6493results about "Patient-specific data" patented technology

The invention discloses a verifiable medical data query method based on a DAG block chain architecture, and belongs to the technical field of block chains and medical information management. According to the method, medical events such as diagnosis and treatment records and image reports are modeled into DAG nodes, an on-chain topology with time sequence and causal association is constructed by encrypting a hash pointer, dynamic indexing and multi-keyword verifiable query are achieved in combination with an off-chain authentication data structure, a verification object containing a matching node set and a verification path is received, and the verification object is subjected to dynamic indexing and multi-keyword verifiable query. The authenticity, integrity and timeliness verification of the result is independently completed based on the authentication root stored on the chain without depending on a third party. According to the method, through an on-chain and off-chain collaborative storage and incremental authentication updating mechanism, on the premise of ensuring privacy and tamper-proofing of medical data, query efficiency, verification performance and expandability under high concurrency are remarkably improved, communication and resource expenses are lower, and the method is suitable for large-scale popularization and application. The method is suitable for application scenes such as cross-institution medical data sharing, remote diagnosis and treatment and compliance auditing.

The invention discloses a multi-modal data fusion-based intelligent management system for a thoracic surgery monitoring unit, belongs to the technical field of medical information and artificial intelligence, and aims to solve the limitation of an existing thoracic surgery monitoring system in the aspects of multi-modal data fusion, heterogeneous data semantic alignment and intelligent deep analysis and prediction decision. The system is characterized by comprising a multi-modal data acquisition unit, a heterogeneous data fusion and semantic alignment module, an intelligent analysis and prediction decision module, a man-machine interaction and visual presentation module and a secure storage and management module. By the adoption of the technical scheme, comprehensive multi-modal data fusion, high real-time performance, deep intelligent analysis and prospective prediction can be achieved, intelligent decision support, resource optimization, continuous learning and self-adaptive optimization are provided, and the intelligent level and patient management efficiency of the thoracic surgery intensive care unit are remarkably improved.

The invention provides a man-machine collaborative intelligent writing method and system for medical documents, and relates to the technical field of man-machine collaboration.The method comprises the steps that multi-modal clinical data of a patient is obtained for cross-modal semantic alignment, reasoning expansion is conducted based on a multi-level traditional Chinese medicine knowledge graph to generate a structured dialectical framework, and the structured dialectical framework is subjected to data processing; a logic consistency verification graph of a candidate medical record fragment set is constructed to solve semantic conflicts, and a medical record generation model is dynamically optimized in combination with clinical actual curative effect data, so that the intelligence degree, the standardization level and the clinical diagnosis and treatment value of traditional Chinese medicine medical record writing are 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 provides a thyroid cancer auxiliary diagnosis and metastasis risk prediction method based on deep learning, and relates to the technical field of artificial intelligence auxiliary medical treatment, and the method comprises the steps: extracting ultrasonic image multi-scale features through a self-adaptive neural architecture search network, combining clinical examination data, fusing diagnosis and treatment knowledge through a neural symbol inference device, and carrying out the prediction of the metastasis risk. Generating a knowledge enhancement feature map; constructing a feature propagation field by using a dynamic neural field network, solving a dynamic evolution equation, and generating a spatial-temporal feature field representing the dynamic change of focus features; constructing a tumor diffusion kinetic model by using an implicit neural representation network and a nerve ordinary differential equation network, calculating a transition probability based on an optimal transmission algorithm, solving an optimal control equation, and outputting a metastasis risk prediction result of each organ; the thyroid cancer diagnosis accuracy and metastasis risk prediction reliability can be effectively improved, and doctors can be assisted in accurate diagnosis and treatment.

The invention relates to the technical field of information security management, and discloses an information security management method and system based on digital medical treatment. The method comprises the following steps: verifying doctor professional qualification, patient authorization range, data sensitivity level and time permission validity in a multi-institution medical data access request, and generating a permission verification result; analyzing ontology semantic attributes and sensitivity characteristics of medical search keywords input by a user, and determining a corresponding hierarchical encryption strategy based on the permission verification result; performing homomorphic encryption transformation on the medical term synonym set according to the hierarchical encryption strategy, and constructing a medical ciphertext database; and receiving a medical query request, executing symptom disease association matching and ciphertext state logical reasoning operation in the medical ciphertext database, and outputting an encrypted state search result. According to the method, the privacy leakage risk caused by search behavior trajectory analysis is effectively prevented, and the security and availability of medical data are improved.

The invention discloses an intelligent medical record quality control method based on a rule and large language model hybrid engine, and relates to the technical field of natural languages, and the method comprises the steps: obtaining original electronic medical record data from a medical information system; performing post-structured processing on the original electronic medical record data, and converting an unstructured or semi-structured text in the original electronic medical record data into a unified structured data object readable by a machine; traversing a plurality of quality control points in a preset quality control rule base, and for each quality control point, adaptively selecting and executing a corresponding quality control path based on the converted structured data object; and based on the quality control results of all the quality control points, performing score calculation and generating a quality control report. According to the method, the automatic and deep quality control of the full-dose medical record can be realized, form inspection is covered, connotation logic analysis can be executed, the system is endowed with excellent flexibility and regional expansion capability, and reliable technical support is provided for improving the overall level of medical quality management.

The invention discloses an AI-driven disease diagnosis and curative effect monitoring analysis system, which comprises a data processing module used for collecting multi-source diagnosis and treatment data and constructing a multi-modal sample set; the diagnosis prediction module is used for generating a diagnosis label, a diagnosis confidence value and a prediction curative effect trend through a multi-task learning model; the curative effect comparison module is used for collecting actual curative effect data, constructing a curative effect observation sequence and generating a curative effect deviation sequence based on the curative effect observation sequence and the predicted curative effect trend; the credibility evaluation module is used for executing credibility review based on the curative effect deviation sequence and the diagnosis confidence value; and the path evolution module is used for recording continuous multi-round diagnosis correction results, generating a jump type diagnosis path and updating a diagnosis and treatment data chain. The dynamic closed-loop verification mechanism between the multi-mode diagnosis and treatment data and the predicted curative effect trend improves the accuracy of disease diagnosis, the reliability of curative effect prediction and the adaptability of the diagnosis and treatment process.

The invention provides a disease course record generation method and system based on a mobile terminal and multi-modal data, and the method comprises the following steps: firstly constructing a directional collection channel and a ward round session associated with a specific patient through the signal interaction between the mobile terminal and a sickbed; in the session, multi-modal data streams with timestamps, such as voice, images and operation instructions, are obtained in real time, and audio semantic anchor points and visual semantic anchor points are extracted from the multi-modal data streams. And constructing an image-text semantic unit by calculating and associating the audio and video anchor points of which the semantic similarity exceeds a threshold. And taking the unit as a current observation state, and verifying the legality of the state transition through a finite-state machine model. And based on the image-text semantic units passing the verification, template slot filling and term standardization are carried out, and a structured disease course record data packet is generated and written back to the HIS system. The method has the effect of improving the working efficiency and quality of clinical documents.

The invention relates to the technical field of intelligent anesthesia precise regulation and control, and discloses an anesthetic dosage optimization method based on artificial intelligence. According to the method, a dynamic treatment interval is constructed, and the boundary of the dynamic treatment interval is adaptively adjusted according to the real-time sedation depth and the nociceptive stimulation level. And in the interval, performing pattern recognition on the continuous electroencephalogram signals and the hemodynamic parameters, and marking abnormal events deviating from a standard anesthesia state. And establishing a correlation network of the drug effect chamber concentration and the abnormal events, and generating a virtual drug response curve for predicting the trend of the abnormal events under different doses. Whether dose strategy reconstruction is started or not is determined by comparing the goodness of fit between the prediction curve and the actual physiological trajectory. During reconstruction, contribution weights of historical drug infusion points to abnormal events are backtracked and analyzed, and adjustment coefficients are distributed and integrated into a new infusion sequence. According to the invention, individualization and self-adaptive optimization of anesthesia administration are realized, and the accuracy and safety of anesthesia depth control are improved.

The invention provides an integrated doctor-patient cooperation and health management platform and a data processing method thereof, and relates to the technical field of health management, and the integrated doctor-patient cooperation and health management platform comprises a multi-source data access layer, a credibility evaluation and correction engine, a semantic fusion and knowledge graph layer, a patient digital twin module, a cooperation interaction module, a privacy protection and joint training module and a contract and audit layer. Dynamic modeling of health data is realized through a space-time diagram neural network, a risk prediction and intervention scheme with a confidence interval is generated in combination with Bayesian reasoning and Monte Carlo simulation, and then digital twins of a patient are constructed and personalized simulation is performed; and meanwhile, the safety and performance of cross-mechanism joint modeling are guaranteed by adopting hybrid synchronous-asynchronous federal learning and a dynamic privacy budget mechanism, and the traceability and compliance of the whole process are realized through a block chain contract. The accuracy and transparency of medical data processing can be remarkably improved, close cooperation between doctors and patients is promoted, and comprehensive health management of the patients is achieved.

The invention relates to the field of clinical terms and the technical field of digital coding, and discloses a data processing method for ICD coding recommendation of outpatient medical records, and the method comprises the following steps: carrying out the preprocessing of outpatient medical record texts, including cleaning, segmentation, coding standardization and punctuation standardization; carrying out medical entity identification and assertion identification on the preprocessed medical record text by adopting a medical entity identification model and an assertion identification model, generating structured information containing evidence fragment offset, and analyzing unstructured and spoken contents by carrying out preprocessing and medical entity identification and assertion identification on the medical record text, so as to obtain a pre-processed medical record text; key information of negative expression and time sequence dependence is captured, structured and standardized processing of medical entity information is guaranteed, the code error and missing risks in code recommendation are reduced, and the accuracy of medical quality control and medical insurance settlement is improved.

Disclosed is a system and method for standalone automated patient referral management. The system comprises an intake module that accepts and processes referrals from diverse referral communication channels as entry points for patient referrals. A processing module undertakes the analysis of patient information to pinpoint a healthcare provider based on a healthcare need, a preference, and a schedule availability. A scheduling module confirms the appointment, and engages in dynamic three-way communication between the patient, the referring entity, and the healthcare provider. A feedback module dispatches feedback to the referring entity, offering a report on an outcome of the appointment. A natural language processing (NLP) module extracts information from conversational speech. The system utilizes algorithms to match patients with the healthcare providers based on criteria including specialty, availability, and patient preferences. A feedback module communicates with the originating EHR systems and provides updates to maintain continuity of care.

A personalised artificial-intelligence (AI) case-manager platform provides real-time, policy-constrained clinical decision support. It ingests heterogeneous data from electronic-health records (EHRs), connected medical devices, and clinician inputs; fuses them with a traceable, multilingual reasoning engine; and screens every candidate action through a multi-tier policy-constraint layer that respects patient-consent artefacts, safety grammars, and jurisdictional rules. Dual-factor credential verification and dynamic, role-based access control secure all protected-health-information (PHI) transactions, while a cryptographically chained audit trail—keyed by a global trace identifier—records inputs, rules, overrides, and triggered workflows. Authorised feedback is adjudicated and fed to adaptive learning modules that tune patient-specific and population-level behaviour. A conflict-detection service flags discordant data streams, and a governance-validated workflow engine can issue proactive alerts, automated record updates, or human-in-the-loop escalations. The platform thus delivers explainable, equitable, and continuously learning automation without compromising privacy or clinical accountability.

The invention relates to the field of medical health and artificial intelligence, and discloses a cancer patient low-molecular heparin individualized dose dynamic optimization method based on reinforcement learning, and the method comprises the steps: S1, collecting multi-dimensional clinical data of a cancer patient, carrying out the preprocessing, carrying out the enhancement of the preprocessed data, and generating time sequence synthesis data; s2, based on the multi-dimensional clinical data and the time sequence synthesis data, constructing a reinforcement learning environment, including defining a state space, an action space and a reward function; s3, constructing an environment adaptive deep reinforcement learning EA-DRL model as a dose optimization model; performing model training and optimization in the reinforcement learning environment until convergence and obtaining an optimal dose decision strategy; and S4, inputting real-time dynamic clinical data of a patient to be decided into the trained dosage optimization model, and outputting individualized low molecular heparin LMWH administration dosage recommendation. Individualized precise administration is realized, and the treatment effect and the clinical medication safety are improved.

The invention relates to an intelligent medical record generation and quality control method and system based on a large model, belongs to the technical field of medical information and artificial intelligence, and solves the problems of low writing efficiency and non-uniform quality of medical records in the prior art. Comprising the following steps: constructing a medical field knowledge enhancement large model which has the capabilities of intention recognition, medical record generation, term standardization check, format integrity check, information extraction and logic consistency check; when input information of a to-be-generated medical record is received, generating a complete medical record through intention recognition and medical record generation capability of the medical field knowledge enhancement large model and the constructed medical record template library and medical database; and when a medical record to be subjected to quality control is received, quality control suggestions are generated through the capabilities of term standardization check, format integrity check, information extraction and logic consistency check of the medical field knowledge enhancement large model and the constructed medical knowledge graph. And efficient generation and quality control of the medical record are realized.

The invention discloses an electronic medical record data synthesis method based on semantic processing, and relates to the technical field of medical informatization, and the method comprises the following steps: S1, constructing a probabilistic medical knowledge graph; s2, generating a semantic representation vector; s3, constructing a multi-dimensional dynamic space-time atlas; s4, generating a discrete personalized disease course event sequence with space-time coordinates; s5, taking the discrete personalized disease course event sequence and the corresponding medical entity semantic representation vector as condition input, guiding the improved TSDiff model to execute an iterative denoising process, and outputting a multi-dimensional random disease course trajectory; s6, forming multi-modal electronic medical record data; and S7, performing multi-dimensional quality evaluation on the multi-modal electronic medical record data. According to the method, the limitations of logic inconsistency, modal splitting and model capability solidification in a traditional synthesis method are overcome, and an efficient and accurate solution is provided.

The invention relates to the technical field of report optimization, in particular to a user medical history-based physical examination report conclusion priority adjustment method, which comprises the following steps of: acquiring multi-source data, and preprocessing the multi-source data to obtain a structured multi-source data set; based on the structured multi-source data set, mining an association relationship between health data and diseases through association analysis, constructing a health trend prediction model in combination with a deep Q network, and obtaining a multi-dimensional diagnosis analysis result; determining a diagnosis conclusion priority in combination with a multi-dimensional diagnosis analysis result and a priority calculation formula; calculating a comprehensive risk score through the data fusion score, and dividing risk levels according to the comprehensive risk score to match corresponding stage intervention strategies; and inputting a multi-dimensional diagnosis analysis result, intervention strategy execution feedback data and user health preference into a comprehensive diagnosis decision model, and obtaining a differentiated health association strategy in combination with a diagnosis conclusion priority. According to the scheme, the severity of the disease is accurately identified by dynamically adjusting the priority.

The invention discloses a Chinese electronic medical record data processing method and system based on a large language model. The method comprises the following steps: acquiring a Chinese electronic medical record, and performing data preprocessing to obtain a preprocessed Chinese electronic medical record; inputting the preprocessed Chinese electronic medical record into a multi-agent based on the fine-tuned large language model, and combining the multi-agent with the input data according to different processing tasks of the multi-agent to obtain a first cue word; retrieving a medical extraction knowledge base according to the first cue word to obtain corresponding medical knowledge, and combining the first cue word to obtain a second cue word; each agent calls the fine-tuned large language model according to the corresponding second cue word, information extraction and standardization processing are carried out, and extracted medical information is output. According to the method, the accuracy of medical entity and attribute extraction can be improved, combined extraction of complex medical entities and attributes thereof is achieved, standardized mapping of medical terms and time expressions is completed, and the term ambiguity and fuzzy time analysis problems are solved.

The invention provides a cardiovascular trend prediction method and system based on time sequence medical health data, and relates to the technical field of medical health data analysis. The method comprises the following steps: collecting time sequence medical health data of a patient through a multi-source sensor, wherein the time sequence medical health data comprises dynamic physiological indexes such as heart rate, blood pressure and oxyhemoglobin saturation; performing time calibration and feature extraction on the acquired multi-source data; constructing a time sequence feature model based on the time sequence features, and fusing the time sequence feature model with the static information of the patient to generate a comprehensive feature vector; using a deep learning model to train historical data, and learning a dynamic change rule of cardiovascular health indexes; predicting the change trend of the cardiovascular health indexes in real time, and evaluating the risk level of cardiovascular diseases; the model parameters are optimized through online learning, the prediction precision is improved, the change trend of cardiovascular health indexes can be predicted in real time, and support is provided for early discovery and personalized medical treatment of cardiovascular diseases.

The invention relates to the technical field of newborn medical big data risk assessment, and discloses a newborn disease risk assessment method based on big data analysis. The method comprises the following steps: acquiring an initial multi-modal health record of a newborn from a multi-heterogeneous medical data source; carrying out fusion analysis on the data, and constructing a risk assessment map driven by an event; starting a continuous risk tracking process, performing hierarchical risk scanning by using the map, and identifying a stable risk cluster, an evolution risk cluster and an isolated risk signal; deducing a hierarchical risk management and control plan based on the spatial distribution and time evolution mode of the risk cluster; at the end of each period, integrating latest clinical treatment feedback and monitoring readings, and carrying out reverse calibration on entity node states and relation edge strength in the atlas; and dynamically recombining a measure execution sequence and resource allocation in the plan according to the calibrated atlas. According to the invention, dynamic and continuous evaluation and self-adaptive intervention planning of the neonatal health risk are realized.

The invention is suitable for the technical field of medical treatment, and provides a multi-modal medical data processing method and system based on wearable equipment, and the method comprises the steps: synchronously collecting a multi-modal data flow through a wearable equipment group and vital sign monitoring equipment, and carrying out the local preprocessing of the multi-modal data flow through terminal equipment; constructing a multi-dimensional state vector by using terminal equipment, performing reasoning to output a network slice selection instruction and a resource allocation strategy, and transmitting a multi-modal data stream to an edge computing node through a logic transmission channel; the edge computing node maps the semantic features and the spatial-temporal features to a joint embedding space for matching through a cross-modal time sequence alignment algorithm, generates a fusion medical event and uploads the fusion medical event to the cloud platform; and the cloud platform receives the fused medical event and generates a medical record report through a medical special multi-modal large model. According to the invention, the problems of low efficiency, data splitting, poor real-time performance, insufficient intelligence and weak privacy protection in the existing medical recording process are solved.

The invention relates to a personalized traditional Chinese medicine health-care intelligent recommendation system combined with an acupuncture theory. The system comprises a multi-modal data acquisition module, an AI diagnosis module and an acupuncture scheme generation module. According to the invention, the health information of the user is acquired through the multi-modal data acquisition module, the deep learning model fused with the acupuncture meridian theory is utilized to realize physique identification and disease diagnosis, and the personalized acupuncture health care scheme is generated based on the established acupuncture point, physique and disease three-dimensional association database. A reinforcement learning algorithm is combined with a user real-time feedback dynamic optimization scheme, the limitation that traditional acupuncture depends on physician experience is broken through, intelligentization, individuation and dynamics of acupuncture health care are achieved, the accuracy and operability of the health care scheme are improved, and the method is suitable for various scenes such as family health care and community medical treatment and has a wide application prospect. The method has remarkable innovativeness and practicability, the physique and the disease of the user are accurately identified based on multi-modal data, the acupuncture scheme adaptive to personal characteristics is generated, and the problem that a traditional universal scheme is insufficient in adaptability is solved.

A conversational artificial intelligence (AI) system is configured to engage in a multi-turn conversation with a user. The multi-turn conversation is substantially focused on a target topic. A conversation analyzer analyzes the multi-turn conversation to detect and store at least some turns in the multi-turn conversation that deviate from the target topic and instead characterize life attributes of the user. A knowledge graph constructor builds a knowledge graph for the user based on at least some turns in the multi-turn conversation that characterize the life attributes of the user. The knowledge graph translates the life attributes of the user into the user's life biography, including life chronology, life preferences, life milestones, life events, or any combination thereof. A knowledge graph applicator uses parts of the knowledge graph in a subsequent multi-turn conversation with the user by contextually interspersing portions of the user's life biography in the subsequent multi-turn conversation.

The invention discloses a personalized sleep intervention method and system based on a large language model and a multi-modal dynamic knowledge graph. The method comprises the following steps: (S1) collecting multi-modal data including physiological data, environmental data and user logs; (S2) on the basis of the data, utilizing a dynamic relation mining and conflict detection mechanism to update a multi-modal dynamic knowledge graph in real time; (S3) extracting knowledge sub-graphs related to the current user state from the knowledge graph; (S4) inputting the knowledge sub-graph and the real-time data into an LLM reasoning layer through a hierarchical prompt project, and generating personalized intervention suggestions; and (S5) converting the suggestion into an equipment control instruction through an instruction conversion engine and an MQTT protocol, and driving the smart home equipment to execute intervention. After execution, the system collects an intervention effect and reversely optimizes LLM reasoning. According to the method, the problems that LLM knowledge is lagged, interpretability is lacked and intervention cannot be closed-loop are solved, and the accuracy of sleep intervention and user compliance are remarkably improved.

The invention discloses an intensive rehabilitation treatment decision method based on virtual reality, and relates to the field of medical monitoring, and the method comprises the steps: collecting user physiological state parameters in real time, creating a cloud database, and storing the user physiological state parameters collected in real time based on the cloud database; according to the method, the user physiological state parameters stored in the cloud database are traversed, the wooden barrel model is created based on the user physiological state parameters, and the standard wooden barrel model is synchronously created. The dynamic wooden barrel model is created by collecting the physiological parameters in real time, and accurate comparison with the standard model is achieved in a normalization processing and three-dimensional space alignment mode; the communication area and the risk area can be visually recognized, the abstract limitation of traditional data monitoring is broken through, and dynamic and accurate allocation of medical care resources is achieved in combination with a mobile equipment distance scheduling mechanism.

A device to process data associated with a surgical event of a surgery may include a processor. The processor may be configured to receive multiple data streams during the surgical event. The processor may be configured to select a primary data stream based on a surgical data interface via which the primary data stream is received. The processor may be configured to select a secondary data stream based on a surgical data interface via which the second data stream is received. The processor may be configured to identify the surgical data interfaces. The processor may be configured to generate situational data associated with the primary data stream based on the secondary data stream. The situational data may indicate a medical decision-making factor of the surgical event. The primary data stream and the situational data may be sent during the surgical event.

The invention relates to the technical field of medical health monitoring, and particularly discloses a chronic disease patient physical examination data and complication nursing monitoring system based on the Internet of Things, multi-dimensional physical examination data such as vital signs and blood biochemical indexes of a patient are collected in real time through a multi-source sensing equipment network, and an individualized data stream is constructed; extracting parameter collaborative change characteristics by adopting multi-time scale analysis, and constructing a dynamic physiological characteristic spectrum; identifying the abnormal offset by calculating the deviation degree of the feature vector distribution and the reference distribution; based on intelligent matching of the spatial distribution features of the feature vectors and a clinical case library, generating structured early warning information including risk assessment and personalized nursing schemes; and finally, visual display and preventive nursing intervention of the complication risk evolution process are realized.

The invention relates to the technical field of trajectory accompanying, in particular to a schizophrenia prediction model construction method based on multi-modal subject data and artificial intelligence, which comprises the following steps: standardizing a prospective queue design and a multi-modal data acquisition protocol; carrying out synchronous acquisition and quality control on multi-modal data; carrying out dimension reduction and cross-modal alignment fusion processing on the data; and combining an improved loss function and a self-adaptive optimization algorithm to train the model, and performing verification and iterative optimization. According to the schizophrenia prediction model construction method based on the multi-modal subject data and the artificial intelligence, multi-modal standardized acquisition is taken as a core, multi-dimensional pathological support is provided for prediction through multi-modal data deep coverage and standardized acquisition, and refined preprocessing is designed for different modals, so that the schizophrenia prediction model construction efficiency is improved. In combination with modal specific feature extraction and cross-modal alignment fusion, a dynamic weighted multi-modal network is matched with multi-task learning and improved AdamW optimization, and in combination with comparison pre-training, characterization capability and training efficiency are improved.

The invention relates to the technical field of medical informatization, in particular to an MCI cognitive trajectory and bidirectional outcome dynamic prediction method based on a joint model. The method comprises the following specific steps: firstly, constructing a univariate joint model, then constructing a multivariate joint model, and then constructing a function type multivariate joint model; performing parameter estimation and statistical inference on the model; fitting evaluation is carried out on the function type multivariable joint model based on multi-longitudinal cognition and multi-state outcome; and finally, carrying out individualized dynamic prediction. According to the method, a baseline or longitudinal nerve image is processed by using a function type data analysis technology, and a change track is reconstructed; a plurality of cognitive assessments are used as longitudinal measurement indexes, covariants such as age and gender and functional nerve images are used as predictive factors, two-way outcome of MCI and occurrence time of the two-way outcome are set as survival outcomes, a joint model based on multi-longitudinal cognitive multi-state outcomes is constructed, early screening of high-risk groups is assisted, and the survival outcomes of the high-risk groups are determined. And powerful support is provided for optimal distribution of medical and medical and health resources.