222 results about "Medical advice" patented technology

The invention provides a medical decision support method and system based on big data. The method comprises the following steps: integrating and fusing patient information from different medical institutions, personal health monitoring equipment and a public health database to obtain a dynamic update library; a graph neural network algorithm is applied to analyze the relation between the patients and the relation between the patients and the environmental factors in the dynamic update library so as to predict the individualized disease development trend, and dynamic risk early warning signals are generated; based on the dynamic risk early warning signal, through feature importance analysis and a local interpretable model, revealing key factors and an interaction mechanism influencing disease progression, and applying a health economics evaluation method to generate a medical suggestion report; and in combination with the medical suggestion report, establishing a dynamic learning and feedback mechanism, optimizing personalized suggestions in the medical suggestion report, and constructing a medical decision support system. According to the technical scheme provided by the invention, the credibility of medical decision making is remarkably improved.

The embodiment of the invention provides a method and system for generating medical suggestions based on multi-modal data fusion, and the method comprises the steps: integrating a medical image, a physical examination report and dynamic physiological parameters of a patient through a multi-source data fusion module, generating a multi-modal data set, and synchronously inputting the multi-modal data set into a hybrid reasoning module and a dynamic knowledge graph engine. And the dynamic knowledge graph engine accurately recall a target diagnosis and treatment guide associated with the current multi-modal data set. The rule reasoning sub-module generates a first diagnosis suggestion containing a diagnosis conclusion, a treatment scheme and an evidence level based on a guide structured rule, and meanwhile, the neural network reasoning sub-module analyzes a multi-modal data set by relying on a triple topological structure and an edge weight; and generating a second diagnosis suggestion comprising the disease risk probability, the differentiated treatment suggestion and the evidence source. And finally, the interactive output module fuses the two suggestions to generate a medical suggestion report covering the diagnosis basis, the evidence level and the treatment scheme, so that the diagnosis and treatment precision of chronic disease management and health risk assessment is remarkably improved.

The invention discloses an AI nursing whiteboard system and method based on a large model agent, the system comprises a nursing whiteboard terminal, an AI intelligent chain platform and a database, the nursing whiteboard terminal comprises an AI nursing assistant, and a whiteboard control system is arranged in the nursing whiteboard terminal. Through the functions of integration of multi-source data, intelligent early warning, accurate question and answer and the like, various diagnosis and treatment data, such as vital signs, medical advices, nursing records and the like, of a patient can be rapidly integrated and presented in a clear and visual mode, a large amount of time and energy are saved, the patient can rapidly know the condition of the patient, and the diagnosis and treatment efficiency is improved. Vital signs and illness state changes of a patient can be monitored in real time, early warning can be immediately given out once abnormal conditions such as heart rate and blood pressure exceeding normal ranges occur, a nurse can know the abnormal conditions in the first time and take corresponding measures, the response speed to emergency situations of the patient is greatly increased, and when the nurse encounters a question in the aspect of medical knowledge, the nurse can timely take corresponding measures. Inquiry may be made by an AI care assistant.

The invention discloses an old people asthenia phenotype evaluation and nursing plan making method and device and a medium, and relates to the field of asthenia evaluation.The method comprises the steps that physiological health data, psychological state data and social interaction data of old people to be evaluated are collected, key features are extracted, and a plurality of key indexes are obtained; generating a weakness phenotype evaluation report (including a physiological evaluation result, a psychological evaluation result and a social interaction evaluation result) of the to-be-evaluated old people according to the plurality of key indexes; generating a nursing plan (including medical suggestion measures, psychological support suggestion measures and social activity suggestion measures) of the to-be-assessed old people according to the weakness phenotype assessment report; and nursing the to-be-evaluated old people based on the nursing plan, collecting health feedback data according to a set period, and optimizing and adjusting the nursing plan according to the health feedback data. According to the method and the device, the efficiency and the precision of asthenia phenotype evaluation of the elderly are improved, the personalized nursing plan is generated, and the nursing effect of the elderly is improved.

The invention discloses an online medical inquiry method based on language reasoning analysis of a large language model. The method comprises the following steps: acquiring doctor seeing information of a patient; constructing a medical knowledge base and a drug database; carrying out keyword extraction on the doctor seeing information of the patient based on the medical concept to construct an inference tuple database containing symptom elements, diagnosis elements, complication elements and treatment elements; and constructing a corresponding semantic network by taking each element in the reasoning tuple database as a node and taking a medical reasoning direction as a relation edge, and forming a reasoning model by the semantic network and a pre-trained large language model. The invention further provides an online medical inquiry system. According to the method provided by the invention, the medical concept can be fused into the man-machine interaction process to optimize the logicality of the inquiry result, so that more reliable medical suggestions are provided for the user.

The invention discloses a medical insurance intelligent auditing and path recommending method and system based on a large language model, and particularly relates to the technical field of medical insurance auditing, and the method comprises the steps: S1, analyzing massive medical insurance policy documents through a first LLM, and generating a comprehensive confidence coefficient in combination with triple confidence coefficients of semantics, context support degree and timeliness, after grading processing, constructing a dynamically updated medical insurance rule knowledge base; s2, converting free text medical advices of doctors into structured data by means of a second LLM, quantifying the integrity of the medical advices through field weights, and dynamically asking and complementing information in combination with constraint urgency; s3, associating the diagnosis and treatment scheme with the medical insurance information of the patient, matching rules in real time, and generating a color identifier for early warning violation; s4, recommending compliance alternative schemes in combination with the medical knowledge graph, and sorting according to multi-dimensional scores such as medical insurance economy and the like; and S5, automatically extracting multi-system data, and generating a standardized medical insurance declaration report. The medical insurance management efficiency is improved, the burden of medical care and medical insurance departments is relieved, and reasonable use of medical insurance funds is guaranteed.

The invention discloses a multi-role collaborative management architecture based on RBAC + HIS synchronization, and the architecture comprises a role authority management module which builds a static role authority baseline based on an RBAC model, and associates the real-time organization architecture data of an HIS system through a dynamic rule engine, and generates a multi-level role authority tree; the data synchronization middleware adopts an API gateway to realize one-way data push between the HIS system and the psychological assessment platform, comprises a field-level encryption unit and an incremental data verification unit, and supports isolated transmission of patient medical records, medical advices and assessment tasks according to inpatient areas; the multi-terminal cooperation engine realizes authority mapping of the doctor workstation, the mobile nursing terminal and the patient self-service terminal through distributed session management, a conflict detection mechanism is built in, and a secondary authentication process is triggered when cross-inpatient-area operation is detected. According to the invention, dynamic authority management is realized, data secure transmission and multi-terminal cooperation are guaranteed, auditing tracing is enhanced, and business continuity when the system is abnormal is ensured.

The invention discloses a mobile rescue intelligent management method based on RFID automatic identification and AI artificial intelligence interaction, and belongs to the field of mobile rescue intelligent management. According to the invention, the problem of low efficiency of medicine and material inventory management of the rescue carriage in an inpatient area due to an existing manual inventory mode is solved, batch accurate identification can be carried out by adopting an RFID remote identification technology, batch inventory is realized to replace one-by-one checking, the inventory efficiency is effectively improved, and the inventory management cost is reduced. Manual electrocardiogram data checking is replaced by image visual recognition of electrocardiogram data, secondary recording is not needed, rescue data can be automatically recorded, rescue efficiency is improved, medical advice recognition is assisted by AI voice instead of manual recording of medical advice, the workload of nurses is reduced, the workload of secondary recording is reduced, and rescue efficiency is improved. And through a rescue scene full-process digital AI closed loop, digital tracing can be completed in the rescue process, and the problems that traditional handwritten records and data are not real-time and inaccurate are solved.

A cardiovascular disease early warning and intervention system based on a multi-modal large model comprises a user interface layer, a multi-modal information module, a natural language processing module, a medical knowledge base, a data management module, an intelligent recommendation module, a doctor assistance module, a data analysis module and a safety and privacy protection module. Through fusion of multi-modal data and application of a deep learning algorithm, the system can provide personalized medical suggestions and health management schemes, significantly improve the quality and efficiency of medical services, help users to better manage their health, help doctors to diagnose and treat, and improve the working efficiency and diagnosis accuracy of the doctors; in a word, the cardiovascular disease early warning and intervention system based on the multi-modal large model realizes remarkable improvement in the aspects of understanding patient demands, personalized suggestions, data analysis, knowledge base integration and the like, can effectively improve the efficiency and quality of medical follow-up visit, and provides powerful support for early discovery and intervention of cardiovascular diseases.

The invention relates to the technical field of digital data processing, in particular to an intelligent triage and guide analysis method, device and system based on a large language model.The method comprises the steps that symptom information of a patient is collected in a text input or voice input mode, and the symptom information and patient identity information are associated; cleaning the collected symptom information and extracting a symptom keyword group; matching the extracted symptom keywords with data in a knowledge graph to determine a to-be-selected disease group; generating a corresponding recommended department table according to the to-be-selected disease group, and arranging the departments according to the priority; and correcting the arrangement sequence of the departments based on the historical treatment records of the patients. Through the steps, the symptom information of the patient can be efficiently collected and processed, and accurate and personalized medical suggestions can be provided. According to the method, the efficiency and the quality of medical services are greatly improved, and meanwhile, the medical experience of the patient is also improved.

The invention relates to the technical field of intelligent inquiry, in particular to a hospital intelligent inquiry method and system based on natural language processing, and the method comprises the following steps: based on the input text of a patient, analyzing the relation between text content and medical terms, identifying key symptom description, carrying out term adaptation, correcting context errors, and obtaining a context adjustment result. According to the method, the adaptation degree and correlation of medical terms are evaluated through accurately analyzed data, it is ensured that deduced medical suggestions are highly matched with patient symptoms, in the aspect of physical parameter analysis, through real-time monitoring and fluctuation analysis of physiological indexes of patients, health risks can be recognized earlier, medical responses can be adjusted in time, and the medical treatment effect is improved. Not only is the workload of medical staff relieved, but also the individuation level and efficiency of medical services are improved, and remarkable advantages are provided for accelerating the inquiry process and accurately positioning the health problem.

The invention relates to an intelligent oral disease risk prediction and analysis system, which comprises an oral health data acquisition module, a data preprocessing and standardization module, a multi-modal data fusion module, a risk prediction and suggestion module and a medical interaction interface module, the data preprocessing and standardization module is used for preprocessing original data and performing standardization operation, the multi-modal data fusion module is used for multi-modal data fusion, and the risk prediction and suggestion module is used for oral disease risk prediction and generation of personalized medical intervention suggestions. And the medical interaction interface module is used for checking a prediction report by a user, receiving medical suggestions and providing feedback information. According to the intelligent oral disease risk prediction analysis system, a multi-modal oral data feature extraction algorithm based on a convolutional neural network is proposed to perform feature extraction on data; and an oral disease risk prediction algorithm based on machine learning is provided to predict the oral disease risk.

The invention discloses a safe medication system and a safe medication method for elderly patients, which can monitor whether the elderly take medicine according to medical advice or not and monitor the health state of the elderly. The physiological data of the patient is monitored by the wearable end, so that the health state of the patient can be roughly judged by combining data such as heart rate, electrocardio, respiratory rate and blood pressure, and whether the patient is in danger or not can be found in time; and meanwhile, whether the drug effect of the patient is exerted and whether the patient has drug safety can be known by monitoring the physiological data for a period of time after drug use. According to the invention, the medical advice is uploaded or issued through the medical care terminal, and the medication information in the medical advice is analyzed into the medicine type counting quantity and the medication mode of the medication, so that the medication plan of the patient is planned, and then the patient is prompted to take the medication on time according to the plan. And the medicines are distributed through the intelligent terminal module, and the intelligent terminal module communicates through the system, so that the medicines can be distributed according to the control of the medical care terminal, the management terminal and the system to prevent the patient from eating by mistake, and the medication safety is greatly improved.

One or more embodiments of the invention provide a medical examination report interpretation method and device based on a large language model, and the method comprises the steps: obtaining a to-be-interpreted medical examination report image, and carrying out the optical character recognition of the medical examination report image, identifying a medical examination report text from the medical examination report image; performing information extraction on the medical examination report text to extract a target text related to a medical examination result from the medical examination report text, and organizing the target text into a structured text according to a preset structured text format; and inputting the structured text into a large language model, and reasoning based on the structured text by the large language model to generate a medical suggestion text corresponding to the medical examination result.

The invention relates to the technical field of wisdom medical treatment and artificial intelligence, and discloses a health suggestion report generation method and device, computer equipment and a storage medium, which are applied to a health assessment and health suggestion system of wisdom medical treatment, and the method comprises the following steps: collecting multi-dimensional time sequence health data of a user; preprocessing the multi-dimensional time sequence health data to generate a structured time sequence feature text; identifying an abnormal mode label based on the structured time sequence feature text through a lightweight language model; performing knowledge matching and knowledge adjustment on the basis of the abnormal model label through the associated medical knowledge base, and generating target health description information and target medical suggestion information; and generating a health suggestion report according to the abnormal mode label, the target health description information and the target medical suggestion information. The method has the advantages that the health assessment accuracy is improved, the personalized suggestion generation capability is enhanced, and the dynamic threshold adjustment is realized.

The invention relates to the technical field of intelligent hospital guidance, and discloses an intelligent medical decision and intervention method and system based on a multi-source data fusion large language model. The method specifically comprises the steps of collecting medical dialogue data from real dialogue data of a plurality of medical centers, establishing a database, and performing fine adjustment on a pre-trained large language model based on the medical dialogue data to obtain a large language model in a medical dialogue scene. The real-time medical dialogue data is analyzed according to the large language model in the medical dialogue scene, the doctor's advice content is generated, the mood and frequency of the doctor's advice content are adjusted according to the illness state and the emotional state of the patient, the capabilities of the large language model in the aspects of semantic comprehension, natural interaction and regionalization adaptation are remarkably improved, and the user experience is improved. A more efficient, accurate and personalized solution is provided for communication between the patient and the medical staff, communication obstacles between the patient and the medical staff can be relieved, and powerful technical support is provided for intelligence of medical services.

The invention discloses an artificial intelligence-based whole doctor's advice reasonable medication auditing method and platform, and the method comprises the steps: integrating the structured data and unstructured text of a patient, carrying out the preprocessing according to a time sequence, determining a physiological safety range, and constructing a panoramic taboo doctor-seeing portrait; after deep information extraction and multi-source data fusion, a panoramic image of a patient is formed and is subjected to taboo comparison with medicine advised by a doctor, and a hard interception warning is triggered to avoid medicine conflict; further establishing a digital twinborn model, setting medical advices subjected to taboo comparison as a baseline scheme, simulating dynamic changes of physiological indexes within 72 hours, and assisting doctors to adjust drug doses in combination with clinical experience; finally, an individualized medication safety baseline is determined according to the simulation curve, and accurate simulation, dynamic early warning and scientific decision making are achieved. According to the scheme, the treatment safety and accuracy are remarkably improved, full-process intelligent medication auditing support is provided for complex patients, and the scheme has important clinical application value.

The invention provides an innovative personalized medical decision support system, and aims to generate highly-customized medical suggestions by comprehensively analyzing real-time physiological data and historical medical records of a patient in combination with latest clinical guidelines and expert experience. The system adopts an advanced data acquisition technology to monitor patient conditions in real time, uses a data warehouse technology to integrate historical medical data, and deeply mines data values through machine learning and deep learning algorithms. Besides, the system has adaptive learning ability, and can continuously optimize the decision model according to new medical data and user feedback, so as to realize more accurate and timely medical decision support. The design of the system not only improves the quality and efficiency of medical services, but also provides more personalized medical experience for patients, and is expected to bring revolutionary change in the medical field.

The invention provides a hospital adverse event risk identification method and system based on causal reasoning, and the method comprises the steps: responding to an operation request of a user for reporting a hospital adverse event based on the optimization of medical advice data processing and a medical quality management process in an operation risk identification scene, inputting the detailed information of the event through a formatted form, and carrying out the operation of the event. Through multi-node, multi-auditor and multi-department cooperative auditing, an adverse event form reported by a user is subjected to approval, flow and summary analysis, medical advice data is subjected to structured storage by adopting visual enhancement OCR, dynamic knowledge graph verification and a time sequence causal reasoning model, a time window division algorithm is adopted, and medical advice data is obtained. The causal discovery algorithm constructs an unplanned re-operation association network, early warning of adverse events of operation complications is carried out, and compared with a traditional mode, the medical advice processing efficiency, the transcription error rate and the unplanned operation detection rate are all greatly improved.

The invention relates to an automatic configuration system for vein clinical drugs and an application method thereof, a mechanical arm is configured in a vein configuration center for taking and delivering drugs, after a doctor's advice is issued, the doctor's advice for dispensing is analyzed, and basic information of the vein clinical drugs to be configured is obtained; the storage address ID of the to-be-configured vein clinical medicine in the corresponding configuration unit is searched by the medicine storage address library, the mechanical arm background control system generates a corresponding mechanical arm working instruction according to the storage address ID, and a medicine taking mechanical arm of the vein configuration center takes the medicine and places the medicine on a preset workbench; the medicine feeding mechanical arm conveys the medicine on the workbench to a default medicine taking point. Therefore, the mechanical arm can automatically take and convey the medicine according to the doctor's advice. By means of automatic medical advice reading and execution, the situation that medicine allocation errors are caused by fatigue and carelessness of workers can be avoided, and by means of the automatic medicine dispensing mode, the manual medicine dispensing intensity can be reduced, and the automatic medicine dispensing efficiency can be improved.

An intelligent medical follow-up visit system based on a large language model comprises a user interface layer, a voice recognition module, a natural language processing module, a medical knowledge base, a data management module, an intelligent recommendation module, a doctor assistance module, a data analysis module and a safety and privacy protection module. Personalized medical suggestions and health management schemes are provided for the user, the quality and efficiency of medical services are improved, the user is helped to better manage own health, doctors are helped to make diagnosis and treatment suggestions, and the working efficiency and accuracy of the doctors are improved; the system ensures that personal health data of a user is fully protected through the security and privacy protection module, and the personal health data conforms to related privacy laws and regulations, in short, the intelligent medical follow-up visit system based on the large language model is remarkably improved in the aspects of understanding patient requirements, personalized suggestions, data analysis, knowledge bases and the like, and the system has a wide application prospect. The efficiency and quality of medical follow-up visit can be better improved.

The invention discloses a complex medical quality management and control index automatic calculation method based on an intelligent agent. According to the index semantic model construction method provided by the invention, the automatic conversion of the medical quality control indexes from a natural language to structured semantics is realized, so that the index definition has computability and mobility, the dependence of manual analysis and script configuration is eliminated, and the standardization, generalization and reuse efficiency of the index definition is remarkably improved. Through multi-source data semantic packaging and an MCP service abstraction mechanism, semantic unification and interface standardization of multi-source heterogeneous data such as electronic medical records, inspection information, disease course records and medical advice management are achieved, and a semantic data layer capable of achieving cross-system access is constructed; the problems of data dispersion, field isomerism and interface incompatibility in a traditional system are effectively solved.

The invention provides an AI-based health management method and system, and the method comprises the steps: carrying out the analysis of diagnosis data based on a preset AI large language model if the diagnosis data of a medical worker for a user is obtained, so as to generate a diagnosis instruction which is convenient for the user to understand; displaying the diagnosis description to the user; generating a health management plan based on the diagnosis data and pre-acquired physical state data of the user; and executing the health management plan aiming at the user to perform health reminding and / or health description on the user. According to the method, the deep learning of the AI large language model and the natural language processing capability are fused, and professional medical terms and detection results are automatically converted into forms easy to understand, so that doctor-patient communication obstacles are reduced, and the diagnosis and treatment efficiency is improved; and through AI personalized medication reminding and life guidance, the patient is helped to better follow medical advices, the treatment effect is improved, the disease recurrence rate is reduced, and improvement of the disease cognition level of the patient and implementation of health management are facilitated.

The invention discloses an intelligent medicine identification method and system based on image visual identification, and relates to the technical field of image visual identification, and the method comprises the steps: shooting a liquid bag and a medicine bottle, carrying out the model training, and obtaining information through scanning a two-dimensional code of a doctor's advice; identifying and comparing the liquid bag and the medicine bottle through a multi-model fusion algorithm; and uploading the identified and compared data to a server, and carrying out docking and tracing management on the data. According to the method, the adaptability of a medicine identification system is improved, manual input errors are reduced, the overall efficiency and safety of medicine management are improved, through efficient algorithm processing, the identification time is shortened, the medicine distribution and management work flow is optimized, complete data tracing historical records and statistical analysis are provided, and the medicine management efficiency is improved. Therefore, quality control and management decision making of the medical institution are facilitated, the relevance between the medicine information and the medical advice information is improved, the risk of medicine use errors is effectively reduced, and the medical service quality is improved.

The utility model relates to a medicine liquid separation type storable medicine placing frame which comprises a doctor's advice storage grid, a medicine storage box and a liquid placing basket, the doctor's advice storage grid and the liquid placing basket are installed on the left side and the right side of the medicine storage box respectively, and the liquid placing basket can be unfolded or folded. The clinical liquid preparation device has the beneficial effects that the structure is simple, the layout is reasonable, medicines and patients can be classified, placed and collected according to numbers, the medical advice execution condition can be checked, color code management is matched, the checking function is highlighted in the clinical liquid preparation process, and the phenomena of liquid placement, liquid preparation and checking by a single person are avoided; in addition, the medicine placing frame can be unfolded or folded, storage space is saved, and operation is flexible.

The invention discloses an orthopedic trauma diagnosis and treatment information processing method, and relates to the technical field of data analysis and processing, and the method comprises the following steps: building an electronic medical management system which comprises a management monitoring end, an outpatient service application end and a patient application end, and building an electronic medical record of a patient in the system, and the outpatient service application terminal generates a personalized doctor's advice. By integrating the data collection functions of the remote monitor and the patient application end, monitoring of physiological parameters of a patient is achieved, abnormal changes of heart rate and blood pressure key indexes are captured, and early warning information is generated based on abnormal execution evaluation indexes in combination with medicine use frequency and rehabilitation training duration data. The medical advice compliance of the patient is remarkably improved, the risk that the treatment effect is poor or complications occur due to the fact that the patient does not follow the medical advice is reduced, for the orthopedic trauma patient, disease deterioration can be effectively prevented through timely intervention, complications can be reduced, and the rehabilitation process is accelerated.

A system which includes an apparatus for generating radio frequency scanning data which includes a transmitter for transmitting radio waves below the skin surface of a person and a two-dimensional array of receive antennas for receiving the radio waves, including a reflected portion of the transmitted radio waves that is reflected from a blood vessel of the person. The wave signal is compared to known standard waveforms, and similar waveforms are input into a machine learning algorithm to determine one or more health parameters of the person. The system then notifies the person and / or health professionals of the person's health status. The health parameters can be used to trigger other software and hardware modules such as another measurement device, a medication scheduler or alert, medical recommendations, guidance software, a virtual assistant, or any other hardware and / or software which may help the patient or health professionals.

The invention discloses an infected case real-time monitoring system and method, and relates to the technical field of medical treatment, through analysis of a characteristic difference factor Tcyz, an asymptomatic infected patient and a normal healthy patient can be preliminarily distinguished, the analysis process is based on cross comparison of pathological data and behavior data, a potential asymptomatic infected patient can be identified, and the accuracy of diagnosis and treatment is improved. And preliminary estimation is provided for the infection risk of the patient, so that the patient is ensured to enter a to-be-audited mechanism before symptoms appear, and the risk of missed diagnosis is reduced. The natural language processing technology is used for analyzing infection classification of the patient to be verified, and the limitation of a traditional method in patient record processing is overcome. According to the NLP technology, a large amount of unstructured clinical text data such as medical records, medical advices and laboratory reports are automatically processed, relevant information can be rapidly extracted from the unstructured clinical text data, infection types of patients are preliminarily classified, and the infection possibility is judged based on a judgment coefficient Pdxs.

The invention provides an information generation method and device, an information model training method and device, equipment, a medium and a product. The method comprises the steps of obtaining a clinical data set and a standard knowledge base; for each piece of clinical data, executing the following thinking chain data generation operation: based on a standard knowledge base, generating thinking chain data from patient condition information to diagnosis information and doctor's advice information in the clinical data by utilizing a large language model technology; and for each piece of clinical data, taking patient condition information in the clinical data as input data, taking diagnosis information and doctor's advice information in the clinical data and thinking chain data of the clinical data as label data, and performing supervised training on a preset large language model to obtain an information generation model. The information generation model obtained through training of the method has the medical thinking ability, the thinking decision process of a doctor can be restored, meanwhile, the model illusion phenomenon is avoided, and the accuracy, reliability and interpretability of the information generation model are greatly improved.

The invention relates to a disease information management method and system based on multi-source data, and relates to the technical field of medical artificial intelligence, and the method comprises the steps: forming multi-source heterogeneous data through employing the obtained infectious disease data and chronic disease data, and carrying out the preprocessing with the corresponding data features as a reference, on one hand, risk prediction and clustering analysis are performed on infectious disease related data through a logistic regression model to obtain risk assessment information, and a propagation trend is predicted through the model; on the other hand, chronic disease data are predicted and evaluated through the model, a medical advice result is obtained, and finally disease control information management is conducted in combination with all the data. Therefore, a set of perfect disease prevention and control information management is realized, on one hand, more timely and accurate infectious disease detection and early warning are realized, and the efficiency and effect of major infectious disease prevention and control work are improved, and on the other hand, chronic disease management is perfected, and the problems of diagnosis and treatment errors and lagging of patient treatment consciousness caused by incomplete data are avoided.