155 results about "Medical documents" 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.

An intelligent analysis method based on medical document structure perception and multi-modal fusion comprises the following steps: carrying out structure topology modeling on a medical document, extracting visual layout, text meta-information, space coordinates and semantic keyword features, constructing a semantic topological graph and dynamically shielding irrelevant contents; selecting an extraction path according to a document type, performing deep semantic analysis and entity recognition on a text-type document, and performing visual enhancement OCR recognition on a scanning-type document; the features are injected into a medical knowledge graph, and feature fusion, semantic verification, relation reasoning and information completion are achieved through a graph neural network; a three-stage strategy optimization model of basic pre-training, domain adaptation and online reinforcement learning is adopted; and large-scale processing is realized through a dynamically aggregated distributed architecture. The method is used for intelligent analysis and structured conversion of documents of hospitals, medical insurance and medical scientific research. The problems that heterogeneous medical document analysis adaptability is poor, multi-modal fusion is difficult, medical knowledge utilization is insufficient, and large-scale processing efficiency is low are solved.

The invention relates to the technical field of medical document management, in particular to a digital management method and system for medical documents. Obtaining medical document images of a target medical business system, processing the medical document images, and generating a standardized text stream of unified coding of each medical document image; constructing a feature matrix with spatial-semantic dual attributes in a feature region in each medical document through the standardized text stream of the unified coding of each medical document image; and extracting an entity relationship triple in the feature matrix, obtaining semantic fingerprint features of each medical document according to the entity relationship triple, and classifying each medical document according to the semantic fingerprint features of each medical document. According to the invention, efficient and accurate classification of the medical documents can be realized, and the intelligent and automatic level of medical document management is improved.

The invention provides a medical multi-hop text question and answer retrieval enhancement method based on heterogeneous knowledge. The method comprises the following steps: firstly, constructing a uniform heterogeneous graph structure based on a medical knowledge graph of a medical document, and establishing a semantic bridge through an entity-document mapping relationship; performing semantic decomposition on a complex medical problem input by a user by utilizing the large model, and iteratively generating a series of mutually independent atomic queries; searching a reasoning path in the entity sub-graph of the heterogeneous graph, and calculating a path score by fusing the weighted combination of the entity association text similarity, the entity matching degree and the path edge weight; training a retriever by adopting a marginal sorting loss function, and optimizing a retrieval effect through positive and negative sample comparative learning; and finally, calling a large model to convert the reasoning path with the highest score into a text, and extracting a document fragment corresponding to a path node. According to the method, the problems that an existing retrieval enhancement technology is insufficient in complex problem processing capacity, poor in reasoning interpretability and the like are effectively solved, and high-accuracy medical questions and answers are achieved.

The invention provides an intelligent medical question-answering system and method based on hybrid retrieval and lightweight reordering, and is applied to the technical field of medical data processing. According to the method, five types of core entities are extracted through the preset Chinese medical NER model, and the structured knowledge base is formed through relation extraction modeling association. Analyzing user Chinese query, extracting medical entities, identifying four types of appeals and converting the four types of appeals into semantic vectors; a PubMedBERT is adopted to encode a medical document to generate a vector for storage, BM25 and vector retrieval are executed in parallel when query is received, and candidate documents are generated through fusion of an RRF algorithm. And generating a score data set based on the prompt template, and training the lightweight model to sort and output an evidence set. In combination with query semantics, a simplified context is generated through retrieval, sorting and compression, and FlashAttention optimization calculation is integrated. An optimized U-Net segmentation image is utilized to generate a structured report, and multi-modal information is integrated to generate an accurate answer giving consideration to the image and medical knowledge through LLM reasoning.

Example solutions for real-world evidence generation using artificial intelligence models and performing trial simulations include: training a large language model (LLM) to receive medical documents that include medical text associated with a patient output predicted values for medical attributes of the patient based on the medical text; performing attribute extraction from structured medical documents, including extracting values for a first plurality of attributes associated with the plurality of patients; performing attribute extraction from a plurality of unstructured medical documents of the plurality of patients using the LLM, including extracting predicted values for a second plurality of attributes associated with the plurality of patients; and performing a survival model simulation that computes estimations of hazard ratio (HR) between cases and controls using real-world data of the plurality of patients extracted in the first attribute extraction and second attribute extraction.

The invention relates to a professional knowledge graph construction method for the medical field, and the method comprises the following steps: obtaining a multi-modal medical document, carrying out the deep analysis through a Transform-based multi-modal model, and obtaining an analyzed multi-modal medical document; dynamically segmenting the analyzed multi-modal medical document by adopting a context-based adaptive text segmentation method to obtain independent text blocks; based on the independent text blocks, performing structured extraction, and performing verification by adopting a dynamic dual-verification mechanism to form knowledge sub-graphs; and based on the knowledge sub-graph, further constructing a standardized medical knowledge graph. Compared with the prior art, the method has the advantages of high precision, expandability and the like.

The invention discloses a medical data structured extraction method based on machine learning, and the method comprises the following steps: carrying out the standardization processing of multi-source heterogeneous data in different medical scenes, constructing a time and condition two-dimensional filtering rule, and extracting preliminary data; and a modular index structure is formed according to medical process and technical attribute division. And generating analysis limiting conditions by fusing the medical knowledge graph and the knowledge base, guiding an analysis engine to perform semantic routing and reasoning, and outputting a structured result. Finally, disease identification and quality judgment are achieved, and structured information meeting or not meeting the standard is output. The method aims at efficiently extracting the structured information from various types of medical documents.

The invention provides a man-machine collaborative intelligent writing method and system for medical documents, and relates to the technical field of man-machine collaboration.The method comprises the steps that multi-modal clinical data of a patient is obtained for cross-modal semantic alignment, reasoning expansion is conducted based on a multi-level traditional Chinese medicine knowledge graph to generate a structured dialectical framework, and the structured dialectical framework is subjected to data processing; a logic consistency verification graph of a candidate medical record fragment set is constructed to solve semantic conflicts, and a medical record generation model is dynamically optimized in combination with clinical actual curative effect data, so that the intelligence degree, the standardization level and the clinical diagnosis and treatment value of traditional Chinese medicine medical record writing are improved.

The invention discloses a medical question and answer method based on knowledge graph and retrieval enhancement generation, and belongs to the technical field of natural language processing and knowledge management. The method includes the following steps that text blocks are obtained on the basis of medical document preprocessing and partitioning, and an abstract tree is constructed; entity relationship extraction is carried out based on the text blocks, and a knowledge graph is constructed; constructing a mapping relationship between the abstract tree and the knowledge graph, and obtaining a vector database; performing entity extraction and query association degree calculation based on user query, judging a retrieval type of the user query, and obtaining a corresponding retrieval result by utilizing the vector database according to the retrieval type; and inputting a retrieval result into the large language model to generate a result. Aiming at the defect of insufficient quality of intelligent questions and answers generated in the medical field by retrieval enhancement generation, the method disclosed by the invention has the advantages that depth and breadth can be considered by one-time retrieval by constructing an abstract tree and a knowledge graph, forming an abstract tree-knowledge graph double-layer index and dynamically typing according to the query association degree, so that the accuracy and efficiency of medical questions and answers are improved.

The invention discloses a processing method for structural information extraction and privacy protection of a medical report, which relates to the field of medical report processing and can efficiently and accurately extract text information in a table by integrating advanced image preprocessing, dynamic layout analysis, OCR (optical character recognition) technologies and a dual-channel privacy detection model. Establishing a coordinate mapping relation between the image description paragraph and the corresponding image; the generated JSON structure containing the text, table and image-text mapping relation ensures the integrity and accuracy of the data; the problems of data dislocation, context splitting, privacy disclosure and the like when the medical document with complex typesetting is processed in the prior art are solved; besides, an improved Transform framework is constructed on the basis of a Qwen model, a logical reasoning chain is injected, meanwhile, a case reading large model is obtained by integrating clinical diagnosis rules and multi-modal contrast learning strategy training, the model is utilized to generate a high-quality structured abstract, and the abstract generation accuracy is greatly improved.

The invention provides a medical document semantic entity recognition method and system based on spatial semantic enhancement, and the method comprises the steps: detecting a text region in a medical document through an OCR technology, and extracting text content and bounding box coordinate information thereof; based on the spatial geometrical relationship between the textboxes, calculating the distance and angle between the textboxes, fusing the spatial semantic features with the text hiding state, and constructing a spatial-temporal feature matrix; through a multi-head attention mechanism, deep interaction is carried out on the fused geometric and text features, and the understanding of the model on the semantic relationship between the texts is further strengthened; and inputting the enhanced features into a classification head for accurately identifying and classifying semantic entities. According to the scheme, the spatial semantic information is introduced, so that the model can better understand the spatial layout of the text in the document, and the accuracy and robustness of multi-category semantic entity recognition in the complex medical document are remarkably improved.

The invention discloses a medical document slicing and retrieval method and device, equipment and a storage medium, and the method comprises the steps: generating a high-fidelity semantic summary text and a child node of a structured keyword set through a father node containing an original complete paragraph text, and storing the child node to a vector database; storing the semantic summary text and the structured keyword set into a keyword database; and when a user query instruction is received, performing dual-channel retrieval through the vector database and the keyword database, obtaining related target child nodes, extracting target original contents of a target father node from the target child nodes, transmitting the target original contents to the large language model, generating a final answer, and sending the final answer to the user. According to the method, the relevance and accuracy of retrieval results can be remarkably improved, instant and accurate decision support basis is provided for doctors, semantic integrity can be reserved, efficient multi-modal retrieval can be achieved, and the speed and efficiency of medical document slicing and retrieval are improved.

The invention discloses a system and a method for extracting key information of a medical document by using a deep NLP model, and relates to the technical field of natural language processing, and the method comprises the following steps: collecting multi-source data, cleaning noise, aligning according to a standard term library, and constructing an original corpus, a standardized entity dictionary and a key information ontology library; a pre-training model BioBERT is used for carrying out adaptive adjustment on the annotation data set, a multi-task NLP model is trained according to tasks, and the weight of the model is optimized based on high-frequency terms; processing a new document by using the optimization model, updating the ontology library after threshold filtering, clustering analysis and expert verification, and supplementing new terms and labels; new and old annotation data are mixed, and the model is incrementally updated by using an EWC algorithm; and analyzing the new document, and constructing a dynamic medicine knowledge graph. The method can effectively improve the condition that the model is difficult to update in time because new knowledge and new terms in the field cannot be quickly incorporated into the model in the prior art.

The invention provides a medical document intelligent generation method based on retrieval enhancement and multi-modal features, and relates to the technical field of intelligent retrieval, the method comprises the following steps: constructing a cross-modal semantic association map to realize semantic fusion and path reasoning of multi-modal medical data; retrieving relevant document segments from a knowledge base based on the atlas representation; and performing graph-guided sequence generation, converting a document skeleton according to a causal logic path, dynamically fusing candidate fragments and keeping semantic consistency. According to the method, the accuracy, logicality and clinical correlation of medical document generation can be improved.

The invention relates to the technical field of artificial intelligence, and discloses a medical intelligent assistance method, device and equipment and a storage medium, and the method comprises the steps: collecting the dialogue data of a patient and a doctor, obtaining the medical record text data of the patient, obtaining a structured medical document, and carrying out the entity recognition, so as to obtain entity information and semantic information; querying in a preset knowledge graph based on the entity information and the semantic information to obtain a first query result; and inputting the first query result into a preset medical big model to generate a first text verbal skill set. The system has the advantages that the burden of doctors is relieved in an intelligent mode, the communication efficiency is improved, and it is ensured that patients have clear and accurate information when receiving medical services. The leveled text verbal skill not only can improve the doctor-patient relationship, but also can improve the doctor-seeing experience of the patient and the overall quality of the medical service, and provides better support for doctors.

The invention relates to the technical field of data processing, in particular to a medical document quality detection method and system based on a large model and a knowledge graph, and the method comprises the steps: obtaining a corresponding target quality detection standard through matching a target attribute tag of a target medical document with a preset attribute tag of a preset quality detection standard in a standard database; in combination with deep understanding and analysis of a preset large language model on medical document contents and target quality detection standards, various medical concepts, relationships and logics involved in the medical document can be comprehensively covered, and various abnormal triple information such as logic contradictions, data errors, semantic errors, missing records, format errors and the like in the medical document can be accurately identified; and the reliability of quality detection is greatly improved.

The invention discloses a data acquisition method and system based on intelligent identification and filling of medical documents, and relates to the technical field of medical data, and the method comprises the steps: configuring detection key parameters of a target medical document based on an OCR tool; acquiring a medical document sample, and analyzing and counting document information categories according to the medical document sample; dividing document information category areas corresponding to the positions of the medical document samples based on the document information categories, setting area identification marks according to the document information category areas, and establishing area logic association according to the area identification marks; establishing a medical document extraction template based on the region identification identifier and the region logic association; identifying the target medical document by using the detection key parameters and an OCR tool, and obtaining medical document text information; and extracting based on the medical document text information and the medical document extraction template, generating medical document key information, and correspondingly filling the medical document key information into the system.

The invention discloses an OCR-based medical document intelligent identification method and system, and relates to the technical field of document identification, and the method comprises the steps: collecting a medical document image through a mobile terminal, generating a binary image based on the collected image through U-Net in combination with multi-scale feature fusion and an attention mechanism, and cutting the binary image; based on the cut binary image, text information is extracted through an OCR model, and a structured field is extracted according to the typesetting rule and geometric distribution of the medical document; and performing deterministic rule judgment and risk assessment on the structured data. According to the method, the image calculation complexity is reduced through standard graying processing, the text region feature extraction precision is improved by fusing a U-Net structure of a CBAM attention mechanism, effective fusion and noise suppression of multi-scale features are realized in combination with Attention Gate, text direction correction is realized in combination with Hough transform, and the text detection robustness and recognition accuracy are improved.

The invention provides a method, a system, equipment and a medium for generating an XML (Extensible Markup Language) file conforming to an E2B standard, and relates to the field of medical supervision data submission, the method comprises the following steps: preprocessing an input PDF (Portable Document Format) file to generate a standardized image sequence; identifying text, table and formula elements in the image sequence through a multi-modal AI visual model, and performing semantic error correction in combination with a medical field dictionary to generate structured data; based on a preset E2B semantic mapping rule, converting the structured data into an XML node label; xML node labels are injected into the dynamically constructed XSD template, and an initial XML file is generated through multi-layer verification; and outputting the standardized XML file in combination with a self-adaptive rechecking mechanism. Through a multi-mode AI visual model, a UMLS medical ontology library, XSD drive verification and a self-adaptive rechecking mechanism, high-precision, compliant and credible conversion from an unstructured medical document to an E2B standard XML is achieved.

The invention relates to the technical field of document generation processing, in particular to a medical document automatic generation and archiving system and method.The system comprises an information extraction and structuring module, a document generation and verification module, an abnormal content recognition module and a label generation and archiving module. According to the method, the electronic medical records are analyzed word by word and key information is structured, so that accurate extraction and rapid processing of the medical data are realized, the document generation efficiency is greatly enhanced, the verification process of document continuity and logicality is optimized, the document quality is improved, and through comparative analysis and accurate matching of medicine doses, the accuracy of the medical data is improved. Potential medical errors are effectively recognized, treatment safety is improved, document archiving is systematically performed through an automatic tag generation and data matching technology, document retrieval speed and accuracy are remarkably improved, safe storage of information is ensured, high efficiency of information processing and accuracy of archiving are ensured, and the method is suitable for popularization and application. And the medical document processing cost is obviously reduced.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating answers to medical questions using neural networks and other components. In one aspect, a method includes: obtaining question data representing a medical question; obtaining a plurality of document snippets from a medical database that stores medical documents; for each document snippet in the plurality of document snippets, determining a relevance score for the document snippet by using a ranking neural network based on the document snippet and the medical question; selecting, based at least in part on the relevance scores, a subset of the plurality of document snippets; generating a prompt that includes (i) the medical question and (ii) the subset of the plurality of document snippets; and generating an answer to the medical question based on processing the prompt using a generative neural network.

The invention provides a medical literature automatic classification system and semantic retrieval method based on deep learning, and belongs to the technical field of big data processing. Comprising. The data preprocessing module is mainly used for performing cleaning, normalization and feature extraction processing on texts, images and structured data of medical literatures; the deep classification model is used for training historical data based on a mixed neural network architecture fused with a medical knowledge graph, and automatically classifying medical literatures through a multi-stage classification module and a literature utilization rate analysis module; the multi-level classification module is mainly used for performing hierarchical primary classification on medical literatures according to predefined medical subject large categories and sub-categories thereof; and the literature utilization rate analysis module mainly constructs a dynamic weight model based on the related data of the literature, and classifies and sorts each sub-class literature based on the model data. The method has the advantages that automatic classification and semantic retrieval of medical literatures are achieved, and classification precision and retrieval efficiency are improved through multi-level classification and literature utilization rate analysis.

To provide a computer program, an information processing method, an information processing device, an information processing system, and an anonymization device that can be expected to support creation of a medical document based on medical information stored in a database, retrieval of medical information, or the like.SOLUTION: Receiving an input of identification information of a target patient, receiving an input of condition information related to generation of the medical document, acquiring one or a plurality of pieces of medical information from the database on the basis of the received identification information of the target patient and the condition information, and generating prompt information related to the generation of the medical document on the basis of the condition information; Inputting the acquired medical information and the generated prompt information to a language model, acquiring a medical document output by the language model, and displaying the acquired medical document on a display unit.SELECTED DRAWING: Figure 16

The invention discloses a medical document processing method and system based on a double-pipeline architecture, and relates to the technical field of document processing. According to the medical document processing method based on the double-assembly-line architecture, through a closed-loop process of document classification, preprocessing, double-assembly-line directional parallel processing and hierarchical vectorization storage, precise adaptation and efficient processing of multi-format and multi-type medical documents are achieved, the information loss rate and the key information truncation rate are greatly reduced, and the medical document processing efficiency is improved. According to the method, the document processing efficiency and the data standardization degree are improved, the warehousing success rate and the data traceability of the vector library are ensured, high-quality and structured data source support is provided for subsequent medical intelligent retrieval, clinical question and answer and retrieval enhancement generation system application, the knowledge base construction and maintenance cost is remarkably reduced, and the method is suitable for popularization and application. The problems that in existing medical document processing, medical semantics are not taken into consideration, so that key clinical information is easy to cut off, and a single processing flow cannot adapt to a structured guide and an unstructured case are solved.

The invention provides a medical document multi-language intelligent analysis system, relates to the field of language translation, and improves the accuracy and efficiency of professional term translation. The method comprises the following steps of: firstly, performing language recognition on an original text document by a recognition module through text unitization and context vector generation, and matching a corresponding corpus; then, a translation module carries out lexical element alignment on the source lexical elements through term bank injection and an AI model, the translation process is automatically optimized, and accurate translation of the terminologies is ensured; and finally, the reconstruction module accurately replaces corresponding contents in the original text document with translation output through the mapping file, so as to ensure that the document format and typesetting are consistent. Through the automatic and optimized translation process, the quality and efficiency of professional term translation are remarkably improved, manual intervention is reduced, and the translation requirement of a high professional standard is met.

The invention discloses a doctor inquiry training system based on a generative AI technology, which comprises a medical document database and a processing unit in information interaction with the medical document database, the voice input module, the voice output module, the training module, the federation module, the vector database, the embedding module, the inspection module and the evaluation module are in information interaction with the processing unit. The system has the advantages that a real inquiry scene can be dynamically simulated to provide efficient and accurate inquiry ability training and evaluation support, and the problems that an existing inquiry training system is difficult to simulate real patient communication, the inquiry process cannot be deeply evaluated and continuously improved, a cross-hospital cooperative training mechanism is lacked, and the training effect is poor can be solved.

The invention discloses a medical document knowledge extraction and structured processing system and method based on a large model. According to the system, a user demand is converted into a standard task mode through a task definition analysis module; the cue word automatic generation and optimal selection module generates and selects an optimal cue word according to the cue word automatic generation and optimal selection module; the constrained large model extraction module performs information extraction by using the cue word; the confidence evaluation and calibration module generates interpretable confidence by calculating multi-source signals and performing fusion calibration; the cue word strengthening and fallback retry module performs feedback retry according to the low-confidence result; and the result merging and consistency verification module finally outputs high-quality structured data. According to the method, the full-process automation and high adaptability of medical document information extraction are realized, the output quantifiable confidence provides a solid guarantee for the result reliability, and the practical value and the feasibility of the system are remarkably improved.

The invention discloses an LLM-based medical translation model training method and a medical document translation method. The model training method comprises the steps of constructing a Chinese-English medical corpus, pre-training a medical translation model, finely tuning the medical translation model, quantifying the medical translation model and deploying the medical translation model. The medical document translation method comprises the steps of document format conversion, document preprocessing, medical document translation, document post-processing and document format restoration. According to the medical translation model training method and the medical document translation method based on the LLM, the accuracy of medical translation is remarkably improved, professional terms, complex sentence patterns and context logic relations in medical texts are efficiently understood and translated, and translated texts which are more natural, smoother and higher in accuracy can be generated.

The invention discloses an underwriting multi-dimensional decision-making method for performing intelligent inspection and evaluation on health risks, and the method comprises the steps: collecting image data of a medical data file, and carrying out the preprocessing of the image data; the preprocessing comprises image enhancement, gray level transformation, Gaussian filtering and Hough transform correction; applying an OCR model based on a neural network, performing structured extraction on the image data according to an underwriting multi-dimensional medical decision rule to obtain structured data in a text form, performing standardization processing on the structured data, and transmitting the processed structured data into the underwriting multi-dimensional medical decision rule; and according to the underwriting multi-dimensional medical decision rule, performing multi-dimensional analysis and evaluation on the standardized structured data based on a preset rule flow, and automatically outputting an underwriting result. The multi-dimensional medical decision rule is adopted to effectively overcome multiple technical difficulties in the underwriting and underwriting fields, manual operation is replaced, the underwriting operation efficiency is greatly improved, the underwriting process is simpler, more convenient and faster, and the underwriting conclusion is more accurate.