, assessment, intervention, and evaluation, and in improving the accuracy of personalized optimization measures. It also supports the integration of high-quality pre-rehabilitation medical resources and promotes the popularization of pre-rehabilitation medical services, which is beneficial in alleviating the current problems of strained medical and nursing resources and a shortage of pre-rehabilitation professionals. Furthermore, it improves preoperative preparation efficiency, shortens preoperative waiting time, and enhances the quality of perioperative medical and nursing care.

[0058] For example, healthcare professionals can conduct appropriate inquiries and provide guidance to patients based on the screening and assessment results, pre-rehabilitation measures set, and evaluation results, and further conduct pre-rehabilitation assessments.

[0059] According to some embodiments, the pre-rehabilitation intervention set includes an implementation period, an implementation cycle, and an implementation type. The method further includes: pushing the implementation type associated with the target user to the client associated with the target user during the implementation period of each implementation cycle.

[0060] For example, the pre-rehabilitation measures set can be broken down according to time points and pushed to the patient's smart terminal. For instance: At 9:00 AM, the system pushes: Please complete 15-20 medium-sized balloon blowing exercises. Please perform three stair-climbing exercises, with the intensity reaching a point where you feel a slight increase in heart rate, each lasting 15-20 minutes. Please monitor your blood sugar 2 hours after breakfast.

[0061] For example, user completion status can be collected, and further evaluation results can be generated based on the collected completion status information, including completion feedback reminders and suggestion feedback reminders. An example is: The system asks: Did you complete the balloon blowing and stair climbing exercises? The target user answers: I completed the balloon blowing exercise, but not the stair climbing exercise. The system asks: Why didn't you complete the stair climbing exercise? The target user answers: This intensity was too high for me; I was out of breath when I finished, and I was afraid my heart couldn't handle it. Some examples also include:

[0062] During the implementation period of each implementation cycle of the aforementioned measures, the device status information of the smart terminal device to which the client associated with the target user belongs is collected;

[0063] The completion result of the implementation type of the measures associated with the target user is evaluated based on the device status information.

[0064] In some examples, the measure implementation type is a motion type, the device status information is mobile phone gyroscope information and acceleration information, and the evaluation of the completion result of the measure implementation type associated with the target user based on the device status information includes:

[0065] The evaluation of the target user's associated motion measures is based on information from the mobile phone's gyroscope and acceleration.

[0066] In some examples, it also includes:

[0067] Image data from the smart terminal device of the client associated with the target user during the implementation period of each implementation cycle of the measures;

[0068] The completion results of the implementation type of the measures associated with the target user are evaluated based on the image data.

[0069] In some examples, the measure implementation type is a nutritional supplement type, and the evaluation of the completion result of the measure implementation type associated with the target user based on the image data includes:

[0070] The image data is analyzed to determine the computational reference comparison image portion in the image data, wherein the computational reference comparison is an everyday standard item with a defined size;

[0071] If the computational reference comparison is present in the image data, the food category in the food image portion of the image data is determined;

[0072] The portion size data for each food category is determined based on the proportional relationship between the reference comparison image portion and the food image portion;

[0073] The completion results of the implementation type of the measures associated with the target user are evaluated based on the food category and the portion size data.

[0074] In some examples, the implementation type of the measure is a movement type, and the method further includes:

[0075] Generate an initial body temperature comparison measurement command to instruct the temperature management system of the smart terminal device associated with the target user's client to gradually increase the temperature of the smart terminal device so that the target user can bring the smart terminal device close to the human body temperature measurement location for temperature comparison until the temperature is close to the target user's current body temperature, and then receive the target user's request to stop increasing the temperature.

[0076] The temperature of the smart terminal device at the moment the request to stop heating is received is taken as the initial body temperature of the target user.

[0077] Based on the target user's initial body temperature and physical condition information, the target user's body temperature rise after completing the exercise type intervention is determined. The temperature management system of the smart terminal device is then controlled to ensure that the smart terminal device reaches the sum of the body temperature rise and the initial body temperature, so that the target user can self-test whether the exercise type intervention has been completed by comparing their post-exercise body temperature.

[0078] Please see Figure 3 One embodiment of the pre-rehabilitation service assistance recommendation device based on a large language model in this application may include:

[0079] Extraction unit 21 is used to extract basic assessment data for the pre-rehabilitation assessment of the target user from the medical case system based on the target user information, wherein the basic assessment data includes historical case data and standardized inquiry result data;

[0080] The data supplementation unit 22 is used to determine the missing data to be collected based on the surgical type of the target user and the basic assessment data, so as to assist in generating an examination form to obtain supplementary assessment data;

[0081] Assessment unit 23 is used to obtain the initial assessment results of the target user based on the basic assessment data, the supplementary assessment data, and the surgical type through the pre-rehabilitation clinical knowledge base;

[0082] Recommendation unit 24 is used to generate a set of pre-rehabilitation intervention measures based on the initial assessment results of the target user and the analysis of the pre-rehabilitation clinical knowledge base, and to push them to the client associated with the target user.

[0083] In summary, the pre-rehabilitation service assistance recommendation device based on a large language model provided in this application extracts basic assessment data for the pre-rehabilitation assessment of the target user from a medical case system based on the target user's information. This basic assessment data includes historical case data and standardized inquiry result data. Based on the target user's surgical type and the basic assessment data, missing data to be collected is identified to assist in generating examination forms to obtain supplementary assessment data. Based on the basic assessment data, the supplementary assessment data, and the surgical type, an initial assessment result for the target user is obtained through a pre-rehabilitation clinical knowledge base. Based on the initial assessment result and the pre-rehabilitation clinical knowledge base analysis, a set of pre-rehabilitation intervention measures is generated and pushed to the client associated with the target user. This is significant for improving the efficiency of pre-rehabilitation screening, assessment, intervention, and evaluation, and for improving the accuracy of personalized optimization measures. It also supports the integration of high-quality pre-rehabilitation medical resources and promotes the popularization of pre-rehabilitation medical services, which is beneficial for alleviating the current problems of strained medical and nursing resources and a shortage of pre-rehabilitation professionals. Furthermore, it improves preoperative preparation efficiency, shortens preoperative waiting time, and improves the quality of perioperative medical and nursing care.

[0084] like Figure 4 As shown, this application embodiment also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 320 and executable on the processor. When the processor 320 executes the computer program 311, it implements the steps of any of the above-mentioned methods for pre-rehabilitation service assistance recommendations based on a large language model:

[0085] Based on the target user information, basic assessment data for the pre-recovery assessment of the target user is extracted from the medical case system, wherein the basic assessment data includes historical case data and standardized inquiry result data;

[0086] Based on the target user's surgery type and the basic assessment data, identify the missing data to be collected to assist in generating an examination report to obtain supplementary assessment data;

[0087] Based on the basic assessment data, the supplementary assessment data, and the surgical type, the initial assessment results of the target user are obtained through the pre-rehabilitation clinical knowledge base;

[0088] Based on the initial assessment results of the target users and the analysis of the pre-rehabilitation clinical knowledge base, a set of pre-rehabilitation intervention measures is generated and pushed to the client associated with the target users.

[0089] Optionally, the pre-rehabilitation intervention set includes the implementation period, implementation cycle, and implementation type of the measures. The method further includes: pushing the implementation type of the measures associated with the target user to the client associated with the target user during the implementation period of each implementation cycle.

[0090] Optional, also includes:

[0091] During the implementation period of each implementation cycle of the aforementioned measures, the device status information of the smart terminal device to which the client associated with the target user belongs is collected;

[0092] The completion result of the implementation type of the measures associated with the target user is evaluated based on the device status information.

[0093] Optionally, the measure implementation type is a motion type, the device status information is mobile phone gyroscope information and acceleration information, and the evaluation of the completion result of the measure implementation type associated with the target user based on the device status information includes:

[0094] The evaluation of the target user's associated motion measures is based on information from the mobile phone's gyroscope and acceleration.

[0095] Optional, also includes:

[0096] Image data from the smart terminal device of the client associated with the target user during the implementation period of each implementation cycle of the measures;

[0097] The completion results of the implementation type of the measures associated with the target user are evaluated based on the image data.

[0098] Optionally, the measure implementation type is a nutritional supplement type, and the evaluation of the completion result of the measure implementation type associated with the target user based on the image data includes:

[0099] The image data is analyzed to determine the computational reference comparison image portion in the image data, wherein the computational reference comparison is an everyday standard item with a defined size;

[0100] If the computational reference comparison is present in the image data, the food category in the food image portion of the image data is determined;

[0101] The portion size data for each food category is determined based on the proportional relationship between the reference comparison image portion and the food image portion;

[0102] The completion results of the implementation type of the measures associated with the target user are evaluated based on the food category and the portion size data.

[0103] Optionally, the implementation type of the measure is a movement type, and the method further includes:

[0104] Generate an initial body temperature comparison measurement command to instruct the temperature management system of the smart terminal device associated with the target user's client to gradually increase the temperature of the smart terminal device so that the target user can bring the smart terminal device close to the human body temperature measurement location for temperature comparison until the temperature is close to the target user's current body temperature, and then receive the target user's request to stop increasing the temperature.

[0105] The temperature of the smart terminal device at the moment the request to stop heating is received is taken as the initial body temperature of the target user.

[0106] Based on the target user's initial body temperature and physical condition information, the target user's body temperature rise after completing the exercise type intervention is determined. The temperature management system of the smart terminal device is then controlled to ensure that the smart terminal device reaches the sum of the body temperature rise and the initial body temperature, so that the target user can self-test whether the exercise type intervention has been completed by comparing their post-exercise body temperature.

[0107] Since the electronic device described in this embodiment is the device used to implement a pre-rehabilitation service assistance recommendation device based on a large language model in this application embodiment, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the method described in this application embodiment. Therefore, how the electronic device implements the method in this application embodiment will not be described in detail here. Any device used by those skilled in the art to implement the method in this application embodiment is within the scope of protection of this application.

[0108] In practical implementation, when the computer program 311 is executed by the processor, it can achieve the following: Figure 1 Any of the implementation methods in the corresponding embodiments:

[0109] Based on the target user information, basic assessment data for the pre-recovery assessment of the target user is extracted from the medical case system, wherein the basic assessment data includes historical case data and standardized inquiry result data;

[0110] Based on the target user's surgery type and the basic assessment data, identify the missing data to be collected to assist in generating an examination report to obtain supplementary assessment data;

[0111] Based on the basic assessment data, the supplementary assessment data, and the surgical type, the initial assessment results of the target user are obtained through the pre-rehabilitation clinical knowledge base;

[0112] Based on the initial assessment results of the target users and the analysis of the pre-rehabilitation clinical knowledge base, a set of pre-rehabilitation intervention measures is generated and pushed to the client associated with the target users.

[0113] Optionally, the pre-rehabilitation intervention set includes the implementation period, implementation cycle, and implementation type of the measures. The method further includes: pushing the implementation type of the measures associated with the target user to the client associated with the target user during the implementation period of each implementation cycle.

[0114] Optional, also includes:

[0115] During the implementation period of each implementation cycle of the aforementioned measures, the device status information of the smart terminal device to which the client associated with the target user belongs is collected;

[0116] The completion result of the implementation type of the measures associated with the target user is evaluated based on the device status information.

[0117] Optionally, the measure implementation type is a motion type, the device status information is mobile phone gyroscope information and acceleration information, and the evaluation of the completion result of the measure implementation type associated with the target user based on the device status information includes:

[0118] The evaluation of the target user's associated motion measures is based on information from the mobile phone's gyroscope and acceleration.

[0119] Optional, also includes:

[0120] Image data from the smart terminal device of the client associated with the target user during the implementation period of each implementation cycle of the measures;

[0121] The completion results of the implementation type of the measures associated with the target user are evaluated based on the image data.

[0122] Optionally, the measure implementation type is a nutritional supplement type, and the evaluation of the completion result of the measure implementation type associated with the target user based on the image data includes:

[0123] The image data is analyzed to determine the computational reference comparison image portion in the image data, wherein the computational reference comparison is an everyday standard item with a defined size;

[0124] If the computational reference comparison is present in the image data, the food category in the food image portion of the image data is determined;

[0125] The portion size data for each food category is determined based on the proportional relationship between the reference comparison image portion and the food image portion;

[0126] The completion results of the implementation type of the measures associated with the target user are evaluated based on the food category and the portion size data.

[0127] Optionally, the implementation type of the measure is a movement type, and the method further includes:

[0128] Generate an initial body temperature comparison measurement command to instruct the temperature management system of the smart terminal device associated with the target user's client to gradually increase the temperature of the smart terminal device so that the target user can bring the smart terminal device close to the human body temperature measurement location for temperature comparison until the temperature is close to the target user's current body temperature, and then receive the target user's request to stop increasing the temperature.

[0129] The temperature of the smart terminal device at the moment the request to stop heating is received is taken as the initial body temperature of the target user.

[0130] Based on the target user's initial body temperature and physical condition information, the target user's body temperature rise after completing the exercise type intervention is determined. The temperature management system of the smart terminal device is then controlled to ensure that the smart terminal device reaches the sum of the body temperature rise and the initial body temperature, so that the target user can self-test whether the exercise type intervention has been completed by comparing their post-exercise body temperature.

[0131] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0132] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0133] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0134] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0135] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0136] This application also provides a computer program product, which includes computer software instructions that, when executed on a processing device, cause the processing device to perform actions such as... Figure 1 The corresponding embodiment describes the process of recommending pre-rehabilitation services based on a large language model.

[0137] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0138] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0139] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0140] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0141] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0142] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0143] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A pre-rehabilitation service assisted recommendation method based on a large language model, characterized in that, include: Based on the target user information, basic assessment data for the pre-recovery assessment of the target user is extracted from the medical case system, wherein the basic assessment data includes historical case data and standardized inquiry result data; Based on the target user's surgery type and the basic assessment data, identify the missing data to be collected to assist in generating an examination report and obtaining supplementary assessment data; Based on the basic assessment data, the supplementary assessment data, and the surgical type, the initial assessment results of the target user are obtained through the pre-rehabilitation clinical knowledge base; Based on the initial assessment results of the target users and the analysis of the pre-rehabilitation clinical knowledge base, a set of pre-rehabilitation intervention measures is generated and pushed to the client associated with the target users.

2. The method as described in claim 1, characterized in that, The pre-rehabilitation intervention set includes the implementation period, implementation cycle, and implementation type of the measures. The method further includes: pushing the implementation type of the measures associated with the target user to the client associated with the target user during the implementation period of each implementation cycle.

3. The method as described in claim 2, characterized in that, Also includes: During the implementation period of each implementation cycle of the aforementioned measures, the device status information of the smart terminal device to which the client associated with the target user belongs is collected; The completion result of the implementation type of the measures associated with the target user is evaluated based on the device status information.

4. The method as described in claim 3, characterized in that, The measure implementation type is a motion type, the device status information is mobile phone gyroscope information and acceleration information, and the evaluation of the completion result of the measure implementation type associated with the target user based on the device status information includes: The evaluation of the target user's associated motion measures is based on information from the mobile phone's gyroscope and acceleration.

5. The method as described in claim 2, characterized in that, Also includes: Image data from the smart terminal device of the client associated with the target user during the implementation period of each implementation cycle of the measures; The completion results of the implementation type of the measures associated with the target user are evaluated based on the image data.

6. The method as described in claim 5, characterized in that, The implementation type of the measure is nutritional supplementation, and the evaluation of the completion result of the implementation type of the measure associated with the target user based on the image data includes: The image data is analyzed to determine the computational reference comparison image portion in the image data, wherein the computational reference comparison is an everyday standard item with a defined size; If the computational reference comparison is present in the image data, the food category in the food image portion of the image data is determined; The portion size data for each food category is determined based on the proportional relationship between the reference comparison image portion and the food image portion; The completion results of the implementation type of the measures associated with the target user are evaluated based on the food category and the portion size data.

7. The method according to any one of claims 2-6, characterized in that, The measure is implemented as a sports activity, and the method further includes: Generate an initial body temperature comparison measurement command to instruct the temperature management system of the smart terminal device associated with the target user's client to gradually increase the temperature of the smart terminal device so that the target user can bring the smart terminal device close to the human body temperature measurement location for temperature comparison until the temperature is close to the target user's current body temperature, and then receive the target user's request to stop increasing the temperature. The temperature of the smart terminal device at the moment the request to stop heating is received is taken as the initial body temperature of the target user. Based on the target user's initial body temperature and physical condition information, the target user's body temperature rise after completing the exercise type intervention is determined. The temperature management system of the smart terminal device is then controlled to ensure that the smart terminal device reaches the sum of the body temperature rise and the initial body temperature, so that the target user can self-test whether the exercise type intervention has been completed by comparing their post-exercise body temperature.

8. A pre-rehabilitation service assistance recommendation device based on a large language model, characterized in that, include: The extraction unit is used to extract basic assessment data for the pre-recovery assessment of the target user from the medical case system based on the target user information, wherein the basic assessment data includes historical case data and standardized inquiry result data; The data supplementation unit is used to determine the missing data to be collected based on the surgical type of the target user and the basic assessment data, so as to assist in generating an examination form to obtain supplementary assessment data. An assessment unit is used to obtain the initial assessment results of the target user based on the basic assessment data, the supplementary assessment data, and the surgical type through a pre-rehabilitation clinical knowledge base. The recommendation unit is used to generate a set of pre-rehabilitation intervention measures based on the initial assessment results of the target user and the analysis of the pre-rehabilitation clinical knowledge base, and to push them to the client associated with the target user.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program stored in the memory, implements the steps of the pre-rehabilitation service assistance recommendation method based on any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the pre-rehabilitation service assistance recommendation method based on any one of claims 1-7.

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