81 results about "Patient feedback" patented technology

The invention discloses a traditional Chinese medicine nursing management system and method based on a cloud follow-up visit management platform, and belongs to the technical field of intelligent nursing. The management system comprises the following modules: a dynamic data acquisition module for acquiring physiological parameters, living habits and environmental data of a patient in real time and performing primary processing; the dynamic health archive construction module is used for constructing and continuously updating an individualized health archive of the patient based on the data collected in real time; the traditional Chinese medicine knowledge map and reasoning module depends on the traditional Chinese medicine nursing knowledge map and combines an intelligent reasoning technology to realize accurate traditional Chinese medicine syndrome differentiation and treatment analysis and automatically generate a personalized nursing scheme; the multi-dimensional patient feedback module is used for constructing a feedback mechanism fusing objective data, patient subjective feeling and medical care evaluation so as to extract key nursing effect information; and the nursing scheme iterative optimization module dynamically adjusts and optimizes the traditional Chinese medicine nursing scheme based on the feedback data, and realizes personalized and self-adaptive continuous improved nursing.

The invention provides an evaluation-intervention integrated system and equipment oriented to personalized rehabilitation scheme optimization. The evaluation-intervention integrated system comprises a personalized scheme recommendation module, a curative effect evaluation module and an intervention scheme optimization and adjustment module. The personalized scheme recommendation module generates a personalized rehabilitation treatment scheme by collecting multi-dimensional data and combining with a machine learning algorithm. And the curative effect evaluation module monitors and comprehensively evaluates the rehabilitation effect of the patient in real time, and generates a detailed curative effect report. And the intervention scheme optimization and adjustment module dynamically adjusts the treatment scheme and optimizes the treatment content, intensity and frequency according to the curative effect evaluation result and patient feedback. Through intelligent analysis and a feedback mechanism, an accurate and customized rehabilitation treatment scheme is provided for each patient, dynamic adjustment is performed according to a real-time evaluation result, and the rehabilitation treatment accuracy and the treatment effect are remarkably improved. The system also supports real-time data sharing and cooperation among patients, doctors and rehabilitation experts, and can perform cross-region and cross-professional personalized treatment management.

The invention belongs to the related technical field of neural rehabilitation engineering, and particularly relates to a cerebral apoplexy rehabilitation method based on fusion of a motor imagery brain-computer interface and transcranial direct current stimulation, which comprises the following steps: acquiring a corresponding artifact template based on transcranial direct current stimulation parameters; carrying out source separation on the collected electroencephalogram test times by adopting a joint blind source separation algorithm to obtain clean electroencephalogram signal components; wherein the construction mode of the artifact template library comprises the following steps: extracting electroencephalogram trial artifact samples of multiple stimulation periods, and generating a transcranial direct current stimulation artifact template through an average analysis or principal component analysis mode to obtain the artifact template library; performing motion intention decoding on the clean electroencephalogram signal component to obtain a motion intention of the patient; inputting the motion intention, pre-collected patient body state evaluation information and patient feedback comfort into a fuzzy system algorithm to obtain stimulation parameters of to-be-applied transcranial direct current stimulation, and realizing active rehabilitation through the transcranial direct current stimulation. According to the invention, algorithm-driven closed-loop rehabilitation regulation and control can be realized.

The invention relates to the technical field of medical health, in particular to a chronic disease patient-oriented home nursing management platform, comprising: a terminal sensing module comprising a monitoring bracelet and a glucometer and used for obtaining real-time data of a patient; the monitoring bracelet is used for monitoring physiological indexes and motion data of a patient in real time; the glucometer is used for monitoring blood glucose data of a patient in real time; the edge calculation module comprises home gateway equipment and an edge intelligent analysis system; the cloud service module comprises a data processing center and an intelligent decision-making system; the intelligent decision-making system comprises a health risk assessment model, an exercise scheme optimization model and an expert knowledge base comprising an exercise guidance strategy and a life rhythm management strategy; and the optimization module is used for comprehensively coordinating the generated results, dynamically adjusting motion scheme parameters according to patient feedback and execution conditions, and pushing an optimized personalized scheme to a user terminal for execution.

The invention belongs to the technical field of medical data processing, and relates to a urinary surgery diagnosis and treatment data intelligent processing method and system, and the system comprises a multi-modal data collection module which is used for collecting basic diagnosis and treatment data, behavior data and diagnosis and treatment process data of a patient; the diagnosis and treatment data intelligent analysis module is used for calculating the intention value of the patient for the diagnosis and treatment item and generating a diagnosis and treatment path diagram; the intelligent decision support module is used for generating personalized diagnosis and treatment suggestions in combination with a guide and a model; the doctor-patient interaction feedback module is used for collecting patient feedback and updating an analysis weight; and the follow-up management module is used for generating a follow-up plan and relapse early warning. Basic diagnosis and treatment data and patient behavior data are fused, and diagnosis and treatment requirements are reflected more comprehensively; the dimension weight is adjusted and analyzed in real time through patient feedback, and the personalized diagnosis and treatment precision is improved; a guide rule and a machine learning model are combined to assist a doctor to quickly generate an optimal treatment scheme; through the block chain and privacy protection technology, compliance use of the medical data is ensured.

The invention relates to the technical field of health data processing, in particular to a clinical patient self-health management recommendation method and system. Obtaining multi-dimensional health data of the target patient, including a clinical measurement index set and a self-report symptom set, and performing standardized preprocessing on the multi-dimensional health data to generate standardized health data; constructing a personal health state map based on the standardized health data; performing health risk traceability analysis according to the personal health state map, and identifying a key risk node set and a risk propagation path; generating an initial health management recommendation strategy set in combination with a predefined medical knowledge base and patient personalized constraints; and dynamically optimizing the initial health management recommendation strategy set based on the patient feedback data and the health state change information, and outputting an optimized personalized health management recommendation scheme. According to the invention, personalized, precise and dynamic patient self-health management recommendation in a clinical scene can be realized.

The invention discloses a method for testing and evaluating safety effectiveness of a digital therapy product for mental diseases, which relates to the technical field of mental diseases and comprises the following steps: S1, evaluating objective indexes of the digital therapy product; s2, testing a man-machine interaction feedback mode; s3, mapping measurement of product output content and patient feedback; and S4, performing real-time adjustment and optimization. According to the method for testing and evaluating the safety effectiveness of the mental disease digital therapy product, aiming at the mental disease digital therapy product, a special testing and evaluating system is constructed, objective indexes and output contents of the digital therapy product are tested, and in the interaction process of a patient and the product, the safety effectiveness of the product is tested. By detecting data such as micro-expression, electroencephalogram signals and eye movement signals of a user in real time, feedback of a patient is obtained in real time, corresponding relation analysis is conducted on the feedback and output content of a digital therapy product, therefore, the treatment effect of the product can be accurately evaluated, and the accuracy of mental disease diagnosis and treatment is improved.

The invention discloses a chronic disease risk prediction and dynamic management system based on multi-modal data fusion, and relates to the technical field of chronic disease management, the chronic disease risk prediction and dynamic management system comprises a data acquisition layer, a data fusion processing layer, a risk prediction management layer and an application service layer, the data fusion processing layer performs preprocessing, feature extraction and fusion analysis, the risk prediction management layer constructs a risk prediction model according to result analysis, and the application service layer provides a risk prediction result, a personalized management scheme and an interactive interface service for a user. Through the arrangement of the data acquisition layer, the data fusion processing layer and the risk prediction management layer, 'symptom-physiology-image 'full-dimensional data is covered, more comprehensive health state evaluation is supported, dynamic intelligent prediction can be carried out, diet, exercise and medication suggestions are automatically adjusted according to patient execution feedback and the latest prediction result, and the health state evaluation efficiency is improved. And a self-adaptive management cycle is formed.

The invention provides a head and neck cancer patient symptom management method and system based on multi-source data fusion and AI driving, and the method comprises the steps: integrating electronic medical records, wearable equipment, questionnaire survey, popular science videos and other multi-modal data, employing an attention mechanism to dynamically fuse features, and constructing unified knowledge representation. A dynamically extended symptom-risk-intervention knowledge graph is established based on a Bayesian network and a graph neural network (GNN), and cross-layer symptom recommendation is realized in combination with a collaborative filtering algorithm. The symptom severity is evaluated in real time through an XGBoost algorithm, three-color early warning is output, and finally the knowledge graph is continuously optimized through reinforcement learning based on patient feedback data. According to the method, accurate and personalized symptom management of the patient with the head and neck cancer is realized.

A method and apparatus for generating a visual field pattern which serves as a subjective test protocol to evaluate central vision using dynamic image generation to prevent the brain from filling-in to provide better results of patient feedback and reporting of locations of visual disturbance. This enables effective central vision testing to detect the presence of visual field disruptions including scotoma, visual-neurological perceptual effects such as filling-in, and Troxler fading. The present method and apparatus can be used for vision monitoring and self-assessment, and more particularly for monitoring vision for damage or disease to the macula or retina.

The invention relates to a hospital nursing resource intelligent allocation method and device, equipment and a medium. The method comprises the following steps: generating a nurse skill assessment report based on nursing operation records, training data and patient feedback; secondly, analyzing a nursing demand dependency relationship and a time constraint in the electronic medical record, and constructing a patient demand constraint graph; further quantitatively matching the nurse skill items with the patient demand items, and generating a nurse-demand sorting result sorted according to the matching degree; and finally, through deviation analysis of actual execution data and a prediction result, continuously optimizing the nurse skill evaluation model. According to the method, the technical defects that dynamic changes of nurse skills are not captured in real time, a complex nursing demand dependency relationship is not systematically analyzed, man-post matching lacks a quantitative basis and execution feedback does not drive model optimization in traditional manual distribution are overcome, and the nursing resource utilization rate and the clinical demand response accuracy are remarkably improved.

The invention relates to the technical field of telemedicine, and particularly discloses an online inquiry method and device, computer equipment and a storage medium. According to the method, the doctor can be accurately matched according to the inquiry information and the historical medical data of the patient, the inquiry information and the historical medical data are analyzed, the to-be-examined part and the examination prompt of the patient are determined, effective reference is provided for diagnosis and treatment, manual analysis is not needed, the virtual consulting room is constructed through the augmented reality equipment, and the diagnosis efficiency is improved. Patient feedback can be obtained in time according to the patient virtual image, and the diagnosis efficiency is improved. The method is applied to an online inquiry system in the medical field, inquiry information and historical medical data of a patient can be accurately analyzed, and a doctor is effectively assisted in quickly and accurately obtaining a diagnosis result.

The invention relates to a respiration guiding system for image examination, which is applied to the technical field of image examination and comprises a respiration monitoring module, a patient feedback module, a control processing module and a physician interaction terminal, a plurality of different target breathing waveforms are pre-stored in the doctor interaction terminal, the control processing module generates real-time breathing waveforms based on breathing waveform signals, time synchronization is conducted on the target breathing waveforms and the real-time breathing waveforms, then the patient feedback module and the doctor interaction terminal are controlled to be displayed in a parallel comparison mode, and a patient is guided to adjust breathing. According to the invention, through ''physician-system-patient'' three-element closed-loop interaction, respiratory training and pre-judgment before scanning and continuous guidance and stable maintenance during scanning are realized, so that the success rate of first scanning is improved, the number of times of repeated scanning is reduced, and the radiation dose risk and medical cost of a patient are reduced.

The invention discloses a disease control state evaluation system and method based on psoriasis patient feedback, subjective feelings of patients on disease control can be captured in clinical and non-clinical scenes, and a concept framework is based on literature and doctor-patient feedback. Different from the past psoriasis PROM, the invention provides a method for comprehensively, standardly and quickly collecting'disease control 'information, and the structure content has value for clinical and scientific research. Statistical analysis shows that good clinical benefits can be obtained when the model is applied clinically, and an effective basis for individualized precise treatment is provided for improvement and self-management of psoriasis, especially chronic diseases in China.

A method for capturing patient feedback for a medical treatment includes: receiving user instructions indicating a symptom being treated, a symptom score for the symptom being treated, and an indication of a beginning of a treatment session; receiving patient feedback comprising an updated symptom score for the symptom being treated and / or absence or presence of one or more of a plurality of attributes; storing the received patient feedback and a time in the treatment session at which the respective patient feedback was received; repeating the receiving of patient feedback and storage of data during the treatment session; and receiving a user instruction indicating an ending of the treatment session, wherein an alerting module alerts a user of the computing device for input of patient feedback after a predetermined period of time has elapsed following receipt of patient feedback without a subsequent receipt of patient feedback.

The present invention belongs to the field of medical simulation and education technology. The present invention discloses a method and system for building an interactive medical simulation scene based on AI artificial intelligence, including collecting patient physiological data, medical information data and patient feedback data; preprocessing the patient physiological data and medical information data to obtain a physiological characteristic data set and a medical information characteristic data set; fusing the patient feedback data into the physiological characteristic data set and the medical information characteristic data set to obtain a medical comprehensive characteristic data set; training a medical scene prediction model, inputting the medical comprehensive characteristic data set into the medical scene prediction model, and predicting medical scene simulation data; processing the predicted medical scene simulation data to obtain data simulation similarity error; providing more realistic data support for medical training, research and decision-making, and improving the effect and reliability of medical simulation.

The present invention discloses a retinal fixation training method and device based on fundus images, belonging to the field of retinal training technology, comprising the following steps: S1: adjusting a fundus camera to obtain a real-time fundus image of a patient based on the fundus camera; S2: processing the real-time fundus image of the patient to determine a fundus feature image of the patient; S3: analyzing and evaluating the fundus feature image of the patient to determine the fundus image analysis and evaluation result; S4: performing intelligent retinal fixation training on the patient; and S5: optimizing and adjusting the retinal fixation training program based on patient feedback and suggestions. The present invention solves the problem that existing personalized retinal fixation training programs cannot be formulated for patients based on fundus images, resulting in poor retinal fixation training results for patients. The present invention can formulate personalized retinal fixation training programs for patients based on fundus images, thereby improving the retinal fixation training results for patients.

This invention provides a basic jaw pad, a dental instrument, and a preparation method. The basic jaw pad includes a first support portion and a second support portion connected to each other. After the basic jaw pad is installed in the through hole of an orthodontic appliance, at least a portion of the first support portion is located within the cavity of the orthodontic appliance for accommodating the dentition. The first support portion mates with the occlusal surface of the dentition on which the orthodontic appliance is worn. At least a portion of the second support portion is located outside the orthodontic appliance and protrudes towards the occlusal surface of the opposing dentition. The basic jaw pad has a preset basic shape, and at least the material of the second support portion of the basic jaw pad is a polished material. This invention improves the versatility of the basic jaw pad, enabling mass production. Furthermore, clinicians can make real-time adjustments and optimizations based on the target positions of the upper and lower jaws in the patient's occlusal state, the specific situation during actual wear, and patient feedback, thereby improving the orthodontic effect.

The invention belongs to the field of auxiliary medical treatment, and provides a personalized electrical stimulation treatment scheme generation system and method, and the method comprises the steps: obtaining a voice signal of a patient, and extracting voice dynamic change information; acquiring neuromyoelectric signals, heart rate signals and motion information of the patient, and extracting physiological change information; performing association modeling on the voice change information and the physiological change information to generate fusion features for representing a multi-part tremor mode of the patient; performing dynamic generation of personalized electrical stimulation parameters based on the fused features; constraining the dynamic change of the current amplitude, the stimulation frequency and the pulse width within an individually set safety threshold range, and performing protective adjustment in combination with real-time feedback of a patient; and the electrical stimulation module is used for implementing electrical stimulation on a target nerve part in the radial nerve, the median nerve or the ulnar nerve according to the parameters issued by the decision module.

The present invention relates to a health management data processing method and system based on patient dynamic feedback, belonging to the field of health management. By downloading from the chain multiple patient physical sign change curves whose recorded disease states are consistent with the patient's disease states after blockchain certification and whose recorded health management plans are consistent with the health management plans, traversing the multiple curves to statistically calculate the ratio of the number of neighboring curves to the mean and setting it as multiple confidence densities, and extracting the physical sign change curve with the maximum confidence density; during health management intervention, statistically calculating the deviation vector between the change monitoring curve of the patient feedback information and the curve in the previous section of the change to correct the curve in the subsequent section of the change to obtain the corrected curve in the subsequent section of the change; when the deviation between the corrected curve in the subsequent section of the change and the physical sign change curve is greater than or equal to the expected deviation, feeding back the inadaptability identification of the health management plan execution to the health management terminal, which solves the technical problem of being difficult to accurately process health management data to generate a health management plan that conforms to individual characteristics.

This invention provides an intelligent pre-diagnosis medical history collection system for anesthesia clinics, characterized by: a patient terminal, a physician terminal, and a control terminal; the control terminal includes a consultation module and an analysis module; the consultation module stores consultation templates for the patient terminal to access; the analysis module identifies and analyzes patient feedback information and adds intelligent tags to specific information in the patient feedback information; the patient terminal sequentially calls each sub-module, displays relevant content from the consultation template on the front end of the patient's client, and guides the patient to fill in self-reported information; the physician terminal obtains the patient form with added intelligent tags from the control terminal. This system significantly reduces the interview time for information collection, improves the efficiency and accuracy of anesthesia clinic diagnosis and treatment, and reduces the error rate caused by insufficient experience.

The invention belongs to the technical field of artificial intelligence, and discloses a multi-agent architecture-based gamification psychotherapy healing system, which comprises four agents, namely a trigger, a negative thought generation module, a guider module and a planner module, and is characterized in that the trigger is used for generating a dynamic virtual scene; the negative thought generation module is used for generating a negative thought matched with the psychological state of the patient; the guider module is used for generating a structured suggestion of the corresponding iteration process according to the dynamic virtual scene and the negative thought of the current iteration process and compressing the structured suggestion into a key abstract; and the planner module is used for generating a strategy instruction according to the key abstract of the current iteration process and the patient feedback information. According to the method, the dynamic scene is generated through multi-agent cooperation, so that a patient can more easily substitute own emotion in a virtual environment, the immersive experience is enhanced, and the common emotion is remarkably improved; feedback of a patient can be sensed in real time, scene content and role behaviors are dynamically adjusted, and a personalized chemotherapy healing path is achieved; and various psychological themes are supported, and the method can be expanded to different psychological health intervention scenes.

The invention belongs to the technical field of big data medical health, and particularly relates to a maternal and fetal congenital anomaly case tracking system based on regional big data, which comprises a data acquisition module, a data transmission and storage module, a big data analysis module, a case tracking and early warning module, a patient feedback and intervention module and a case database and cloud management module. The system can monitor the health conditions of pregnant women and fetuses in real time, generate abnormal early warning in time, effectively reduce the missed diagnosis rate and misdiagnosis rate of maternal and fetal congenital abnormalities, and perform intelligent analysis on large-scale maternal and fetal health data through the big data analysis module to find potential disease risks, thereby providing decision support for doctors, and improving the diagnosis efficiency of the maternal and fetal congenital abnormalities. Meanwhile, the system not only can monitor health data of individuals, but also can realize large-scale maternal and fetal congenital abnormality case tracking, promote reasonable configuration and allocation of regional health resources, improve the working efficiency of doctors, reduce errors of manual diagnosis and ensure early intervention of maternal and fetal health.

The invention discloses an electronic medical record analysis and personalized rehabilitation recommendation method and system based on a large model, and relates to the technical field of medical information processing and artificial intelligence. Comprising the steps of collecting multi-source medical data of a patient from a hospital information system; preprocessing the multi-source medical data; fusing the preprocessed multi-source medical data, and constructing a multi-modal patient portrait; inputting the constructed patient portrait into a pre-trained artificial intelligence analysis model, and performing multi-dimensional analysis to obtain an analysis result; constructing a personalized rehabilitation recommendation engine; based on the analysis result, generating a rehabilitation recommendation scheme through a personalized rehabilitation recommendation engine; in the execution process of the rehabilitation recommendation scheme, continuously collecting feedback data of the patient, and performing incremental learning and retraining on the artificial intelligence analysis model. According to the method, the electronic medical record can be automatically and accurately analyzed, and the personalized rehabilitation recommendation scheme is generated based on the analysis result.

The present invention discloses a method for obtaining and reproducing the trajectory of moxibustion manipulation, which relates to the technical field of moxibustion treatment. The method includes installing sensors and patient feedback devices, collecting moxibustion environment data and patient information, and dynamically adjusting initial parameters; according to the initial parameters, collecting the moxibustion manipulation trajectory of the moxibustion therapist, the changes in the moxibustion environment, and the real-time feedback of the patient, and performing multi-modal data fusion to generate a high-quality trajectory data set, training a machine learning model to obtain a trajectory learning result; combining the trajectory learning result with the moxibustion environment data and patient information to control the moxibustion robot to perform the reproduction of the manipulation trajectory; and evaluating the moxibustion effect and generating a moxibustion report. By integrating a variety of sensors and combining the moxibustion environment data and patient personalized information, the present invention greatly improves the consistency and safety of the moxibustion effect. At the same time, it can automatically adjust the moxibustion parameters according to the real-time feedback of the patient, further improving the comfort of the patient and optimizing the treatment experience.

The invention discloses a medical insurance management system and management method, and belongs to the field of medical insurance management, and the method comprises the following steps: collecting all pharmacy sales data, patient medical insurance medicine purchasing records, patient feedback data and medicine enterprise circulation data of a region, and carrying out the correlation integration processing of the data; and analyzing a price strategy and a prescription recommendation behavior of drug sales of each pharmacy, evaluating an influence degree of the pharmacy on a drug purchase decision of the patient in combination with a drug purchase record of the patient, and finally generating an individual behavior portrait of each pharmacy. According to the method, the multi-source data is collected, associated and integrated, the pharmacy behavior portraits are generated, the pharmacy portraits of the same specification are compared to find the abnormal points, the abnormal standards are formulated according to the abnormal points, regulation and settlement are performed on the illegal pharmacies, and the suspected pharmacies are manually screened, so that the medicine purchasing behavior can be accurately identified and induced, and the loss of medical insurance funds is reduced.

This invention discloses a multimodal health management method and system for TIPS postoperative care, specifically relating to the field of medical and health management technology. It involves constructing a postoperative multimodal follow-up dataset by collecting patient health consultation records, dietary protein intake records, oral medication adherence records, and doctor-patient feedback records. Through consultation topic classification, protein intake deviation identification, medication adherence breakpoint detection, and feedback tendency determination, a set of behavioral events is extracted. Within the same follow-up period, co-occurrence associations of consultation, diet, medication, and feedback events are established to generate a risk trigger chain for hepatic encephalopathy. Based on the risk trigger chain, the intervention type is determined, generating individualized intervention instructions such as dietary guidance, medication reminders, and health consultation responses. These intervention instructions are then pushed to both the patient and healthcare professionals, collecting execution and treatment feedback and writing it back into the dataset to form a closed-loop follow-up update. Finally, the updated data is used to correct the risk trigger chain and automatically generate intervention instructions for the next cycle.

The invention discloses a lung function rehabilitation device capable of conveniently adjusting respiratory resistance, relates to the technical field of medical auxiliary devices, and aims to solve the problems that in the prior art, respiratory resistance adjustment is inconvenient, and training feedback cannot be provided for a patient, the lung function rehabilitation device comprises a shell, and the shell is internally provided with a head end channel and a tail end channel which are communicated with each other; a breathing resistance adjusting assembly used for adjusting breathing resistance is arranged in the head end channel and the tail end channel, and an effect feedback assembly is further arranged in the tail end channel. Adjusting knobs on the two sides of a shell are rotated to drive a fan-shaped baffle to rotate so as to adjust a shielding area of an air conveying hole in an adjusting plate, then breathing resistance is adjusted, a testing ball moves upwards through the air pressure difference generated in a circulation pipeline and an adjusting pipe when a patient breathes, and the breathing resistance is adjusted by combining monitoring of a position sensor. Transmitting to a display device to feed back a respiratory training effect for the patient; the device has the advantages of comprehensive functions, convenience in use, good rehabilitation effect and simple structure.

The present invention discloses an intelligent analysis method and system for oral images, which relate to the field of image processing technology. Through intelligent processing and analysis of oral images, a tooth deviation index YPZ is calculated to accurately reflect the overall deviation degree of tooth arrangement and occlusal relationship. By comparing with a preset clinical statistical deviation threshold LZ, an oral recommendation evaluation plan can be generated, and the deviation index and deviation threshold are iteratively optimized based on patient feedback information. In the traditional orthodontic process, doctors rely on experience and manual judgment, which may result in subjective errors, and the adjustment process lacks a real-time feedback mechanism, resulting in a lag in the treatment effect. Through the feedback and adjustment mechanism of the tooth deviation index YPZ and the clinical statistical deviation threshold LZ, a backpropagation algorithm is used to realize dynamic evaluation of oral health status and real-time optimization of treatment plans, greatly improving the accuracy and efficiency of the treatment effect.

The invention discloses a risk layering and decision-making method based on interaction of a large language model and a risk model, and the method comprises the steps: obtaining and processing multi-modal clinical data of a patient, generating a structured feature matrix, and inputting a risk prediction model to obtain a multi-dimensional risk spectrum; retrieving evidence-based evidence from the medical knowledge base based on the risk probability exceeding the threshold; combining the feature matrix, the risk spectrum and the evidence, and generating an explanatory report through a large language model and cue word engineering; the model weight or retrieval strategy is dynamically adjusted by comparing the consistency of the intervention priority and the risk probability; performing risk layering according to the adjusted risk spectrum and generating personalized intervention suggestions; and continuously optimizing the model according to a medical intervention result and patient feedback data to form closed-loop iterative updating. According to the method, dynamic risk prediction, explanatory report generation and personalized intervention suggestion are realized, and meanwhile, the clinical application value and decision accuracy are improved based on a doctor feedback and patient data closed-loop optimization model.