1022 results about "Patient information" patented technology

A physiological patient monitoring system with a healthcare professional-facing interface is disclosed. The healthcare professional interface may only display by default the most critical non-confidential patient information. To access the full range of features in the system, clinicians can unlock the device. Once unlocked, clinicians can access all of a patient's treatment history, and all the data may be consolidated and presented intuitively.

The invention discloses a medical diagnosis intelligent decision-making system based on multi-modal data fusion, and the system comprises the steps: obtaining a medical image, biological features and electronic medical record data of a patient through a patient information collection unit, and extracting feature vectors from different modal data through a feature processing unit; the core of the system is a modal fusion engine, and the module combines a deep neural network and a Bayesian inference engine to realize deep learning and probability-dependent modeling of an association relationship between features; carrying out weight optimization on different modal features by adopting an attention mechanism through a self-adaptive feature optimizer, and generating optimized feature representation; the intelligent diagnosis analysis unit calculates the diagnosis probability based on the optimized features, performs auxiliary diagnosis in combination with a case knowledge base when the confidence coefficient is insufficient, and outputs a diagnosis report containing diagnosis categories, basis and suggestions; according to the method, multi-source heterogeneous data are fused, deep learning and probabilistic reasoning are combined, intelligent medical auxiliary diagnosis is achieved, and diagnosis accuracy and interpretability are improved.

The invention relates to the technical field of data management, in particular to an information digital management system based on an intensive care medicine department. The system is used for executing the following steps: inputting information data of a patient in the intensive care medicine department by using terminal equipment; missing field value filling processing is carried out on the information data of the patient in the severe medicine department to obtain complete data of the critical patient; performing physiological index time risk analysis on the complete critical patient data to obtain physiological index abnormal risk data; performing intelligent risk early warning according to the abnormal risk data of the physiological indexes, and generating intelligent illness state risk early warning data; performing risk intervention opportunity reasoning on the intelligent disease risk early warning data to obtain risk intervention opportunity decision data; and performing medical resource optimization adjustment according to the risk intervention opportunity decision data to obtain an individualized diagnosis and treatment aid decision strategy. Individual intervention opportunity decision making is realized through intelligent illness risk early warning in the system, and the management efficiency and the resource utilization rate of the intensive care medicine department are remarkably improved.

The invention discloses an intelligent emergency pre-examination triage system based on big data, and relates to the technical field of data analysis, the emergency pre-examination triage system comprises a data acquisition module, an evaluation index extraction module, an intelligent analysis module, a triage evaluation module, a triage decision module, a feedback interaction module and a patient information management module, all the modules are connected through electric signals. According to the intelligent emergency treatment pre-examination triage system based on the big data, the comprehensive evaluation model and the triage evaluation coefficient are integrated, and the symptoms of the patient are evaluated quickly and accurately, so that the triage level and the recommended treatment department are quickly determined, the emergency treatment efficiency is remarkably improved, the waiting time of the patient is shortened, and the patient experience is improved. According to the method, medical resources are configured more reasonably, meanwhile, more comprehensive patient portraits can be provided for medical staff according to multi-dimensional information such as medical history and examination results of patients, and making of more accurate diagnosis and treatment decisions is facilitated.

The invention relates to the technical field of skin disease health evaluation, in particular to a skin disease health evaluation system integrating multiple clinical evaluation tools. Comprising a patient information management and dynamic risk assessment module, a skin microbiome analysis module, a symptom quantification and assessment module, a physiological index monitoring module, an environmental adaptability monitoring module, an intelligent decision support module and a feedback and optimization module. The method comprises the steps of performing dynamic risk assessment in combination with a machine learning algorithm, performing quantitative assessment on the skin health state of a patient in combination with microbiome analysis and real-time physiological index monitoring, performing quantitative assessment on symptoms through an image analysis technology and a standardized questionnaire, and generating a personalized health assessment report and a treatment scheme by using a deep neural network and an optimization algorithm. Continuously improved closed-loop management is realized through a feedback optimization module; according to the invention, not only is a scientific evaluation basis provided for doctors, but also a patient can obtain customized treatment suggestions.

The invention discloses an intelligent dynamic triage system and method for emergency patients. The triage system comprises a data acquisition module used for acquiring basic information, physiological parameters and historical records of patients; the artificial intelligence analysis module generates a preliminary triage result; the Internet of Things connection module collects hospital resource data in real time; the central control processing unit is connected with each module to receive data; the dynamic grading decision-making module divides the patients into critical symptoms, severe symptoms, subcritical symptoms and common symptoms; the resource allocation module dynamically generates a resource allocation scheme according to the grading result; the diagnosis and treatment process management module tracks the whole process of the patient and updates state data; when the state of the patient changes, the system transmits data to the artificial intelligence analysis module again for evaluation, triggers the grading decision and resource allocation module, dynamically updates the triage priority and the resource allocation scheme, and achieves closed-loop feedback and continuous optimization.

The invention discloses a semantic enhancement auxiliary inquiry system and method based on a medical knowledge graph, and the system comprises a patient information receiving module, a theoretical knowledge graph storage library, an evidence-based knowledge graph storage library, a double-track diagnosis path construction module, a medical knowledge conflict judgment module, and a semantic enhancement report generation module. The double-track diagnosis path construction module inquires the received patient information in a theoretical knowledge graph storage library and an evidence-based knowledge graph storage library which are independent from each other in parallel, and a theoretical diagnosis path and an evidence-based diagnosis path are generated respectively; the medical knowledge conflict judgment module dynamically compares the two paths in real-time interaction, and performs priority judgment according to a preset medical judgment rule; finally, the semantic enhancement report generation module presents the two paths and the conflict judgment result to the user at the same time. Transparency and interpretability of the whole interrogation process are assisted, and reliable semantic enhancement decision support is provided for doctors when the doctors face complex medical knowledge conflicts.

The invention provides a patient information collection and medical record construction system and method based on multiple rounds of dialogues, and the system comprises an intelligent guide interaction module which receives the natural language input of a patient; the context-aware question and answer engine adopts a dialogue state representation method based on a graph structure to construct entities, relationships and attributes of each round of dialogue into knowledge sub-graphs; the medical record information dynamic builder monitors an updating event of the dialogue state diagram in real time; the abnormal information detection module adopts a mixed conflict detection method combining rules and learning; and the medical knowledge graph support system maps the oral expression of the patient to a standard medical term system in real time through a multi-level semantic matching strategy. According to the invention, the doctor does not need to distract the record in the inquiry process, and can pay more attention to patient observation and clinical thinking. The quality of the medical record first draft automatically generated by the system is high, a doctor only needs to perform a small amount of auditing and supplementing, and the medical record writing time is greatly shortened.

The invention relates to an advanced gastric cancer survival prediction system based on Lasso regression, Cox regression and an interpretable machine learning technology, and belongs to the technical field of medical artificial intelligence and intelligent decision support. According to the system, by collecting multi-modal clinical data (including demographic information, TNM staging, treatment modes, tumor grading and the like) of a patient, survival-related variables are screened by adopting Lasso regression and a Cox proportional risk model, and an optimized feature set is constructed. Based on the feature set, the system integrates various mainstream machine learning algorithms (such as XGBoost, Random Forest, SVM, Logistic regression and the like) to construct a prediction model, compares the performance of each model, and selects a model with an optimal effect as a main model. And hyper-parameter tuning is performed on the model through grid search and cross validation, so that the precision and generalization ability of the model are improved. An SHAP interpretability analysis method is introduced into the system, transparent interpretation is carried out on a model output result from the global level and the individual level, and the importance and directional effect of all variables in survival prediction are determined. Finally, the model is deployed on a terminal device, a doctor is supported to automatically output the survival probability and an explanation result after inputting patient information, and a reference basis is provided for clinical treatment decision and personalized management. The system has the advantages of high prediction precision, high interpretability, convenience in use, sustainable optimization and the like, is suitable for clinical aid decision-making scenes, and has good application prospects and popularization values.

The invention relates to an intensive care cross-institution collaborative data processing method, which comprises the following steps of: defining a hierarchical data model based on the intensive care field, and modeling various types of information including vital signs, clinical diagnosis and laboratory detection results of a patient according to a time sequence; a self-adaptive standardized interface is designed, and data formats from different hospitals or devices are identified and converted; real-time dynamic tags are introduced for patient data through a tagging technology; various health data, including vital signs, medical history and treatment feedback, of a critical patient are converted into a multi-dimensional health track, and sequential analysis is used for conducting continuous modeling on the condition of the patient; a deep learning and reinforcement learning method is combined to establish a disease evolution deduction engine; the disease development is predicted according to historical data, and the influence of different intervention measures on the disease is simulated to intelligently recommend a treatment strategy; and according to the dynamic deduction result, generating a plurality of treatment schemes, and dynamically adjusting the intervention strategy according to the real-time response of the patient.

The invention relates to the technical field of medical information, in particular to a hospital pharmacy dispensing information intelligent processing method and system, and the method comprises the following steps: S1, obtaining the basic data of a medicine in real time; s2, acquiring medical record information, current medication conditions and physiological parameters of the patient from a hospital information system; s3, analyzing an existing drug combination, and detecting potential drug incompatibility; s4, a medication scheme of the patient is automatically generated; s5, pushing the medication scheme and the taboo information generated in the step S4 to work terminals of doctors and pharmacists, and providing medication suggestions at the same time; and S6, continuously monitoring the reaction of the patient, and when abnormity is monitored, performing drug adjustment through an adjustment algorithm. According to the system and the method, the medicine data is updated in real time, the patient information is integrated, and incompatibility identification and medication adjustment are carried out by utilizing an intelligent algorithm, so that the safety and the efficiency of hospital pharmaceutical dispensing are remarkably improved, and meanwhile, the individuation and the scientificity of a treatment scheme are optimized.

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 described technology is generally directed towards automated and dynamic creation of a consolidated summary of medical information pertaining to a patient. Rather than a caregiver having to trawl through numerous reports and medical information, utilizing consolidated reports enables efficient review of medical information to enable expedited determination and treatment of the patient. In an example sequence of operation, a collection of patient / medical reports can be individually summarized to form a collection of summarized versions of the original reports. The summarized reports can be further collectively summarized to generate a consolidated summary. Hence, the consolidated summary is a summary of the initial collection of patient / medical records. A consolidated summary can comprise patient information, medical history, medical images, pertinent medical research, etc. A consolidated summary can be presented on an interactive display, with the respective initial patient / medical reports accessible via selectable hyperlinks presented in the consolidated summary.

The invention provides a clinical auxiliary decision-making method based on artificial intelligence and a related product, and the method comprises the steps: building a knowledge graph covering medical entities and relationships through learning authoritative medical data and medical records; a doctor inputs patient information, and data standardization and cleaning are carried out; based on the multi-dimensional information of the patient, recommending suspected diagnosis and differential diagnosis; recommending a preset examination scheme according to the suspected diagnosis and differential diagnosis results; when a doctor's advice is issued, the system checks conflicts between inspection and inspection items in real time and provides risk prompts; similar medical records are recommended based on patient clinical information, and diagnosis and treatment reference is provided for doctors. The clinical aid decision-making method based on artificial intelligence and related products have the beneficial effects that the disease list of suspected diagnosis and definite diagnosis is generated by automatically analyzing the illness state of the patient, and examination, inspection and treatment schemes are recommended in a targeted manner, so that the problems of missed diagnosis and misdiagnosis are effectively avoided.

The invention discloses a rehabilitation assisting method and device suitable for cognitive disorder treatment and a medium, and relates to medical rehabilitation. A tablet personal computer, a millimeter wave radar and the like are included, artificial intelligence technologies such as signal processing and deep learning are utilized, and rehabilitation training of patients with mild cognitive impairment caused by brain injury and cerebral apoplexy is assisted. Basic information, medical history and current cognitive conditions of the patient are recorded and collected through the tablet computer, and a rehabilitation file is generated; based on the cognitive disorder type and severity of the patient, a rehabilitation training plan is intelligently made; a patient performs rehabilitation training through a gamified training interface, and the physiological state in the training process is monitored in real time in a non-contact mode based on millimeter wave radar; a deep learning model is used for analyzing training data of the patient, and rehabilitation progress is evaluated; training and evaluation data are summarized, and a subsequent rehabilitation scheme is optimized. Full-process automatic management from patient information collection, training plan making, training implementation, rehabilitation evaluation to data analysis report generation can be rapidly and accurately achieved, and the cognitive rehabilitation efficiency and effect are improved.

The invention discloses a patient clinical information management system and method, and relates to the technical field of medical information management and data security, and the system comprises a data collection module which is used for receiving patient basic information and diagnosis and treatment records; the encryption storage module is used for establishing a patient file identification number according to the basic information of the patient and the diagnosis and treatment record, encrypting the basic information of the patient and the diagnosis and treatment record and then storing the encrypted basic information and the encrypted diagnosis and treatment record into a corresponding electronic medical record database; the semantic analysis module is used for performing semantic analysis on the diagnosis and treatment records and extracting clinical feature tags; the clinical feature construction module is used for constructing a patient similarity matrix based on the clinical feature tags obtained by the semantic analysis module, and performing clustering analysis to obtain a clinical feature group; and the treatment template generation module is used for constructing a corresponding treatment scheme template for each clinical feature group. According to the invention, the safety, accuracy and intelligent level of patient information management are significantly improved.

The invention, which relates to the technical field of nursing management, discloses a patient information safety management system for liver, gall and pancreatic internal medicine nursing, comprising a nursing information management center. The nursing information management center is in communication connection with a patient information collection module, an information classification processing module, a health trend analysis module, a risk assessment analysis module, a risk early warning planning module and an information synchronous updating module. According to the patient information safety management system for nursing in the department of hepatobiliary and pancreatic, through implementation of the patient information safety management system, medical staff can access latest and most accurate patient data in real time, and a risk assessment analysis module comprehensively analyzes the medical history, the current illness state and the treatment progress of a patient and calculates a risk assessment coefficient; in addition, by updating the medical information of the patient in real time, making and executing a personalized nursing plan, the change of the illness state of the patient is quickly responded, and the nursing and treatment scheme is adjusted in time.

The invention discloses a tumor chemoradiotherapy reaction automatic prediction system, and particularly relates to the technical field of medical automation, the tumor chemoradiotherapy reaction automatic prediction system comprises a data acquisition module, a data preprocessing module, a feature extraction and selection module, a model training module, a prediction analysis module, an early warning module and a feedback module; the multi-source data acquisition module comprehensively gathers patient information, the quality is improved through the preprocessing module, a foundation is built for the feature extraction and selection module to accurately screen key features, the data dimension is greatly reduced, and the model training efficiency is remarkably improved; the model training module applies a machine learning algorithm to deeply mine a data complex relationship, so that the prediction is more accurate and reliable; on the basis, the prediction analysis module outputs detailed chemoradiotherapy reaction prediction and provides confidence evaluation to help doctors to make decisions and select; and the early warning module is responsible for predicting, warning abnormity in time and helping doctors to plan intervention in advance.

The invention relates to the technical field of medical evaluation, and provides an evaluation system and method for potential complications of the department of cardiology, and the method comprises the following steps: S1, collecting information data of a patient, and carrying out the data verification and data preprocessing; s2, based on the obtained information, classifying types of complications which may occur on the patient; and S3, based on a complication type classification result, risk factors are extracted, and a risk factor network is constructed. By classifying the complication types, constructing the risk factor network and comprehensively considering multiple factors to calculate the risk index, compared with a traditional assessment mode, the risk of potential complication of a patient can be assessed more comprehensively and accurately, misdiagnosis and missed diagnosis caused by incomplete assessment are reduced, and the risk assessment efficiency is improved based on the risk score, the risk index and the risk type. The drug dosage or the measure intensity is adjusted through the patient specificity index, the prevention measures are dynamically adjusted in combination with the treatment time, and the pertinence and effectiveness of the prevention scheme are improved.

The invention discloses a multi-department medical sample collaborative management system and method, and relates to the technical field of medical information, and the system comprises an integrated information interaction platform which is used for achieving the real-time sharing of basic information, clinical diagnosis information and inspection reports of patients; the unique identification module is used for carrying out full-life-cycle tracking on a patient sample through a bar code or a radio frequency tag and dynamically associating the patient sample with patient information; the Internet of Things monitoring module is used for collecting and uploading temperature and humidity parameters of the sample storage environment in real time and triggering abnormal early warning; the automatic storage equipment executes sample classified storage and calling based on a platform instruction, and synchronously updates a sample state with the information system; and the data security module is used for realizing data encryption, dynamic authority management and operation traceability by adopting role-based access control and a block chain technology. According to the technical scheme, the core problems of data islands, low operation efficiency, weak safety and the like in traditional medical management can be systematically solved.

The invention relates to a personalized medical decision-making method based on artificial intelligence, and relates to the field of medical health and artificial intelligence, and the personalized medical decision-making method comprises the following steps: S1, inputting medical data of a patient in a patient information input module, including historical cases, examination reports, genomic sequences and real-time health monitoring data; and S2, based on the patient information, the patient health comprehensive evaluation module evaluates the health state of the patient, and the medical decision module outputs a preliminary treatment scheme according to the evaluation result. By collecting comprehensive medical data of a patient and comprehensively evaluating the health state of the patient based on the data, the illness state of the patient is more accurately grasped, a more accurate treatment scheme is made, individual differences, including age, physiological indexes, psychological conditions and disease duration, of the patient are fully considered, and by calculating the comprehensive evaluation score of the patient, the patient health state is comprehensively evaluated. The timeliness and effectiveness of a treatment scheme are ensured, and the rehabilitation speed of a patient is increased.

The invention discloses a lingual surface diagnostic information acquisition, diagnosis and analysis method and system, and belongs to the technical field of medical auxiliary diagnos.The method comprises the steps that an acquired lingual surface image is matched with patient information, a symptom associated lingual surface area is determined, the associated lingual surface area is subjected to priority division, and whether the acquired image meets a clear standard or not is judged; the method comprises the steps of constructing an image index evaluation model based on tongue vibration and image texture, performing tongue vibration, texture stability and water vapor fuzzy interference evaluation on an image needing to be processed, constructing a stable frame evaluation model, and importing vibration intensity, texture stability and a water vapor proportion into the stable frame evaluation model to evaluate image area stability. Image registration is carried out on the stable frame set, multi-frame registration and fusion are carried out based on an image stable region, region stability and processing information are recorded, a high-quality image for tongue picture analysis is generated, the image definition of a key diagnosis region is improved, and the accuracy and stability of tongue picture analysis are improved.

The invention relates to the technical field of patient information processing, in particular to an intelligent health management system for diabetic patients, which comprises the steps of acquiring continuous blood glucose monitoring data of patients, extracting blood glucose values, timestamps and patient identity information, establishing a blood glucose data set, performing data cleaning on the blood glucose data set, and removing missing values and repeated values. According to the method, after the data are cleaned and analyzed, the generated blood glucose fluctuation index can reveal the blood glucose fluctuation condition in a short time, and therefore the possible health risk is predicted. The fluctuation indexes are standardized and compared with a set threshold value, potential abnormal blood glucose events are recognized, and the response speed and preventability of disease management are improved. Meanwhile, the health risk of the patient can be quantitatively evaluated through calculation of the abnormal accumulated score, early warning is automatically triggered when the abnormal accumulated score exceeds a safety threshold value, and intervention is carried out in time. And the management efficiency and the treatment pertinence of diabetes mellitus are improved.

The invention relates to the technical field of neurosurgery patient follow-up visit management, and discloses a neurosurgery patient follow-up visit management system. The system comprises a patient information acquisition module, a follow-up plan generation module, a data cross validation module, a risk level judgment module and a follow-up structure output module. The system constructs an original information sequence template by extracting patient medical record text information elements; establishing an information directed path based on the template to form a follow-up visit plan path structure; extracting a path node sequence and screening cross data; matching risk labels according to the terminal node to obtain a risk affiliation label group; and finally generating a follow-up visit structure table. According to the system, structured processing, dynamic planning, data cross validation and accurate risk assessment of follow-up visit information of neurosurgical patients are realized, and scientificity and high efficiency of follow-up visit management are improved.

The invention discloses a clinical depression and melanoma patient nursing intervention system and method, which utilizes big data and artificial intelligence technology to establish a digital twinborn model according to the information, illness state, depression state and the like of a patient, collects existing cases for training to continuously optimize the model, and improves the precision of the digital twinborn model. And then similar cases are recommended by utilizing the database, the patient information and the digital twinborn model, a nursing scheme is generated, and then the nursing scheme is input into the digital twinborn model to be tested and predicted and is continuously improved. According to the mode, the efficiency of making the nursing scheme can be greatly improved, personalized customization can be carried out according to the actual condition of the patient, and the nursing effect is greatly improved. Meanwhile, the physiological data of the patient is detected by adopting the wearable equipment, whether the patient is in a depression state or in a depression mood is speculated by utilizing the physiological data, and psychological nursing is carried out through nursing intervention measures set in advance, so that the depression state of the patient is effectively improved.

The invention provides an intelligent infusion system and an intelligent liquid control method based on database dynamic feedback regulation and control, and relates to the technical field of medical equipment intelligent control. The system comprises an infusion terminal execution module, a multi-parameter monitoring module, a data transmission module, a central control server module and a dynamic database module; patient information, an infusion scheme, a threshold range and multi-dimensional data of historical records are stored through the dynamic database module, the central control server module calls the data and compares the data with real-time physiological parameters and infusion state parameters collected by the multi-parameter monitoring module for analysis, and the target infusion speed is calculated. Early warning is carried out according to the target infusion speed and the real-time monitoring parameters, accurate monitoring of the regulation and control parameters and the individual condition of the patient is ensured, the infusion speed is always in the optimal range adaptive to the physiological state of the patient, and the infusion adverse reaction caused by parameter errors and exceeding the physiological range is avoided.

The invention discloses a medical monitoring method based on multi-modal prompt interaction, which comprises the following steps: acquiring vital sign data of a patient, performing scoring and sorting according to patient information and the vital sign data to obtain patient monitoring scores, and forming a patient monitoring sequence; obtaining the service capability data, the current workload and the position data of the medical personnel, carrying out the sorting and scoring according to the service capability data, the current workload and the position data of the medical personnel, obtaining the state score of the medical personnel, and forming a medical personnel distribution sequence; according to the priority order of the patients in the patient monitoring queue, selecting the optimal medical staff in the medical staff distribution queue; and according to the patient monitoring score, generating a multi-modal prompting strategy, and according to the multi-modal prompting strategy, carrying out multi-modal prompting on the selected optimal medical personnel.

The invention discloses a case analysis method and system based on a large language model, and belongs to the technical field of case analysis, and the method comprises the following steps: S1, patient information input, S2, examination item recommendation and determination, S3, preliminary diagnosis generation, S4, case screening and diagnosis and treatment suggestion generation, S5, diagnosis and treatment process monitoring and adjustment, and S6, case report generation and viewing. By means of the powerful knowledge integration and reasoning ability of a large language model, in combination with massive historical case data and professional medical database information, multi-dimensional diagnosis thought reference is provided for doctors, the misdiagnosis and missed diagnosis risks caused by personal experience limitation are reduced, the health indexes of patients can be monitored, and the diagnosis accuracy is improved. Personalized adjustment is performed on the diagnosis and treatment plan according to the disease change and the diagnosis and treatment condition of the patient; the medical database can dynamically expand and correct contents; diagnosis and treatment plans and suggestions can be given according to diagnosis and treatment passes of previously cured patients with similar conditions.

The invention discloses a caregiver recommendation algorithm according to a matching degree, and relates to the technical field of nursing. The method comprises the following steps of patient information collection: collecting basic information of a patient, obtaining geographical location information of the patient, determining a city and a detailed address of the patient, guiding the patient to describe nursing requirements in detail, and inputting information of a caregiver through a user side information collection module; and the caregiver fills in own professional skill information which covers basic nursing skills, professional nursing specialists and skills in the special nursing field, and uploads related certificate qualification certificates. Through deep analysis and processing of the multi-dimensional demand information of the patient and the comprehensive information of the nursing staff, the intelligent matching model is used to accurately calculate the matching degree, the nursing staff highly meeting the demand of the patient can be screened for the patient, the pertinence and effectiveness of the nursing service are greatly improved, and the problems of poor nursing effect and the like caused by mismatching are avoided.

The invention relates to a gynecological postoperative intelligent rehabilitation management method and system, and relates to the technical field of rehabilitation management. Determining a basic recovery score of the patient based on the preoperative information; determining a rehabilitation adjustment coefficient based on the intraoperative information; based on the basic recovery score and the rehabilitation management adjustment coefficient, physiological feature tags of the patient are determined, and the physiological feature tags comprise a low-risk tag, a medium-risk tag and a high-risk tag; based on the physiological feature tag and the operation type, determining a rehabilitation path according to historical rehabilitation data; and when the physiological feature tags are different risk tags, determining a rehabilitation management strategy according to the rehabilitation path and based on different patient information. According to the method, quantitative indexes are adopted to carry out risk layering on the patients, the situation of one-step operation in traditional rehabilitation management is avoided, and therefore the pertinence and adaptability of a rehabilitation scheme are improved. Meanwhile, the method can improve the effective utilization rate of data.