2147 results about "Medical information" patented technology

The invention discloses an electronic medical record intelligent evaluation method based on complex quality control indexes, and relates to the field of medical information processing and artificial intelligence. The method comprises the steps that an original electronic medical record text is collected and preprocessed, and structured diagnosis and treatment information and an event sequence diagram are extracted; through prompt word chain construction and a semantic reasoning mechanism, a large language model is guided to intelligently evaluate complex quality control indexes in medical records. The complex quality control indexes comprise diagnosis basis sufficiency, treatment scheme rationality, key result and change record integrity, treatment measure integrity and causal relationship rationality. According to the method, technologies such as a medical knowledge graph, a graph neural network and semantic vector retrieval are utilized to realize external knowledge recall and causal reasoning support; a self-consistency reasoning mechanism, a self-reflection mechanism and a multi-model cross validation mechanism are introduced to improve the accuracy and credibility of an evaluation result; and finally, outputting a structured quality control report and a visual reasoning chain. According to the method, the intelligence and refinement level in a complex medical quality control task can be remarkably improved, high interpretability and practical value are achieved, and the method is suitable for application scenes such as hospital quality management, scientific research evaluation and medical document standardization.

The invention relates to the technical field of medical artificial intelligence and knowledge graph construction, in particular to a rare disease knowledge graph construction method based on modal injection and multi-modal fusion. Comprising the following steps: S1, collecting multi-modal medical information including texts, images and genes; s2, standardization processing is carried out, and a three-layer metadata structure is constructed; s3, complementing missing modal data, and performing feature extraction and unified dimension conversion on the modal data to realize representation alignment in a shared semantic space; s4, performing multi-level semantic fusion to obtain a unified fusion semantic vector; and S5, constructing a double-layer structure system rare disease knowledge graph comprising an ontology layer and an instance layer. According to the method, multi-modal medical information of texts, images and genes is selected to construct the knowledge graph of the rare disease, the application range, coverage and accuracy of the knowledge graph are improved, correspondence adaptation of rare cases during clinical diagnosis and treatment of the rare disease can be achieved, and the method has high recognition capacity.

The invention provides a collaborative reasoning method and system fusing a medical knowledge graph and a large model in the technical field of artificial intelligence. The method comprises the steps that S1, medical entities, medical relationships and medical attributes are extracted from a medical data set through a medical information extraction model to construct the medical knowledge graph; s2, monitoring the latest medical information through a medical information monitoring agent so as to update the medical knowledge graph; s3, creating a clinical decision collaborative reasoning model; s4, training and deploying the clinical decision collaborative reasoning model through the medical data set; s5, pushing the clinical decision collaborative reasoning model to a medical terminal through a federal gateway; and S6, the medical terminal inputs the query appeal carried by the query request into the clinical decision collaborative reasoning model to obtain a reasoning report. The method has the advantages that the reasoning ability, timeliness, interpretability and safety of medical reasoning are greatly improved.

The invention relates to the field of medical informatization. The invention discloses a thyroid cancer electronic medical record system based on multi-modal data fusion. The thyroid cancer electronic medical record system comprises a multi-modal data acquisition module which acquires patient texts, ultrasonic images, genes, biochemical indexes and clinical data and performs standardized calibration to generate standard data; the multi-modal feature extraction module extracts semantic, structure, mutation, change and fluctuation features of each standard data through multiple technologies; the single-mode prediction model construction module constructs single-mode prediction models of texts, images and the like based on the features and outputs results; and the multi-modal fusion prediction module fuses the single-modal model based on the deep learning framework to output a multi-modal fusion prediction result. According to the invention, multi-modal data are integrated, and the accuracy and comprehensiveness of thyroid cancer diagnosis are improved. The system ensures consistency through standardized data processing, and assists doctors to accurately judge pathological types, recommend therapeutic schedules and evaluate prognosis by means of a multi-modal feature extraction and fusion mechanism.

The invention relates to a medical information processing and intelligent data management technology, and discloses a hospital multi-source heterogeneous data integration management system and method based on an EMPI, and the system carries out the real-time subscription and batch extraction of the data of an HIS, an EMR, an LIS, a PACS, a DRG and a pre-hospital first-aid system through an interface adapter, and generates a unified main index identifier through the matching of the EMPI, and carries out the real-time subscription and batch extraction of the data of the HIS, the EMR, the LIS, the PACS, the DRG and the pre-hospital first-aid system. Field-level cleaning, standardization and semantic mapping are carried out on the free text and the structured data, an intermediate standardized record set is formed, an event graph is constructed, event cluster recognition is carried out in combination with time, clinical entities, doctor seeing scenes and department similarity, consistency judgment and EM iterative fusion are carried out on the basis of data source reliability, and event cluster recognition is carried out. And outputting a unified patient file set and a downstream service interface. According to the invention, high-precision integration, standardized management and traceable application of multi-source data are realized, and a reliable data basis is provided for clinical decision support, medical insurance management and scientific research.

The invention provides a gynecological disease diagnosis and treatment method and system fused with an electronic medical record, and relates to the technical field of medical informationization, and the method comprises the steps: collecting the multi-modal diagnosis and treatment data of a patient, and constructing a personalized medical database through a self-encoding network; deploying a plurality of agents based on a dynamic causal network, and generating an initial treatment scheme in combination with deep reinforcement learning; through real-time data acquisition and a multi-objective optimization algorithm, the scheme is dynamically optimized by fusing expert decision features. According to the invention, personalized precise treatment of gynecological disease diagnosis and treatment can be realized, the treatment effect is improved, and the complication risk is reduced.

The invention discloses a blood tumor patient clinical data analysis method and system based on a knowledge graph, and relates to the technical field of medical information processing. The method comprises the following steps: analyzing an unstructured text by utilizing a natural language processing model, and constructing a standardized entity set; aggregating laboratory indexes through a dynamic window, and constructing a time axis data set for marking a treatment stage; a standardized entity is integrated based on a blood tumor ontology model, a dynamic global knowledge graph is constructed in combination with a clinical causal relationship and a time sequence path, and chain reaction early warning is realized; based on the real-time patient sub-atlas, successful cases with the same molecular features are retrieved in the knowledge atlas to generate a candidate treatment scheme set, a graph neural network trained through an optimization algorithm is utilized to predict the complication risk of each scheme, and finally the optimal treatment scheme is evaluated and selected; according to the method, a clinical data barrier is broken through, dynamic reasoning, advanced risk early warning and personalized scheme optimization of the whole treatment cycle are realized, and the diagnosis and treatment accuracy, safety and medical resource utilization efficiency are remarkably improved.

The invention discloses an elderly disease health question and answer method based on multiple agents and a knowledge graph, and belongs to the crossing field of artificial intelligence and medical information technologies. Structured representation of medical knowledge is realized by constructing a multi-modal medical knowledge graph and through multiple entities and association relationships thereof. In combination with a domain customized large language model and a multi-agent dynamic routing mechanism, a two-stage training strategy is adopted to optimize a base model, including all-parameter pre-training and parameter efficient fine tuning based on a low-rank decomposition technology, so that the professionality and reliability of the model are effectively improved. In a multi-agent collaborative architecture, the system dynamically selects an optimal processing path according to input characteristics, including knowledge graph query, professional answer generation or external knowledge retrieval, and maintains context continuity of multiple rounds of conversations through a dynamic abstract compression algorithm. According to the method, the multi-agent collaborative architecture is combined with the knowledge graph reasoning ability, and the advantages of relation query and semantic matching are integrated through the mixed retrieval strategy.

The invention relates to the technical field of medical information, in particular to an intelligent prediction model and method for postoperative complications of anesthetized patients, and the method comprises the steps: collecting preoperative to postoperative complete-cycle clinical data of a patient through a medical data interface; analyzing operation codes to generate risk features, extracting vital sign dynamic features, and establishing a complication probability mapping relation through a multi-modal fusion network; combining the complication probability and pharmacokinetic parameters to construct an optimization model, and solving an individualized anesthetic dosage interval by using a gradient descent algorithm; vital signs are dynamically monitored in the operation, a dose re-optimization mechanism is triggered, the infusion rate is adjusted, and a closed-loop control link is formed; and generating a visual decision report. According to the method, through deep integration of complete-cycle clinical data and multi-modal feature modeling, preoperative physiological parameters, operation coding semantic information and intraoperative vital sign dynamic modes are subjected to fusion analysis, a nonlinear mapping relation between dosage and complication probability is constructed, and the risk prediction precision and individualized adaptability are remarkably improved.

The invention relates to the technical field of medical information, in particular to a medical data conjoint analysis system based on medical knowledge graph driving. The system comprises a medical knowledge graph construction module, a medical data quality evaluation module, a medical feature engineering module and a medical knowledge driven analysis module, and heterogeneous knowledge graph modeling can be performed by integrating medical clinical guidelines, biomedical literatures, a drug database and historical medical data so as to generate a medical heterogeneous knowledge graph; obtaining new medical clinical test information and carrying out delayed contradictory learning update to generate a medical dynamic update knowledge graph; the method comprises the following steps: obtaining multi-modal medical data, carrying out quality verification evaluation and medical feature engineering analysis, carrying out knowledge path joint driving analysis at the same time, generating a decision support reasoning path corresponding to medical clinical knowledge, and outputting a corresponding medical knowledge path confidence coefficient. According to the method, fusion reasoning among cross-source data can be realized by constructing the multi-modal medical knowledge graph.

The invention relates to the technical field of medical rehabilitation, and discloses a lower limb weight-bearing gait rehabilitation training system which comprises a data acquisition module, a data processing and analysis module, a patient individualized modeling module, an intelligent decision and control module, a rehabilitation execution module and a man-machine interaction and medical information interface module which are in communication connection through a network. The data acquisition module is used for acquiring multi-modal data of a patient in real time, and the multi-modal data comprises static sign data, dynamic physiological parameters, kinematics and dynamics parameters and non-motion physiological and psychological state data; and the data processing and analysis module is used for carrying out preprocessing, feature extraction and deep analysis on the original data, and outputting a structured patient individualized feature vector and an evaluation result. According to the invention, a patient three-dimensional skeletal muscle digital twinborn model is constructed through the patient individualized modeling module, and in combination with a continuous learning intelligent model library, body sign differences of different patients can be accurately adapted.

The invention relates to the field of medical informatization, and discloses an intelligent medical record generation method and system for realizing non-sensitive experience, and the method comprises the steps: carrying out the real-time dynamic capturing of a whole patient reception process, and extracting a patient physiological parameter boundary, a doctor seeing interaction node and a diagnosis and treatment environment noise region based on a medical scene semantic segmentation algorithm; acquiring a diagnosis and treatment state multi-source data stream sequence; performing cross-modal time sequence deep association matching on the diagnosis and treatment state multi-source data stream sequence; based on the multi-dimensional dynamic feature matrix, utilizing an adaptive semantic evolution clustering algorithm to extract potential semantic offset paths in a medical record generation process, and identifying high-risk information distortion candidate nodes by detecting distribution abnormity of semantic evolution trajectories; dividing the diagnosis and treatment semantic units and the influence intervals corresponding to the high-risk information distortion candidate nodes as candidate correction areas; and generating a map based on the local medical record, and performing intervention decision on the information evolution link. The method has the advantage of improving the experience feeling of the patient.

The invention discloses a special disease queue data capturing method and system based on an intelligent medical knowledge graph, and relates to the technical field of medical information, and the method comprises the following steps: S1, constructing a special disease intelligent medical knowledge graph which comprises a bidirectional mapping relation between standard terms of a single disease category and clinical actual corpora, clinical text data is accumulated in a mode of combining manual annotation and machine learning, and a domain exclusive knowledge base containing symptoms, diagnosis and examination indexes is formed. According to the special disease queue data capturing method and system provided by the invention, by constructing the special disease intelligent medical knowledge graph, bidirectional mapping of single disease specification terms and clinical actual corpora is realized, and the problem of insufficient semantic understanding when non-standardized clinical corpora are processed by a traditional method is effectively solved; the entity information in the unstructured medical data can be accurately extracted by utilizing a natural language processing model and an inference engine.

The invention provides an electronic clinical medical assessment method and system for an ophthalmology diagnosis and treatment scheme, and relates to the technical field of medical information, and the method comprises the steps: 1, carrying out the multi-scale edge detection calculation of an input ophthalmology image-text mixed medical record image, and obtaining a medical record image; the method comprises the following steps: extracting a boundary contour of a text labeling area and a hand-drawn lesion schematic diagram through adaptive threshold segmentation and morphological closed operation, and establishing a coordinate mapping table containing text block circumscribed rectangular coordinates, geometric positions of key anatomical mark points of the schematic diagram and symbol spacing characteristics; 2, constructing a two-channel attention gating network based on the coordinate mapping table, fusing the semantic features of the text region and the morphological features of the schematic diagram through a dynamic weight distribution strategy, and generating a multi-modal fusion feature matrix with a spatial alignment relationship; according to the method, through multi-modal feature fusion, triangular verification region confidence regulation and control and three-dimensional topology modeling, analysis and structured output of ophthalmology image-text medical records are realized, and the spatial alignment of diagnosis and treatment information is improved.

The invention provides an emergency first-aid medical information management method and system based on a block chain and a distribution mode, and relates to the technical field of medical information management, and the method comprises the steps: obtaining first-aid event information, generating an identification code, pushing the identification code to a block chain node through a distributed network, generating a credible record through employing a hierarchical consensus and rule engine, and storing the credible record. And determining an optimal rescue path in combination with real-time data, and triggering a rescue instruction and executing tracking through an intelligent contract. According to the invention, the safety of emergency information transmission is improved, the rescue resource allocation efficiency is optimized, and the traceability of the cross-mechanism cooperative rescue process is ensured.

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 an intelligent assessment method and device for body rehabilitation of a stroke patient, and relates to the technical field of rehabilitation intelligent assessment, and the method comprises the steps: connecting a medical information management system, and obtaining the stroke medical record information of the current patient; performing identification according to the stroke medical record information; the multi-modal sensing module is connected with the rehabilitation device, performs rehabilitation motion sensing on the limb on the affected side according to the multi-modal sensor, and outputs a multi-modal sensing data set; collecting a multi-modal sensing sample set of a healthy limb corresponding to the limb on the affected side; a rehabilitation evaluation model is constructed, the rehabilitation evaluation model evaluates the multi-modal sensing data set, and rehabilitation level indexes are obtained; the rehabilitation level indexes are fed back to the rehabilitation device for training mode optimization. The technical problems that in the prior art, body rehabilitation evaluation depends on subjective judgment, the limb function recovery condition of the patient is difficult to comprehensively reflect, rehabilitation evaluation efficiency is poor, and the result is inaccurate are solved, and the technical effect of improving rehabilitation efficiency and evaluation accuracy is achieved.

The invention provides a medical auxiliary diagnosis method and system based on time sequence and semantic weighting, and belongs to the technical field of medical information processing. Constructing the preprocessed current and historical medical record information of the patient into patient medical record data, and inputting the patient medical record data into a pre-trained medical language model to generate a preliminary diagnosis result; the comprehensive weight of the patient medical record data vector sequence is calculated through a time decay function and the content correlation weight, a medical record fusion vector is obtained through fusion, and the medical record fusion vector and the preliminary diagnosis result are spliced into a query vector; searching candidate fragments in a clinical guide knowledge base based on the query vector, calculating semantic evidence scores and coverage scores based on patient medical record data and tokens of the candidate fragments, obtaining sorting probabilities of the candidate fragments in combination with metadata prior scores of the candidate fragments, and screening out target guide fragments; and the preliminary diagnosis result and the target guide fragment are fused to generate final auxiliary diagnosis and treatment information, so that auxiliary diagnosis and treatment suggestions with traceability, verifiability and authoritative basis are provided.

The invention discloses an electronic medical record LLM generation method based on animal injury, which realizes dialogue structuring and timestamp synchronization through multistage speech recognition and role affiliation. Using standardized medical term mapping and coding to align the free text to a standardized medical entity, and constructing a high-confidence medical entity network based on a semantic anchor point pool; according to the method, context-sensitive entity relationship extraction is realized by combining a large language model and a semantic enhancement template, a high-accuracy structured relationship chain is generated through clinical logic rule set verification, and finally, an electronic medical record template under diagnosis and treatment specifications is automatically filled and privacy desensitization processing is completed. The semantic consistency, the structural accuracy and the data security of automatic generation of the electronic medical record are improved, and standardization and intelligent circulation of medical information are effectively promoted.

The invention provides an electronic medical record information extraction system and method, and belongs to the technical field of medical information extraction. The method aims to solve the problems of inaccuracy, nonstandardization and inconsistent logic when information is extracted from a free text medical record. According to the scheme, the method comprises the steps that cue words containing structured field definitions and rules are generated; inputting the cue word and the medical text into a large language model to obtain a preliminary result; calling a medical knowledge base to correct terms in the result; and performing logic verification on a corrected result, including time sequence and cross-field consistency verification, so as to generate final structured medical record information. According to the method, a closed loop is formed by guiding type extraction and multi-stage verification and by introducing a feedback correction mechanism, so that the accuracy, the normalization and the logic consistency of the medical record information are remarkably improved, and a high-quality data basis is provided for clinical application.

The invention discloses a medical health cost prediction system and method based on multi-source data fusion, and relates to the technical field of medical data analysis, the system firstly fuses a plurality of heterogeneous medical information sources to generate structured data; then, multidimensional features related to the cost are extracted to construct a feature representation vector; and the cost prediction modeling module constructs a cost prediction model through graph structure modeling and semantic embedding, carries out joint optimization by combining a graph attention mechanism and semantic similarity, and outputs a prediction result by fusing a time sequence and static characteristics. A joint optimization algorithm of graph semantic comparison loss and prediction deviation loss is introduced into model training, and positive and negative sample pairs are constructed through a cost label distance. And the feedback optimization module triggers model updating when the prediction deviation exceeds a threshold value, and dynamically adjusts a model structure and parameters through a deviation index and a sample confidence factor. And the prediction interpretation module analyzes influence factors based on intermediate layer features or gradient propagation, outputs an interpretation report, and improves the transparency and credibility of the model.

The invention provides a non-invasive electronic medical record generation method and system, and relates to the technical field of medical informatization and artificial intelligence. The method comprises the following steps: acquiring a doctor-patient dialogue audio, and extracting structured medical record information based on a natural language processing model after speaker role distinguishing and voice-to-text conversion; comparing the information with a medical record standard to check the integrity, and generating a prompt to guide a doctor to supplement inquiry when the information is lost; finally, information is non-intrusively filled in in an analog input manner by identifying a graphical user interface of an existing medical information system. The method solves the problems of low medical record input efficiency and difficulty in integration and transformation in the prior art, and has the beneficial effects of non-invasive deployment, low cost, high efficiency, capability of ensuring medical record quality and the like.

A blockchain-based system and method for secure and auditable sharing of medical image studies between providers, patients, and authorized recipients. The system has modules for verifying identities of trusted healthcare providers as issuers of medical data, managing secure storage of medical images and metadata, minting non-fungible tokens (NFTs) representing patient ownership of studies, authenticating patient consent for data sharing, and controlling access to shared data by authorized parties. Issuer registration authenticates healthcare providers permitted to submit studies by verifying credentials against authoritative sources. Secure data management employs cryptographic wallets and smart contracts to ensure only verified issuers can create new medical data NFTs on the blockchain. Automated processing extracts medical data such as image files and metadata upon upload for separate secure storage, with metadata references encrypted in the associated NFT. The system generates patient-controlled wallets for managing ownership of their medical NFTs without separate identity verification. Patient consent for sharing data is authenticated through multi-signature wallets requiring patient approval before minting study NFTs. Controlled access for other parties is gated by cryptographic proofs of authorization without exposing data. The immutable blockchain ledger records all sharing transactions initiated through patient and provider wallets, providing transparency and an auditable trail of how and when medical data is shared or accessed and by whom. The decentralized architecture promotes patient control, security, and accountability in managing sensitive healthcare information.

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 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 belongs to the technical field of medical informatization, and provides an anesthesia medication management system with biological recognition, comprising: a biological recognition module verifies the identities of medical staff and patients through a vein recognition technology; the multi-source data acquisition module acquires vital sign data and equipment parameters of a patient in real time to obtain multi-source data; the intelligent drug management module authorizes to store and take drugs according to the biological recognition result and generates an electronic anesthesia prescription; the processing decision module receives the multi-source data and generates a medicine adjusting instruction through a preset rule and an algorithm in combination with an electronic anesthesia prescription; the execution control module carries out secondary confirmation on the medicine adjusting instruction through biological recognition and then sends the medicine adjusting instruction to anesthetic infusion equipment to be executed; according to the method, through the electroencephalogram double-frequency index, the entropy index and the hemodynamic parameters, in combination with the anesthesia depth evaluation result and the personalized drug metabolism and effect model, the medication scheme is dynamically adjusted, the optimal infusion rate is generated, the anesthesia depth control is more accurate, and the fluctuation range is narrowed.

The invention provides a DRG intelligent grouping method and system based on a large model, and relates to the technical field of information processing, and the method comprises the steps: obtaining a medical record text of a patient, extracting medical information through a pre-training language large model, generating a structured disease abstract, executing multi-modal matching to generate a standard ICD code, and solving ambiguous contents through similar matching. And forming a complete coding feature, generating an initial grouping path based on a preset rule, updating model parameters through a near-end strategy optimization algorithm, and outputting a final DRG grouping result. According to the invention, the grouping accuracy is improved, the manual auditing burden is reduced, and intelligent medical grouping is realized.

The invention relates to a medical decision-making method and device based on a neural symbol hybrid model, and a storage medium, and relates to the technical field of medical information processing. The method comprises the following steps: firstly, fusing multi-modal clinical data of a target object through an attention weighting mechanism to obtain multi-modal fusion representation; then, on the basis of a medical mask enhancement mechanism of a high-risk index, associating the high-risk index of the structure perception type cross-modal attention network constructed on the basis of multi-modal fusion representation with a medical index mask to obtain a joint feature vector of the target object; and inputting the joint feature vector into a neural network decision layer to obtain a neural network recommendation vector, and inputting the joint feature vector into a medical knowledge graph to obtain a symbol rule recommendation vector. And finally, inputting the neural network recommendation vector, the symbol rule recommendation vector and the joint feature vector into a three-layer neural symbol fusion network to obtain a target decision suggestion. Therefore, the accuracy, interpretability and clinical suitability of medical intelligent decision making are improved.

The invention provides an intelligent medical information system based on multi-system integration, a data security management method and device, electronic equipment and a storage medium, and is applied to the technical field of medical data processing. According to the method, three types of information including core security elements, multi-system heterogeneous adaptation (including HIS / LIS / EMR interface specifications and the like) and full-process auditing (including operation logs and the like) are obtained. Normalizing the core security elements by using technologies such as AI multi-mode privacy identification and the like to generate a standardized security baseline file; heterogeneous adaptation information is processed through a lightweight API connector and the like, and a multi-system security access scheme is formed. Associating the baseline file with the access scheme, and generating a full-life-cycle protection strategy in a zero-trust architecture; and processing the audit information to generate a dynamic security audit report. And finally, based on a security-adaptation linkage decision engine and compliance verification, outputting a cross-system data security sharing scheme and a privacy disclosure emergency response instruction, and realizing a medical data security management closed loop.

The invention discloses an acute pancreatitis complication assessment method based on artificial intelligence, and belongs to the technical field of medical information, and the method specifically comprises the steps: firstly, obtaining a clinical data set containing real-time clinical indexes and basic disease historical records; performing time sequence analysis on the historical records of the basic diseases, extracting long-term influence characteristics of the historical records of the basic diseases and generating basic disease influence factors; carrying out standardization processing on the real-time clinical indexes and the basic disease influence factors, and generating a comprehensive feature vector after calculating association weights among features; inputting the vector into a trained artificial intelligence evaluation model, and outputting a complication risk probability through layer-by-layer nonlinear transformation; and finally, mapping the risk probability value into a risk level, and integrating patient information to generate a structured risk assessment report. And an acute pancreatitis complication evaluation scheme integrating the acute stage index and the chronic basic disease influence is established.