1517 results about "Electronic medical record" patented technology

The invention provides a medical decision-oriented multi-modal data dynamic fusion and labeling method and system, and relates to the technical field of medical decision-making, and the method comprises the steps: collecting medical images, electronic medical records and physiological monitoring data of a patient, extracting multi-modal features such as a focus form, a disease type, a symptom, a treatment record and a physiological index, and carrying out the fusion and labeling of the multi-modal features; and constructing a dynamic fusion labeling model. According to the model, multi-modal features are coded, similarity is calculated, a feature association graph is constructed, feature aggregation is carried out, labeling is carried out in combination with a medical knowledge base, and a dynamic labeling vector is generated. And inputting the dynamic labeling vector into a medical decision support model, and based on similar case analysis and deep reinforcement learning, generating a diagnosis and treatment suggestion containing a treatment scheme and early warning information. By dynamically fusing multi-modal data and an online updating mechanism, the accuracy and efficiency of medical decision making can be improved, and personalized diagnosis and treatment are realized.

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

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 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.

A browsing support system is provided that enables medical-related information to be entered accurately. [Solution] The information processing terminal 10 included in the browsing support system 1 has an input unit 12 that creates questions for contents to be registered in the electronic medical record displayed on the display unit 11. The medical record management system 3 has a medical record management unit 31 that outputs creation information for creating the electronic medical record, which is input by the information processing terminal 10, to a RAG system 4, receives answers from the RAG system 4, and supports the creation of the electronic medical record. The RAG system 4 has a vector DB 52 that stores knowledge that has been vectorized and embedded in a vector space, and a knowledge search unit 44 that searches the vector DB 52 for knowledge based on the vectorized creation information and outputs answers formed from the searched knowledge to the medical record management unit 31.

The invention discloses a children MPP (Mycoplasma Pneumoniae) auxiliary diagnosis system based on multi-modal time series data modeling, and aims to improve the auxiliary diagnosis, clinical typing and severe prediction capabilities of mycoplasma pneumonia and reduce the workload of doctors. The MPP dynamic representation method based on multi-modal time sequence modeling is innovatively provided, multi-modal data such as images, electronic medical records and inspection indexes are fused, the time sequence feature embedding technology is adopted, and the evolution trend of the disease course can be effectively captured. Furthermore, deep fusion of different modal data is realized by using a progressive cross-modal semantic alignment strategy and a modal attention gating mechanism, and the problem of insufficient diagnosis accuracy caused by incompatibility of data modals in a traditional method is solved.

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 discloses a medical history recognition method and system based on a large model and an expert strategy, and the method comprises the steps: collecting data in a medical network, and carrying out the cleaning and standardization processing of the data; extracting medical entities and relationships thereof from the text, and optimizing the rule set; the extracted entities and relations are fused into a knowledge graph, a multi-relation graph is constructed, and attributes are defined; storing the knowledge graph in a graph database, and receiving new data through a real-time data stream processing system; basic information and symptom description of a patient are collected, and key information is extracted by using a large language model; an inquiry path is dynamically adjusted, and specific symptoms are clearly and fuzzily answered or deeply discussed by questioning; and generating a structured electronic medical record by using the large language model. Through an intelligent pre-inquiry process, the system can collect detailed medical history information and generate a standardized electronic medical record before a patient officially sees a doctor, so that the information verification and supplement time when a doctor receives the doctor is greatly shortened.

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 an electronic medical record automatic quality control system and method based on a large language model, and the method comprises the steps: extracting medical record data from a medical information system, and carrying out the data preprocessing; and performing deep semantic analysis on the medical text by utilizing a large language model, and extracting key diagnosis and operation information. And performing semantic representation on the medical terms by fine tuning the pre-training language model and combining with a multi-layer attention mechanism. And constructing an ICD coding knowledge graph, and calculating the semantic similarity between the diagnosis terms and the ICD coding terms in the electronic medical record by using an embedded vector technology to realize automatic matching. And according to a predefined quality control rule, automatically checking the electronic medical record through the logic expression, and generating a quality control report and an improvement suggestion. And finally, visually displaying quality control and coding results, and outputting a quality control report. The electronic medical record quality control efficiency and accuracy are improved, and standardization and automation of quality control work are achieved.

The invention relates to the technical field of text data analysis, in particular to an electronic medical record free text analysis method, system and device, which improve the efficiency of information extraction and reduce the demand of manual intervention. The method comprises the steps of receiving free text data of the electronic medical record, performing cleaning, word segmentation and medical term standardization processing, and converting an unstructured text into structured data; based on deep learning and a medical knowledge base, text features are extracted through a pre-training language model, entity boundaries are captured on an output layer in combination with a conditional random field, medical entities in a text are recognized, and the recognized entities are classified and labeled; extracting a causal relationship, a treatment relationship and an examination relationship among entities through a dependency syntactic analysis and semantic role labeling technology, and constructing an entity association network; performing dynamic correction and supplementation on entity classification and relationships in combination with medical record context information; and outputting an analysis result in a structured JSON format to generate a computable semantic map.

The invention discloses a medical text-oriented intelligent disease diagnosis causal atlas construction method and system. The method comprises the steps of medical text related data acquisition, causal entity identification, causal relationship extraction and entity unification. The model and the method for automatically constructing the causal knowledge graph are realized, the functions of dynamic uncertain causal knowledge graph editing, clinical intelligent auxiliary diagnosis, automatic extraction of electronic cases and the like are provided, and multi-source fusion of medical text data and expert experience knowledge is realized, so that the accuracy and the reliability of the system are improved. And a disease causal knowledge graph with a certain clinical application value can be established.

The invention discloses an electronic medical record feature extraction method based on natural language processing, and the method comprises the steps: carrying out the cleaning, medical term standardization, Chinese word segmentation and part-of-speech tagging of an original electronic medical record text, and generating preprocessed text data; carrying out medical entity identification by utilizing the pre-training BERT model of the fine-tuning medical corpus, and extracting a medical entity set; constructing a relation extraction model based on a self-attention mechanism, and generating an entity relation triple set; finally, the extracted medical entities and relations are stored in a graph database, an API interface is provided for query and analysis, and therefore the problem that in the prior art, unstructured text information of electronic medical records is difficult to extract is effectively solved.

The invention relates to the technical field of intelligent medical treatment, in particular to an endocrine patient health management method and system integrated with an electronic medical record. The method comprises the following steps: performing multi-dimensional data acquisition of an endocrine patient through a hospital information system, and performing multi-dimensional portrait construction of the patient to obtain a multi-dimensional health portrait of the patient; performing time sequence data feature extraction on the multi-dimensional health portrait of the patient to obtain time sequence feature data; acquiring historical physiological data of the patient; establishing an individualized physiological rhythm model according to the historical physiological data of the patient to obtain the individualized physiological rhythm model; and performing non-uniform interpolation on the time sequence characteristic data according to the individualized physiological rhythm model to obtain interpolated data. According to the method, the individualized physiological rhythm model and the dynamic baseline model are constructed, the dynamic early warning threshold value and subtype layering are combined, and accurate risk assessment and individualized health information management are performed on each patient, so that the health management effect is remarkably improved.

The invention relates to the technical field of medical information, in particular to an ophthalmology information extraction system based on electronic medical records. The system comprises an electronic medical record data acquisition and cleaning module, a domain knowledge driven ophthalmology information extraction module, a multi-modal data fusion and labeling module and a structured result generation and display module, and original medical record data can be acquired; performing data cleaning on the original case data to generate a patient electronic case data set; therefore, through the whole-process modular design and multi-modal data fusion processing, the problems that in a traditional ophthalmic case information extraction mode, data processing is not coherent, information extraction is not accurate, and results are difficult to directly express are solved, and the data utilization rate and clinical application value are improved.

The invention belongs to the field of remote virtual rehabilitation training, and particularly relates to a remote rehabilitation management method and system based on virtual reality. The method comprises the steps that firstly, the current health condition, the expected rehabilitation target and the needed period of a rehabilitation patient are predicted through a comprehensive evaluation algorithm and machine learning, secondly, a personalized training plan is intelligently recommended from a virtual rehabilitation training strategy library according to an evaluation result, and in the training implementation process, a sensor continuously monitors physiological indexes and micro-expression response of the rehabilitation patient; the rehabilitation progress is analyzed and evaluated through real-time data and fed back to a training strategy library in time, the training intensity and period are dynamically adjusted, and it is ensured that training is closely matched with the actual health state of a rehabilitative person; by fusing electronic medical record processing, virtual reality technology, machine learning prediction and visual emotion recognition, precise and intelligent management of rehabilitation training is realized, and rehabilitation efficiency and experience are remarkably improved.

The invention discloses an intelligent remote follow-up visit method and system for tumor patients, and relates to the technical field of medical intellectualization, and the method comprises the steps: collecting follow-up visit data through a remote medical terminal, carrying out the processing of the follow-up visit data, obtaining a physical sign index sequence and a symptom feature vector, and generating a dynamic follow-up visit evaluation score through a time sequence prediction model; performing semantic analysis on the electronic medical record data of the patient based on the medical knowledge map, extracting tumor stage classification data, chemoradiotherapy measurement data, weight nutrition indexes and laboratory inspection data, determining a basic risk coefficient, a cumulative dose ratio and a body state score, and generating a baseline follow-up assessment score through a prediction model; and according to the dynamic follow-up evaluation score and the baseline follow-up evaluation score, calculating a comprehensive follow-up grade and generating a next follow-up plan. According to the invention, the accuracy of patient state evaluation is improved, accurate configuration of medical resources is realized, and the quality and efficiency of remote follow-up visit are effectively improved.

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 relates to a multi-source data fusion management method and system for medical record information, and the method comprises the following steps: carrying out the data collection of an electronic medical record system, a medical image workstation and an inspection information system of a medical institution, and obtaining an original medical record data set; performing semantic mapping and concept association on the original medical record data set based on a preset medical ontology knowledge graph to obtain a semantic association medical record information network; performing dynamic time sequence feature analysis on the semantic association medical record information network through a time sequence feature extractor to obtain a time sequence medical record feature sequence; and carrying out heterogeneous data fusion on the time sequence medical record feature sequence to obtain a unified medical record data view, so that the technical problem that the capability of deeply understanding and mining medical record information is limited due to the fact that an existing medical record management method usually ignores semantic relevance between data is solved.

The invention discloses an artificial intelligence perioperative period anesthesia depth detection system which comprises a data acquisition module which is provided with a multi-mode sensor and is used for acquiring physiological data of a patient in a perioperative period, and an artificial intelligence model module which is used for constructing an anesthesia depth prediction model based on a deep learning algorithm. The result output and display module is used for outputting the prediction result of the anesthesia depth in real time and visually displaying the prediction result on a display screen in the form of digits and charts, and the communication and interface module is provided with an interface for data communication with a hospital information system and an electronic medical record system. By means of multi-mode collection and innovative design, the anesthesia depth detection accuracy is improved, precise monitoring and intelligent auxiliary decision making are achieved, the method can be used for various scenes, the safety of patients in the perioperative period is effectively guaranteed, and the medical efficiency is improved.

The present invention provides a method and system for intelligent follow-up of chronic atrophic gastritis based on multi-source electronic medical records, which relates to the technical field of intelligent disease follow-up, including preprocessing and feature extraction of structured, semi-structured and unstructured electronic medical record data, mining the inter-layer feature association relationship by a meta-path reasoning module, and generating comprehensive graph features of patients; identifying the causal relationship between features by a Bayesian causal network module, and building a disease risk prediction model in combination with a temporal causal graph module; and optimizing the follow-up plan by a Nash equilibrium algorithm based on risk prediction results and medical resource constraints; the present invention can effectively predict the disease risk of chronic atrophic gastritis, and optimize the follow-up plan in combination with medical resources, thereby improving the follow-up efficiency and facilitating the early diagnosis and treatment of chronic atrophic gastritis.

The invention discloses an apoplexy patient management method and system based on a multi-mode RAG and LLM model, and the method comprises the steps: obtaining the standardized electronic medical record data of a patient based on an FHIR standard electronic medical record; performing feature extraction on the electronic medical record data to obtain multi-modal data; integrating and representing a time-space relationship among the multi-modal data, and obtaining a dynamically updated dynamic knowledge graph; analyzing and integrating the electronic medical record data to obtain integrated information; constructing a stroke risk causal network based on an LLM model in combination with a causal reasoning mechanism; obtaining a personalized health report based on the dynamic knowledge graph and a stroke risk causal network; and based on a personalized report result and feedback data of the patient, a treatment scheme and a management strategy of the patient are optimized in real time. According to the application, the causal reasoning mechanism is embedded into the Transform architecture of the LLM, so that the modeling and prediction capabilities of the stroke risk causal relationship are enhanced.

Various systems and methods are provided for generating and editing of medical reports and records using artificial intelligence (AI) assistance. An intelligent medical reporting tool may edit medical materials such as electronic medical records, medical reports, and treatment plans. The intelligent medical reporting tool can provide edit recommendations and suggested additions to medical materials to a user of the tool. Edit recommendations and suggested additions may be related to contradictions, incompleteness, clarity, and clinical guidelines.

The invention discloses a clinical aid decision-making system based on big data, and relates to the technical field of intelligent medical treatment, and the system extracts structured and unstructured data from electronic medical records and medical images, employs a bidirectional LSTM deep learning framework based on a self-attention mechanism, carries out the alignment of the cross-modal features of medical record texts and image data, and carries out the recognition of the cross-modal features of the medical record texts and the image data. The method comprises the following steps: establishing a medical knowledge graph based on an RDF triple, modeling a high-order interaction relationship through a cross-modal interaction attention mechanism CMA, enabling disease expression to be more accurate and interpretable, constructing the medical knowledge graph based on the RDF triple, dynamically expanding knowledge in combination with a graph neural network GNN, and matching the disease expression of a patient through a semantic similarity calculation model; a multi-layer similarity calculation framework is adopted to perform similar case screening, coarse screening is performed through surface feature matching, deep semantic matching is performed in combination with GNN optimized disease semantic vectors, cases with similar disease progress paths are inferred and matched through a knowledge graph, and comprehensive and multi-level similar case screening is realized.

The invention relates to the field of medical material management, and discloses a hospital material management server, which comprises a data collection module used for collecting material consumption data, the number of patients and operation plans of all departments from a hospital information system and an electronic medical record system, and transmitting the collected data to a prediction analysis module; the prediction analysis module is connected with the data collection module, and the invention also discloses a hospital material management method which comprises the following steps: S1, collecting material consumption data of each department from a hospital information system and an electronic medical record system through the data collection module; by using an adaptive neural network and a virtual twinning technology, accurate demand prediction, inventory dynamic adjustment, distribution optimization, global monitoring and environment simulation adjustment are realized; the interactive feedback module forms closed-loop optimization, the meta-universe cooperation module supports virtualization operation and training, and the defects of a traditional system in prediction, scheduling, monitoring and cooperation are overcome.

The invention discloses a multi-granularity knowledge graph auxiliary diagnosis method based on DeepSeek and Agent. The multi-granularity knowledge graph auxiliary diagnosis method comprises the following steps: step 1, receiving electronic medical record text data and medical image data of a patient; 2, constructing a knowledge graph; updating the knowledge graph every day to reflect the latest medical research result; step 3, analyzing the text data of the electronic medical record through DeepSeek-R1; 4, extracting spatial structure feature nodes of the medical image data through a multilayer three-dimensional convolution kernel; the method comprises the following steps: segmenting medical image data into sequence blocks through a Vision Transform; 5, the output of the DeepSeek-R1, the output of the multi-layer three-dimensional convolution kernel and the output of the Vision Transform are input into a multi-modal fusion module; step 6, outputting a high-confidence diagnosis conclusion and probability distribution; and step 7, generating an intelligent report of the structured text. According to the method, by combining natural language processing, computer vision and the knowledge graph technology, accurate and efficient medical examination is achieved.

The invention discloses a multi-source clinical data quality inspection method and device based on a large model and electronic equipment, and belongs to the technical field of data processing.The multi-source clinical data quality inspection method based on the large model comprises the steps that multi-source clinical data are obtained, and the multi-source clinical data comprise electronic medical records, laboratory inspection reports, medical image reports and operation records; performing textualization processing on the multi-source clinical data to obtain a format text; determining cue words according to the format text, wherein the cue words are correspondingly matched with the quality inspection rules; utilizing cue words to guide the trained deep learning model to extract keywords in the format text; and carrying out logic judgment on the keywords by utilizing a quality inspection rule to obtain a quality inspection result. According to the method, the problem that the rule engine is insufficient in fixity and adaptability is solved, and the accuracy and flexibility of multi-source clinical data quality inspection are remarkably improved.

The invention relates to a visualization method of an electronic medical record system and a visual medical record system. The method comprises the following steps: constructing a system main page; providing entry options, basic information corresponding to the patient, chief complaint information and current medical history information in the navigation bar area; loading and displaying the three-dimensional human body model in the three-dimensional human body model display area; in response to analysis of the electronic medical record information of the patient, automatically identifying and extracting lesion part information described in the medical record information; performing visual labeling on the mapped diseased region on the three-dimensional human body model; setting human body system options in the three-dimensional human body model display area; and in response to the operation that the mouse of the user hovers on the lesion part label on the three-dimensional human body model, displaying an examination result and abnormal index information related to the lesion part. According to the invention, the defects of an existing electronic medical record system in the aspects of information acquisition efficiency, spatial information presentation and information interaction modes can be effectively overcome, and the readability of medical record information and the diagnosis and treatment efficiency of doctors are remarkably improved.

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

The invention 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.