119 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 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.

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 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 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 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.

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.

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 provides a closed-loop medical voice interaction system and method. The closed-loop medical voice interaction system comprises a voice generation unit, a voice acquisition unit, a semantic understanding unit, a dialogue management unit and a data flow controller. According to the system, a structured inquiry summary can be automatically generated and converted into a standard medical document through the seq2seq model, and the document workload of a doctor is greatly relieved. For special groups such as patients with mobility difficulties, residents in remote areas and old people, the system provides a convenient remote inquiry way, and the problem of non-uniform distribution of medical resources is effectively relieved.

The invention discloses a medical document quality evaluation method based on a large language model, which comprises the following steps: acquiring medical document data, and generating medical document classification structured data; configuring a medical document quality evaluation rule according to the medical document quality evaluation method; and constructing a large language model input item by using the medical document classification structured data and the medical document quality evaluation rule, inputting the large language model input item into a large language model for processing, and outputting a medical document quality evaluation result. The invention further discloses a medical document quality evaluation system and device based on the large language model. The method has the following prominent effects: full-amount quality evaluation becomes possible, the method can adapt to expansion and development of the medical document, and quality monitoring of the medical document is changed into prevention in advance from afterward responsibility investigation; the large language model is applied to medical document quality control, the efficiency is greatly improved, and even quality evaluation can be carried out on past historical archives.

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 an intelligent medical insurance settlement auditing method, device and system based on multi-modal deep learning and interpretability, and aims to improve the intelligence and accuracy of medical insurance auditing, reduce manual intervention and improve the auditing efficiency. The method comprises the following steps: receiving multi-modal data, such as medical documents, diagnosis and treatment records, medicine information, image data and the like, and carrying out data cleaning and standardization processing; a multi-modal neural network model is adopted, a multi-channel convolutional neural network (CNN) is used for image feature extraction, and text data are processed in combination with a long short-term memory (LSTM) network; deciding and providing interpretation for the deep learning model using an interpretability technique (such as LIME); and an auditor carries out manual confirmation and modification according to system suggestions. According to the system, automatic and precise settlement auditing can be realized, a decision-making basis is provided for auditing personnel through the interpretability module, and the efficiency and the accuracy of settlement auditing are remarkably improved.

The application discloses an OCR-based medical document intelligent identification method and system, relates to the technical field of document identification, and comprises the following steps: collecting a medical document image through a mobile terminal, generating a binary image based on the collected image through a U-Net combined with multi-scale feature fusion and an attention mechanism, and performing clipping; based on the clipped binary image, extracting text information through an OCR model, and extracting structured fields according to the layout rules and geometric distribution of the medical document; and performing deterministic rule judgment and risk assessment on the structured data. The application reduces the image calculation complexity through standard gray scale processing, improves the text region feature extraction accuracy through the U-Net structure combined with the CBAM attention mechanism, realizes effective fusion of multi-scale features and noise suppression in combination with the Attention Gate, realizes text direction correction in combination with the Hough transform, and improves the text detection robustness and recognition accuracy.

This invention discloses a method, apparatus, and device for evaluating the quality of medical document images, relating to the field of medical technology. The method includes: acquiring a medical document image to be evaluated; detecting text boxes containing text content within the image; inputting the text boxes into a text fuzzy prediction model to obtain a fuzzy prediction result for the text boxes; and determining the quality of the medical document image based on the fuzzy prediction result. This method can accurately evaluate the quality of medical documents that are partially clear and partially blurred.

The invention provides a general medical record generation method and system and a storage medium, and the method comprises the steps: receiving user input information, and analyzing the user input information to extract appeal data; processing the appeal data through a large language model configured with a structured cue word project to dynamically generate a structured medical record text conforming to a preset medical document specification; multi-dimensional verification is carried out on the structured medical record text to obtain a final medical record text, and the multi-dimensional verification at least comprises term normalization verification based on a medical term library and numerical value consistency verification based on appeal data. According to the invention, the medical record can be efficiently and accurately generated.

This application discloses a method and system for intelligent generation and quality control of medical documents, relating to the field of medical information technology. The disclosed method and system for intelligent generation and quality control of medical documents acquires multi-source medical data, constructs a medical semantic graph network, performs clinical logical reasoning, generates and verifies medical documents, and updates them based on feedback. It can automatically construct a patient-specific medical knowledge network, identify deep semantic relationships between clinical entities, generate logically rigorous professional descriptions, and improve the quality of medical documents and medical safety.

The application provides a medical intelligent verification method and system, and relates to the technical field of automatic verification. The multi-modal large model medical verification system provided in the application can automatically read, understand and check prescriptions and various medical documents, accurately aligns key information by using visual positioning technology, makes compliance judgment in combination with medical knowledge, and is fully explainable, traceable and does not produce hallucinations, thereby greatly reducing the workload of doctors and improving medical safety.

The present application relates to the technical field of artificial intelligence, in particular to a fine-tuned medical knowledge graph question answering system, comprising an input module, a medical Neo4j graph database module, a query module, a fine-tuned medical LLM module and an output module. The fine-tuned medical knowledge graph question answering system converts unstructured medical documents into structured entities, relationships and attributes by constructing a knowledge graph in the medical field and storing them in a Neo4j graph database. In the question answering process, the system first uses the fine-tuned medical large language model to understand the user's question, then generates a Cypher query statement to retrieve structured knowledge from the knowledge graph, and finally outputs the retrieval results and the generation results of the large language model. Compared with the pure large language model question answering system, the "hallucination" problem of the model in the medical professional field is effectively reduced, the accuracy, factuality and traceability of the answer are significantly improved, and the user can verify the rationality of the answer combined with the structured information in the knowledge graph.

The application provides a medical document image information extraction method and system based on computer vision, and relates to the technical field of medical document image processing. The application separates a medical record image into a layout reference layer and a dynamic data layer based on chrominance difference; extracts continuous line segments of the layout reference layer to construct a layout grid and obtain reference coordinates and a reference threshold; scans discrete pixel regions of the dynamic data layer, peels off occluded pixels, and performs connectivity compensation; calculates free coordinates and literal values of the repaired discrete pixel regions, and marks misaligned discrete pixel regions; calculates target mounting vectors based on spatial offset and semantic correction weight to complete position updating; and assembles target structured data according to spatial arrangement order. The application combines layout constraints and semantic verification to effectively improve the accuracy of medical record image information extraction.