559 results about "Electronic medical record" patented technology

The invention relates to the technical field of medical decision, and discloses a medical decision support system based on a knowledge graph, and the system comprises a knowledge graph construction module which constructs an initial knowledge graph based on a medical ontology library, and the knowledge graph comprises entities and association relationships of diseases, symptoms and drugs; the data acquisition module is used for acquiring data from an electronic medical record, wearable equipment, a medical literature library and a hospital information system and normalizing the data through a standardized protocol; the dynamic knowledge updating module is used for processing normalized data through an incremental graph neural network; a multi-source knowledge fusion module; a context awareness module; a dynamic deduction module; and a decision optimization closed loop module. And triggering a preset clinical rule in real time based on the pathological state of the patient, dynamically adjusting the intensity value of the related edge in the factor graph, and persistently storing the intensity value back to the knowledge graph, so that logic adaptation and individualized experience precipitation of general medical knowledge in a special pathological state are realized, and the individualized treatment accuracy is ensured.

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 discloses an auxiliary film reading method and system based on artificial intelligence, and the method comprises the steps: 1, collecting a pathological WSI, an electronic medical record, detection data and equipment parameters, correcting the equipment difference through adaptive dyeing normalization, and constructing a structured data package associated with an ID-timestamp of a patient; 2, developing a dynamic branch CNN, migrating teacher model knowledge through knowledge distillation, and introducing federated learning; step 3, the edge generates a thermodynamic diagram to mark a suspicious area, and the cloud outputs a structured report; step 4, constructing a normal tissue feature space by the variational auto-encoder, detecting abnormal slices and triggering expert re-checking; a reverse automatic encoder generates a pseudo-health image to compare and position a pathological area, and dynamic weight adjustment balances the federal learning convergence speed; 5, integrating the thermodynamic diagram, the gene data and the clinical indexes by a three-dimensional platform, and supporting multi-dimensional superposition display; webGL realizes browser end rendering, and NLP automatically generates a report abstract marked with a key evidence chain and is in butt joint with an international diagnosis and treatment guide.

Described herein are a system, method, and device for configuring a generative machine learning model using a syntactic interface. A system may include a user interface, a memory, and a processor configured to, using a syntactic interface displayed using the user interface, receive a syntactic interface input from a user; identify an electronic medical record (EMR) by generating an EMR database query as a function of the syntactic interface input, querying an EMR database using the EMR database query, and receiving, from the EMR database, an EMR database response; generate a prompt as a function of the syntactic interface input, generate a first generative model output as a function of the prompt and the EMR using a trained generative machine learning model and using a conversational interface displayed using the user interface, display the first generative model output to the user.

The invention discloses an intelligent auxiliary diagnosis system and method based on big data analysis, and relates to the field of intelligent auxiliary diagnosis. Original CT scanning data and electronic medical record data of a patient are processed in parallel, and a deep learning model is introduced to extract high-dimensional image features and text features; through constructing a knowledge prior relation matrix derived from clinical guidelines and expert rules, knowledge-guided cross-modal attention fusion is carried out on image features and text features so as to realize deep and accurate joint characterization of patient conditions, and then intelligent risk prediction and classification are carried out on the basis of the deep and accurate joint characterization. Thus, through deep coupling of data-driven feature learning and medical knowledge-driven logical reasoning, intelligent and logical integration of multi-modal medical information can be realized, and the accuracy and reliability of an auxiliary diagnosis model in a complex clinical scene are effectively 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 provides a traditional Chinese medicine electronic medical record natural language processing and knowledge graph construction method and system. The method belongs to the technical field of the crossing field of traditional Chinese medicine informatization, artificial intelligence natural language processing and knowledge engineering, and comprises the following steps: performing multi-granularity semantic unit division on the traditional Chinese medicine electronic medical record to generate traditional Chinese medicine text semantic unit data; constructing a multi-granularity semantic decoupling engine according to the traditional Chinese medicine text semantic unit data so as to construct a traditional Chinese medicine text semantic analysis framework; performing traditional Chinese medicine unstructured text deep analysis according to the traditional Chinese medicine text semantic analysis framework, and performing multi-dimensional semantic feature extraction to obtain traditional Chinese medicine text semantic feature data and medical record time sequence data; through multi-granularity semantic unit division and deep analysis, the unstructured text in the traditional Chinese medicine electronic medical record can be effectively converted into structured semantic data, and the ability to understand and process traditional Chinese medicine clinical information is further improved.

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 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 discloses a trusted data sharing method based on a block chain consensus mechanism. The method comprises the following steps: S1, constructing an alliance chain network; s2, completing KZG polynomial commitment generation, ML-KEM algorithm encryption storage and pointer registration; s3, the data provider medical institution completes an evidence storage transaction of the FROST threshold signature; s4, the target medical institution completes the access request transaction; s5, the data provider medical institution sends a KZG unpacking proof and a data item, the target medical institution obtains a storage pointer and a commitment value, KZG verification check is executed, and after verification is passed, an ML-KEM algorithm is executed for depacking to obtain a session key; and S6, the target medical institution completes the audit transaction, and after the alliance chain network broadcast, the consensus node executes rule verification of the access control contract and the audit contract. The multi-node electronic medical record sharing and tamper-proof evidence storage method is high in performance and traceability, and is suitable for credible circulation of sensitive health data among multiple medical institutions.

The invention relates to the technical field of reinforcement learning, and discloses a chronic disease intervention scheme generation method and system based on reinforcement learning and multi-modal data, and the method comprises the steps: obtaining the multi-modal data of a patient, the multi-modal data at least comprising electronic medical record data, voice data, image data, text data and physiological time sequence data; performing feature extraction on the multi-modal data to obtain a multi-modal feature vector; on the basis of the multi-modal feature vectors, health state vectors are constructed, and the health state vectors at least comprise a physiological risk score, a treatment compliance score and a lifestyle health degree score; designing a reward function according to the dynamic change of the health state vector; optimizing the strategy network in a predefined intervention action space according to the reward function by utilizing a reinforcement learning algorithm so as to output an optimal intervention action; and converting the optimal intervention action into personalized natural language interaction content through a generative AI model. According to the invention, the efficiency, precision and patient compliance of chronic disease management can be significantly improved.

The invention discloses a medical informatization data intelligent analysis system based on a large model, and belongs to the technical field of medical informatization and artificial intelligence, and the system comprises a data collection and standardization module, a medical knowledge graph construction module, a knowledge enhancement inference analysis module, a data quality evaluation module and a structured output module. The system collects multi-source heterogeneous medical data from an electronic medical record system, a laboratory information system, a medical image information system and a hospital information system, performs standardization processing, automatically constructs a medical knowledge graph, and performs intelligent analysis and reasoning on the medical data by using a knowledge retrieval enhanced medical field large language model. Meanwhile, the data quality is evaluated in four dimensions of integrity, accuracy, timeliness and relevance, and finally a structured analysis report is generated. The multi-source medical data can be effectively integrated, the accuracy and interpretability of medical data analysis are improved, and intelligent support is provided for clinical decision making.

The invention discloses a full-process automatic voice-driven electronic medical record generation system, and belongs to the technical field of medical information. The invention aims to solve the problems of insufficient medical term precision, low medical record structuring efficiency, lack of clinical knowledge support, data security risk and the like of voice recognition in the prior art. According to the system, a full-automatic process from voice input to structured medical record output is realized by integrating medical level voice acquisition, term enhanced voice recognition, intelligent medical record generation based on a large language model and a knowledge base, multi-modal interaction and system integration and a full-process safety compliance system adopting edge computing and block chain technologies. According to the method, the medical record writing efficiency can be remarkably improved, manual errors are reduced, medical safety and data privacy are guaranteed, and the method has wide clinical application value.

The invention discloses a structured information extraction system for a mental disorder electronic medical record based on a knowledge graph and a natural language, and belongs to the technical field of medical information processing. According to the system, firstly, core concepts such as diseases, symptoms and drugs are extracted from an authoritative guide to construct a mental disorder knowledge graph, and a standardized clinical knowledge base is established; then pre-training and fine-tuning the deep learning model on a large number of biomedical texts and desensitized medical records to enable the deep learning model to have a medical language understanding ability; performing preprocessing, named entity recognition and entity linking on the electronic medical record text, and mapping spoken expressions to standard medical terms; inference is carried out by utilizing a relation extraction model and combining with a knowledge graph to complement implicit clinical information; and finally, structured data output meeting the standards of FHIR and the like is generated. According to the method, through deep fusion of knowledge driving and data driving, the problems of insufficient semantic understanding and weak generalization ability of a traditional method are effectively solved, and the accuracy and clinical value of electronic medical record structured information extraction are remarkably improved.

According to the multi-mode speech recognition and structured electronic medical record generation system based on environmental perception, the dialogue content between a doctor and a physical examination person and the oral instruction / result of the doctor are accurately captured in a non-inductive mode and are intelligently filled into the predefined structured electronic medical record template of the corresponding department, so that the manual input time of the doctor is shortened, and the efficiency is improved. The system liberates both hands and eyes, focuses on inquiry and examination, improves the physical examination quality, and improves the satisfaction degree of physical examinees.

The invention discloses a children respiratory tract infection risk prediction method based on multi-source medical data, particularly relates to the field of respiratory tract infection risk prediction, and is used for solving the problems that an existing method cannot identify a cross-regional co-infection mode and cannot prevent an input transmission risk. According to the method, a high-incidence time period baseline is constructed by extracting children respiratory tract infection historical electronic medical records of all regions, a local co-infection mode feature library is established based on a pathogen positive case co-occurrence relation, and a region-time period-pathogen three-dimensional collaborative tensor is constructed to recognize a synchronous high-incidence region group. And capturing real-time pathogen data of the synchronous high-incidence area, matching the real-time pathogen data with a local co-infection mode to generate a real-time risk list, deducing an input co-infection mode in combination with interregional personnel circulation intensity, and calculating the cross-regional risk permeability. And finally, a risk heat map is generated based on the permeability, and regional linkage-oriented children respiratory tract infection risk prediction is realized.

The invention belongs to the technical field of medical information, and provides a standardized electronic medical record shared document automatic generation method and system, and the method comprises the steps: obtaining related specifications, constructing a document directory structure, and generating directory data; the method comprises the following steps: loading a pre-constructed standard data element dictionary and a local business system database structure, respectively analyzing the pre-constructed standard data element dictionary and the local business system database structure to obtain standard data elements and local fields, and establishing a mapping relationship between the standard data elements and the local fields through an intelligent matching algorithm to obtain a field mapping table; acquiring configuration information including node names, data types, whether the user must be filled or not and value ranges, and binding the configuration information with local fields to generate complete node configuration data; and generating a document template according to the node configuration data, generating a shared document file based on the basic information of the patient, the doctor-seeing record and the selected document template, and performing verification. According to the method, the full-process automation of the shared document from directory definition to content generation is realized, and the method has high efficiency, standard performance and intelligence.

The invention relates to the technical field of electric digital data processing, and discloses an intelligent data processing method for clinical research. The method comprises the following steps: constructing a basic clinical knowledge graph fused with an international medical ontology; the method comprises the following steps: performing deep learning driven entity recognition and knowledge graph linking on original data from heterogeneous sources such as an electronic medical record and an inspection system; executing cross-source entity alignment and knowledge fusion based on the graph attention network; performing automatic data quality verification and restoration according to clinical logic rules embedded in the atlas; and finally, extracting and generating a standardized analysis ready data set from the enhanced atlas according to research requirements. According to the technical scheme, high-quality, automatic and semantic integration and flexible delivery of clinical research data are realized, and the data processing efficiency and research reliability are improved.

The invention discloses a personalized health management method and system based on an AI electronic medical record, and relates to the technical field of artificial intelligence medical treatment, and the method comprises the steps: analyzing an original electronic medical record to generate a personal health timeline; carrying out feature extraction on the health state evolution sequence, identifying key nodes, and carrying out pathological labeling according to a medical knowledge graph to form a health state evolution sequence with a pathological label; a risk assessment model is constructed, future disease risks are calculated based on the sequence, and a dynamic report is generated; making a personalized health management plan in combination with the living habits and genetic backgrounds of the users; during plan execution, user feedback and monitoring data are collected in real time, and plan content and strength are dynamically adjusted by using a reinforcement learning mechanism. According to the method, the medical interpretability of health state evolution is enhanced through pathological labeling, and dynamic closed-loop optimization of a management plan is realized through reinforcement learning.

The invention relates to the field of medical information security, and discloses a medical conjunct data security access control system based on quantum encryption, and the system comprises a data fragmentation module which processes original electronic medical record data according to a patient identifier and a module type to obtain a plurality of scattered data units; the labeling encryption module is accessed to the quantum key distribution network to obtain a dynamically updated encryption key, and performs homomorphic encryption operation on the dispersed data units; the distributed storage module is used for generating a storage topology constraint condition containing a logic adjacency degree and a synchronous delay tolerance threshold value according to a causal sequence and a relative time interval carried in the annotation encryption fragments, and distributing the annotation encryption fragments to different medical institution servers for storage; and the access verification module is used for verifying the access authority and marking the integrity of the encrypted fragment when receiving an access request. The problems that at present, the data semantic relation is split, the storage and access efficiency is low, and the ciphertext utilization capacity is insufficient are solved.

The invention provides a multi-stage electronic medical record generation method and system based on a structured thinking chain, and relates to the technical field of computer data processing, and the method comprises the steps: generating at least one medical record draft intermediate state based on input original medical data through a structured thinking chain processing flow, the medical record draft intermediate state comprises at least one specific paper to be verified; starting a double-path knowledge calibration mechanism, and executing calibration on the intermediate state of the medical record draft, the calibration comprising: a forward knowledge enhancement path: in response to a specific disguise, retrieving associated evidence related to the specific disguise from one or more authoritative knowledge bases; and a backward fact checking path: utilizing a small reasoning model to carry out consistency judgment on the specific discourse and the associated evidence; and correcting the intermediate state of the medical record draft based on the result of the consistency judgment so as to generate a calibrated electronic medical record. According to the method, the fact accuracy and credibility are improved, and the structural integrity and logic self-consistency are ensured.

The invention discloses an electronic medical record intelligent writing and structured processing system based on AIGC, and relates to the technical field of medical information processing and artificial intelligence application, and the system comprises a medical record information extraction and analysis module which extracts diagnosis records, medication schemes and treatment plan data of a patient in different departments and time points from an electronic medical record system, and obtains a medical record information analysis module; analyzing semantic content and format differences of the data by adopting a natural language processing algorithm to obtain a standardized medical information set; the diagnosis and treatment map construction module is used for constructing a patient diagnosis and treatment map according to the standardized medical information set and determining potential heterogeneous data connection points in the map; according to the electronic medical record intelligent writing and structured processing system based on AIGC, medical record data consistency checking and conflict automatic coordination are achieved, and the cross-department diagnosis and treatment cooperation efficiency and the safety and accuracy of a patient treatment scheme are remarkably improved.

The invention discloses an intelligent medical inquiry and medical record generation method and system based on multi-technology fusion, and the system comprises a specialized dynamic pre-inquiry module which is used for sequentially executing the analysis of a patient chief complaint, the matching of a dynamic template, the intelligent inquiry and the completion and verification of a knowledge graph according to a chief complaint text inputted by a patient, and obtaining the comprehensive information of the patient; the dual-drive medical risk control module is used for performing dominant and recessive risk identification based on a rule engine and a large model in combination with an RAG knowledge base according to the comprehensive information of the patient, and realizing a collaborative early warning closed loop; the voice structuring and medical record generation module is used for executing double-track voice separation and transcription, specialized structuring processing and multi-source data fusion and EMR template matching in the dialogue process of the patient and the doctor to generate a structured electronic medical record; and the system integration and performance optimization module is used for adapting to a hospital HIS / EMR system through a standardized interface and executing data preloading and cache optimization to guarantee the response performance of the system.

The invention provides a medical record automatic filling method and system based on voice input, and relates to the technical field of medical information processing.The method comprises the steps that voice input data are received by stages through voice collection equipment during diagnosis and treatment, real-time diagnosis and treatment stage identifiers are obtained, and a staged voice processing model is constructed based on the real-time diagnosis and treatment stage identifiers; therefore, semantic hierarchical processing is performed on the voice input data to generate a hierarchical semantic result, and a dynamic mapping relationship between the hierarchical semantic result and the medical record field is established and is converted and filled into a template to generate a staged medical record document. Semantic inheritance processing is carried out on the documents in different stages to correct semantic faults, and a complete medical record document is obtained and uploaded to a hospital electronic medical record system to complete automatic filling. The medical record recording efficiency and accuracy are improved.

The invention relates to the technical field of medical information processing, and discloses an acute pulmonary embolism auxiliary diagnosis decision-making system based on a large model, and the system comprises a data collection and preprocessing module which is used for receiving multi-source clinical data and preprocessing the multi-source clinical data; the large model reasoning module is used for inputting the preprocessed multi-source clinical data into a large model fused with a multi-modal causal attention and identification adversarial learning mechanism, and outputting a reasoning result; the dynamic learning module is used for accessing desensitized case data of a hospital electronic medical record system, continuously learning new cases through federal learning, and optimizing the recognition capability of a large model pair; the diagnosis suggestion generation module is used for generating structured diagnosis suggestions according to the reasoning result output by the large model; the result output and interaction module is used for displaying diagnosis suggestions to doctors and updating reasoning results in real time by a large model when the doctors supplement data; the diagnosis efficiency is improved.

The invention discloses a semi-supervised prognosis prediction system based on irregular sampling medical data pre-training, and the system comprises a clinical electronic medical record data collection and preprocessing module which automatically collects original clinical electronic medical record EHR data from a medical database; and the pre-training module is used for receiving the patient feature vector sequence output by the clinical electronic medical record data acquisition and preprocessing module and carrying out feature representation learning on the preprocessed clinical time sequence data by utilizing a combined multi-task self-supervised learning mechanism. And the classifier fine tuning and pseudo-label iterative optimization module is used for training the pre-trained model through fine tuning of samples with labels and carrying out iterative optimization through guidance of pseudo-labels to obtain a prediction result. And the application display module is used for displaying and outputting a post-hospital-admission vital sign sequence and a prediction result. According to the method, irregular sampling and missing data are effectively processed, information loss is avoided, and the prediction capability of the model and the performance of the model under the conditions of data imbalance and label scarcity are improved.

The invention provides a three-dimensional science popularization animation generation method and system based on an AIGC technology, and relates to the technical field of animation generation. The method comprises the following steps: firstly, fusing structured knowledge such as an anatomy term library, a diagnosis and treatment process guide and a drug specification with electronic medical records and CT / MRI volume data, and generating a split script and a medical term list through a large language model; then reconstructing an organ or focus grid by using a script-image combined driven text-three-dimensional diffusion model, and executing anatomical consistency correction according to the medical knowledge graph; automatically arranging a time axis, a camera track and a skeleton action, and synchronously synthesizing multilingual parastyle subtitles; and finally, frame-level rendering and compression packaging are completed in a real-time rendering engine, and efficient, professional and iterable medical animation generation is realized by means of an expert feedback closed-loop fine tuning model.

The invention relates to the technical field of medical data security, and discloses an electronic medical record data security sharing method based on a block chain. The method comprises the steps of receiving original data of different medical institutions through a multi-source medical data acquisition module and performing standardized cleaning to eliminate data format differences; generating a medical data block chain network by adopting a distributed account book construction module, and generating data blocks from the standardized data according to a time sequence; based on an attribute-based encryption strategy generation module, dynamically configuring patient node access permission granularity, and defining a medical record access range and a data service life of each node; calling the encrypted storage contract through an intelligent contract execution module, writing the electronic medical record into a block, and generating a chained hash verification value; when an external query request is received, the zero-knowledge proof verification module is triggered to generate validity verification parameters. According to the method, safe and controllable sharing of electronic medical records is realized, and cross-mechanism medical cooperation and medical research development are assisted.

The invention relates to the technical field of medical information, and discloses an outpatient service reservation management system based on big data, and the system comprises a global heterogeneous data fusion and management module which is responsible for obtaining data and capturing a real-time event through a hospital information system, an electronic medical record system and a real-time sign-in system, and performing cleaning and standardization processing on the data, and constructing a unified patient portrait model. According to the technical scheme, a dynamic doctor-seeing time length prediction model is called to carry out personalized calculation on the time length required by a single appointment, and an initial appointment queue formed by elastic time blocks with different time lengths is generated based on a calculation result, so that the technical effect of accurately matching appointment time length distribution with the actual demand of a patient is achieved; compared with the technical scheme that the out-call time of doctors is uniformly divided into fixed-length time units in the prior art, the defect that time resources of the doctors are idle or follow-up patient queues wait for a long time due to the fact that the preset duration and the actual duration are disjointed is overcome.

The invention discloses a malignant tumor acute kidney injury prediction method based on machine learning, and the method comprises the steps: taking a single hospitalization record of a malignant tumor patient as an independent sample, collecting and structurally integrating electronic medical record data, and constructing a standardized sample set through the verification of time integrity and numerical value consistency; under correlation constraints, multi-algorithm joint screening is adopted to determine a key feature set, multiple prediction models are constructed based on the feature set, and a target prediction model is selected through a unified verification process; further constructing an individual risk mapping structure and introducing a feature contribution degree interpretation mechanism to realize interpretability analysis of a prediction result; and performing risk assessment on the patient according to a prediction result, and outputting a corresponding monitoring and intervention strategy, thereby realizing early warning and auxiliary decision support for the malignant tumor related acute kidney injury.