258 results about "Medicine field" patented technology

The invention belongs to the field of natural language processing, and provides a pre-training large model physical examination text labeling method and system based on active learning. The method is realized through the following process: collecting and sorting structured and unstructured physical examination data, manually pre-labeling a small number of samples by an expert and standardizing a format; selecting a pre-training large model adaptive to the medical field task; designing a domain-specific cue word template to strengthen semantic understanding of the model; performing parameter optimization by adopting supervised fine tuning and low-rank adaptation technologies; an active learning closed loop is constructed, uncertain samples are selected through multiple rounds of iterative training to supplement labels, and the model is dynamically optimized by using a verification set; and the optimized model is applied to intelligently label the remaining data. According to the scheme, the key problems of low efficiency, poor rule method generalization ability, high machine learning model computing power demand and insufficient large model field adaptation of traditional manual labeling are solved, and the automatic extraction precision of medical entities such as pathological features and clinical indexes in the physical examination report can be effectively improved.

Improved novel 3D imaging and reconstruction methods for quantitative and accurate 3D imaging used in multiple industries, segments, and applications within each. Improvement includes methods and hardware configured to improve performance and lower radiation and improve accessibility. Using simplified system matrix and datasets of little scatter interference in tomographic image acquisition and reconstruction, in some cases, using time continuity and spatial continuity to generate parameter data for substances within VOI for a number of key applications in medical and non-medical fields.

The invention provides a medical large model intelligent inquiry reasoning method and system, and the method comprises the steps: obtaining the input information of a user, and carrying out the processing of the input information, including intention recognition, entity recognition and historical information summarization; sequentially performing pre-retrieval, multi-modal database retrieval and knowledge reordering according to the information obtained after processing; generating an inquiry strategy according to the information obtained after knowledge reordering, and performing information retrieval enhancement generation on the medical large model according to the information obtained after knowledge reordering and the inquiry strategy; and generating content according to the generated medical large model. According to the method, reasoning and replying of symptom description and health problems input by the user are achieved through the generative medical large model and multi-modal data retrieval, the method is suitable for intelligent health management and medical consultation in the medical field, and the intelligent level of medical services and the user experience are effectively improved.

The invention discloses a medical education-oriented knowledge graph construction, retrieval and application system, and the system comprises a data collection and integration module which is used for collecting and integrating various medical professional knowledge, and forming a medical knowledge material library; the intelligent algorithm module is used for constructing a knowledge graph according to the medical knowledge material library and then providing structured knowledge support for learning and reasoning of the knowledge graph by using a graph neural network model; the knowledge retrieval and content generation module is used for performing retrieval in the knowledge graph according to the input content and providing the retrieved knowledge to the large language model to obtain the corresponding content; and the knowledge application service module is used for providing medical education service according to learning and understanding of the graph neural network model on the knowledge graph and integration of the big language model on semantic information in the knowledge graph. The intelligent development of the medical field is better promoted, and more efficient and accurate support and service are provided for medical education, research and the like.

A case generation method, used for generating cases in professional fields such as the field of law, the field of medicine or the field of economy. In the method, on the basis of using existing cases as reference cases, similar new cases can be generated by means of using a large language model, and after the new cases are generated, the accuracy of the new cases can be evaluated from different dimensions by means of a large language model; and when the accuracy of the new cases satisfy requirements, the generated new cases are output. The present application uses knowledge repositories and semantic comprehension capabilities of large language models to instruct the large language models to generate new cases on the basis of existing cases, and further evaluate the generated new cases from different dimensions, thus solving the hallucination problem of large language models, and ensuring the accuracy of generated new cases. Moreover, the present solution uses large language models to automatically generate cases without requiring manual construction of cases by people, thereby effectively improving the case construction efficiency.

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 relates to a contrast test recognition method and device based on a large language model and a storage medium, and is applied to the technical field of medical information, and the method comprises the steps: carrying out word segmentation processing on obtained medical literature data, and then combining the processed medical literature data with RCT-related cue words to obtain an instruction data set; setting a large language model framework and a corresponding loss function, performing iterative training on the model framework through the instruction data set, inputting to-be-classified medical literature data into a trained RCT recognition large language model, and outputting an RCT recognition result according to a format agreed by cue words; according to the scheme, the cue word is combined with the training data, the training data does not need to be labeled, and the problem that the generalization ability of an existing machine learning model in different medical fields and different research problem data sets is greatly different, and the machine learning model extremely depends on a large number of labeled data sets is solved.

The invention discloses a retrieval enhancement generation method and device based on a medical knowledge fine-tuning language model. The method comprises the steps of constructing a medical knowledge base, performing fine-tuning on the medical language model, inputting medical questions by a user, retrieving the knowledge base, screening knowledge fragments, generating answers and the like. And the medical knowledge base is constructed through semantic partitioning, so that the integrity and continuity of related knowledge are ensured. And a medical knowledge fine tuning model is used as a screener to further screen high-quality knowledge fragments most related to user questions, so that the professionality and reliability of generating answers are improved. The method is suitable for the medical field, and can provide more accurate and more professional medical consultation services for patients and doctors. The fine-adjusted medical model is innovatively used as a filter, knowledge fragments are evaluated from multiple dimensions, it is ensured that the knowledge fragments are highly related to questions, powerful support is provided for generating professional and accurate medical answers, and the precise medical consultation requirement of a user is better met.

The invention discloses a retrieval enhancement generation method based on a medical map, and belongs to the field of artificial intelligence and medical information processing. The method comprises the following three main steps: (1) constructing a medical map: constructing a multi-level medical knowledge map through semantic document segmentation, element extraction, hierarchical linking, relation linking, label generation and map combination; (2) map retrieval: retrieving related information in the constructed map according to user query by using a U-retrieve method; and (3) text generation: accurate and comprehensive answers are generated based on retrieval results. According to the method, by integrating large-scale medical literatures, textbooks and clinical data, the answer accuracy and interpretability of the large language model in the medical field are remarkably improved. Detailed answers containing evidence sources can be generated, and the illusion problem is effectively avoided. A powerful tool is provided for the fields of medical decision support, medical education, patient consultation and the like, and wide application of artificial intelligence in the field of medical health is expected to be promoted.

The invention belongs to a data generation method, provides a multi-modal medical data generation method and a related device, and aims to solve the technical problems of multi-modal medical data resource limitation, modal missing and data scarcity of an existing medical data generation method. Obtaining a fine-tuning medical large model; obtaining a text instruction and a multi-modal data stream associated with the task, and obtaining a task type, an existing modal list, a target modal list and a task instruction after analysis of the text instruction according to the text instruction and the multi-modal data stream; according to the existing modalities in the existing modality list and the task instruction, obtaining a comprehensive shared feature representation; encoding and decoding the comprehensive shared feature representation to obtain a converted feature instruction, and generating a modal mark for a target modal in the target modal list; and updating a generation state through dynamic estimation based on the converted feature instruction.

An AI-based chatbot diagnostic system configured to: receive data from at least one data source related to at least one medical field or disease; store the received data in a medical data repository, wherein the data is categorized according to at least one criterion; access a medical index engine that uses the categorized data and a medical reference database to create and update an index for individual medical fields; utilize interactive data for training a diagnostic model in conjunction with a medical index; select the trained diagnostic model most suitable for a given diagnostic scenario from a model storage database; and manage and interact with the selected diagnostic model using an AI model manager.

The invention discloses high-performance modified calcium carbonate and application thereof in a breathable film. The modified calcium carbonate is prepared by combining a three-element composite modification system composed of stearic acid, titanate and a silane coupling agent with a disc-nest mill three-stage gradient grinding process and antibacterial-hydrophobic dual-functionalization treatment of silver-loaded hydroxyapatite and a fluorinated modifier. The indexes of the prepared modified calcium carbonate are as follows: the median particle size (D50) is 1.8-2.2 mu m, the particle size (D97) of 97% particles is less than 6.5 mu m, the particles with the particle size of 1-5 mu m account for more than 60%, and the particles with the particle size of more than 10 mu m are less than 100 ppm; the content of calcium carbonate exceeds 97%, and the content of harmful impurities such as Fe is less than 200ppm; the whiteness is higher than 95; the oil absorption value is less than 12.5%, and the water content is less than 0.18%. When the composite material is applied to the breathable film in the medical field, the mechanical property, the antibacterial property and the hydrophobic property of the breathable film can be remarkably improved, the special requirements of the medical field on the breathable film are effectively met, and the composite material has good application prospects and industrial value.

The invention relates to a guiding type improved neural network ankle joint fracture classification method, system and device, and belongs to the field of computer-aided medical image processing.The classification method comprises the steps that S1, database images are collected and enhanced; s2, training a segmentation network model by using the segmentation data marked with the fibula region; s3, inputting the fracture type classification data into the trained segmentation network model, predicting a fibula area, and taking a prediction result as guidance to act on the improved neural network classification model; s4, inputting the fracture type classification data into the improved neural network classification model guided by the prediction result for training; and S5, verifying a classification result by using a test set, and evaluating the model by using a statistical index. The ankle joint fracture type can be judged, the method has the advantages of being high in diagnosis accuracy, high in speed and the like, and the problem that a traditional deep learning method is prone to being interfered by other information in images in the medical field to make misjudgment is effectively solved.

The invention provides a method for acquiring a three-dimensional model of an ankle skeleton of a human body by simulating load-bearing CT scanning. The method comprises the following steps: acquiring CT scanning data; uploading the CT scanning data to medical image processing and three-dimensional reconstruction software to obtain an initial scanning image; constructing a three-dimensional model according to the initial scanning image; and carrying out model verification on the three-dimensional model to obtain a skeleton three-dimensional model. According to the method, the high-resolution three-dimensional model of the ankle skeleton can be obtained, and reliable support is provided for medical research, clinical diagnosis and treatment of the ankle and research and development of medical instruments. Compared with a traditional method, the data obtained by simulating the load is more practical, the model quality is remarkably improved through the advanced technology and algorithm, and development of related research and application in the medical field is promoted.

The invention discloses a new medicine application of a local injection acoustic response stent in enhancing ultrasound histotripsy. The acoustic response scaffold (ARS) comprises a scaffold material, fluorocarbon gas or liquid fluorocarbon particles with liquid-vapor phase change capacity, and microbubble cloud in the scaffold generates a strong cavitation ablation treatment effect in a target area under the action of low-frequency and low-sound-pressure ultrasound. Fluorocarbon gas or liquid fluorocarbon particles contained in the ARS form microbubble cloud and ideal cavitation nuclei under low-frequency low-sound-pressure ultrasonic excitation, inertia cavitation resonance is achieved under the ultrasonic action, and then cavitation mechanical damage with local tissue controllable strength is generated. The low-intensity ultrasonic histotripsy has the characteristics of low sound pressure, low sound intensity, high targeting property, small tissue damage outside a target region, simplicity in instrument manufacturing and the like, and the targeting property and the safety of treatment are improved. The method does not need very accurate ultrasonic focusing, reduces the operation difficulty, and has application prospects in multiple medical fields such as prostatic hyperplasia, tumor ablation and tumor immunotherapy.

The invention relates to the technical field of micro-fluidic chips and biosensors, in particular to a paper folding type micro-fluidic paper chip analysis device for detecting ATP (adenosine triphosphate) based on a difunctional DNA tetrahedron and application of the paper folding type micro-fluidic paper chip analysis device. The analysis device comprises a detection layer and a washing layer, and is characterized in that a difunctional DNA tetrahedron system containing detection ATP is added into a reaction zone; wherein the bifunctional DNA tetrahedron comprises a capture DNA tetrahedron (B-TDN) and a detection DNA tetrahedron (D-TDN), and a single-stranded DNA sequence extending from any vertex of the tetrahedron formed by the B-TDN and a single-stranded DNA sequence extending from any vertex of the tetrahedron formed by the D-TDN can be complementary. The paper folding type micro-fluidic paper chip analysis device based on the difunctional DNA tetrahedron has the advantages of being small in size, low in scientific research cost, biodegradable, high in sensitivity, good in selectivity and the like, a new method is provided for rapid ATP detection in the medical field, and the paper folding type micro-fluidic paper chip analysis device is suitable for remote areas and economic outdated areas.

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

The embodiment of the invention relates to a method and a device for generating a medical report based on evidence-based medicine. The method comprises the following steps: acquiring patient information and an original clinical problem; inputting the patient information, the original clinical question and the first cue word into a target model to generate a structured clinical question; the target model is a medical field large language model; retrieving a medical literature library according to the clinical problem to obtain a plurality of medical literatures; inputting the target medical literature and the second cue word into a target model, so that the target model performs bias risk assessment on the target literature, and a structured initial assessment result of the target literature is generated; inputting the clinical problem, each medical literature, the initial evaluation result of each medical literature and the third cue word into a target model, and enabling the target model to carry out thinking chain gradual reasoning based on a GRADE evaluation process in the third cue word to generate a medical report; the medical report includes medical suggestions for clinical issues, and recommendation intensities and evidence quality levels of the medical suggestions.

The invention provides a self-supervised pre-training method based on federal learning, so as to improve the liver cancer image segmentation effect. Medical image segmentation aims at accurately extracting a region of interest to assist diagnosis and treatment, but a traditional method depends on a large amount of labeled data and is difficult to obtain in the medical field. According to the method, a ViT encoder is used for carrying out feature learning on an unmarked liver cancer masking image, masking pixel blocks are reconstructed through a ViT decoder, and then embedded features are transmitted to a downstream task; a current local model and a previous global model are combined, the training process is optimized, the feature representation distance between the current model and the global model is reduced, meanwhile, the feature representation distance between the current model and the local model is increased, a client model gradually approaches the global model through comparison, and a better global model is obtained to segment a liver cancer image. According to the method, performance reduction caused by data isomerism is effectively relieved, the generalization ability of the model is improved, and the problem of insufficient data labels is solved, so that application in the field of artificial intelligence assisted medical treatment is promoted.

The invention relates to a method for processing medical images of human tissue of an area of a patient's body and in particular of the breast by means of a medical imaging device (1) comprising a microwave probe array consisting of K>1 probes spaced apart from one another, the array comprising P>1 different configurations defining transmitting probes and receiving probes for one or more position(s) around the area, in which the transmitting probes are configured to transmit microwave signals so as to illuminate an area of the body and the receiving probes are configured to receive microwave signals after scattering and reflection in the area, the probes being capable, in a complementary manner, of being configured to transmit and receive simultaneously.

The invention discloses a graph embedding-based medical field retrieval enhancement method, a question-answering system and equipment, and the method comprises the steps: collecting a question-answering pair data set in the medical field, and combining questions in the question-answering pair data set with questions queried by a user to establish a question set; classifying sentences in the question set through a large language model (LLM), and judging categories of departments corresponding to the sentences; for the problems in the same category, extracting medical entities in the problems through a UIE model, and constructing a problem map; on the basis of the obtained problem graph, graph embedding of all nodes in the sequence is obtained through random walk and a Word2Vec algorithm to serve as vector representation of the nodes, and the vector representation is stored in a vector database; searching N most similar problems from the vector database, and constructing a search prompt; after the retrieval prompt is acquired, the generator generates a final answer from the retrieval prompt. According to the method, the understanding and reasoning capabilities of the model on user questions are enhanced, and accurate information retrieval and knowledge enhancement generation in the medical field are better supported.

The invention discloses an obsessive-compulsive disorder diagnosis system based on brain multi-modal indexes, and belongs to the field of mental diseases. The problem of lack of a cross-modal feature fusion mechanism is solved. The system comprises an electroencephalogram signal acquisition unit used for acquiring an EEG signal of a testee under a preset stimulation normal form and executing preprocessing operation; the brain imaging data acquisition unit is used for synchronously acquiring brain structure imaging data and brain function imaging data of the testee; the multi-modal data fusion unit is used for extracting frequency band power spectrum density characteristics and event-related potential amplitude or incubation period characteristics from the EEG signals; performing standardization processing on the EEG features, the sMRI structural features and the fMRI functional features; integrating modal features by adopting a weighted average fusion algorithm; screening fused feature subsets through a recursive feature elimination method; and the diagnosis model unit is used for inputting the fusion feature vector into a trained SVM classification model and outputting an obsessive-compulsive disorder diagnosis result. Used in the medical field.

The invention discloses a medical question-answering system based on a large language model, belongs to the field of large language models in artificial intelligence in the field of computers, and particularly relates to the medical question-answering system based on the large language model. The objective of the invention is to solve the problems of low answering accuracy and poor medical problem solving ability of a general large language model in the medical field. The system comprises a large language base module used for selecting an open source large language model of a general field as a base model; the knowledge instance sampling module is used for sampling knowledge instances from the knowledge graph; the question-answer data generation module is used for generating question-answer data according to the knowledge instances by using a large language base model; the large language base model fine tuning module is used for performing fine tuning on the large language base model by using the generated question and answer data to obtain a fine-tuned large language base model; and the reply generation module is used for answering the to-be-processed medical knowledge question based on the fine-tuned large language base model.

The invention discloses a stem cell evaluation method and system based on image analysis, relates to the field of medicine, solves the problem of poor evaluation effect of a stem cell evaluation method, and comprises the following steps: S1, acquiring stem cell density monitoring data; s2, creating a cell type division statistical model to perform density analysis on a plurality of different types of stem cells to obtain a first stem cell quality evaluation coefficient; s3, performing stem cell shape analysis on the patient to obtain a second stem cell quality evaluation coefficient; s4, performing stem cell activity analysis on the patient to obtain a third stem cell quality evaluation coefficient; and S5, performing stem cell comprehensive quality evaluation on the patient according to the first stem cell quality evaluation coefficient, the second stem cell quality evaluation coefficient and the third stem cell quality evaluation coefficient. According to the stem cell quality evaluation method, the comprehensiveness, the accuracy and the pertinence of a stem cell quality evaluation result are improved.

Machine learning model change management in an online Software as a Medical Device (“SaMD”) is provided. One or more machine learning models implemented in a medical domain may be monitored where the one or more machine learning models are continuously updated. One or more changes to the one or more machine learning models. The one or more machine learning models, having the one or more changes, are certified as being in compliance with performance characteristics and compliance criteria.

The invention relates to the technical field of clinical data synthesis, provides a controllable condition clinical data synthesis method and device supporting logic relation perception, and aims to solve the problem of logic inconsistency in an existing synthesis method and flexibly generate data meeting user-defined conditions. The method comprises two key stages: a logical relationship perception stage: capturing inherent semantic information in original data, and learning a complex logical relationship between clinical data columns; and a conditional diffusion model generation stage: fitting data distribution characteristics by using a diffusion model, and integrating conditional semantic embedding to realize controllable clinical data synthesis. Through mutual cooperation of the two stages, a difficult electronic clinical data generation task is decomposed into two relatively simple problems, and the problems of logic inconsistency and lack of controllability are efficiently solved. According to the scheme, the quality and availability of the synthesized data are greatly improved, and a set of high-quality data synthesis scheme is provided for the medical field.

The invention discloses a post-radiotherapy patient nursing auxiliary decision-making system based on artificial intelligence, and relates to the field of medical rehabilitation nursing, and the system comprises a management and control module which serves as a core management end of each function module and carries out the editing and sending of a control instruction; the data archive library is used for storing all the collected user personal data and analysis data thereof; the data acquisition module is used for acquiring basic physique data, illness state related data and radiotherapy plans of the patient through a preset period; individual data of patients are analyzed through a prediction model, personalized nursing suggestions are generated based on medical history, health indexes and radiotherapy plans, personalized nursing schemes are provided for each patient according to curative effect analysis of similar patients, customization is carried out according to unique illness states, physiques and treatment plans of the patients, professional knowledge and data in the medical field are combined, and the patient health management system is established. Potential abnormal changes are output, and patients and medical staff are helped to make timely decisions.

The invention belongs to the field of medicine, and particularly relates to a physical examination data significance analysis method based on multiple examination correction. The method comprises the following steps: firstly, carrying out transverse and longitudinal cleaning on physical examination data, then, carrying out classified analysis on the data, calculating the proportion of each disease in different classifications according to a set threshold value, finally, carrying out significance test on a statistical analysis result by utilizing a chi-square test method, and carrying out correction by utilizing a Bonferroni multiple comparison correction method. Saliency analysis is carried out through multiple comparison and correction methods of physical examination data, various physical examination indexes can be synthesized, the physical health condition level of a user can be described objectively, theoretical guidance is provided for health guidance of a doctor on a physical examination person and a physical examination item method for accurately positioning the patient, and meanwhile, the physical examination accuracy is improved. The communication cost between the physical examination user and the doctor is reduced, and the user is urged to pay more attention to the overall health condition level.

The invention discloses an intelligent pulmonary tuberculosis detection method and system based on images and clinical data, and belongs to the field of medicines.The method comprises the steps that S1, high-resolution chest X-ray images and clinical data such as serum C-reactive protein concentration, erythrocyte sedimentation value, erythrocyte distribution width and body temperature are collected; s2, performing non-linear contrast enhancement on the image, and extracting lung gate texture, lung field transparency and cavity edge features; s3, fusing the image and the clinical features through a graph attention mechanism, and generating a high-dimensional fusion vector; s4, risk grade discrimination is carried out through a multi-scale residual mapping network, and five-grade labels are output; and S5, optimizing the model by utilizing the pathology definite diagnosis sample in combination with the cross entropy and the center loss. The method has the beneficial effects that accurate grading evaluation of the pulmonary tuberculosis risk is realized, and the accuracy of early screening and the clinical interpretability of the model are improved.

The invention belongs to the field of computational medicine, particularly relates to a method and a system for constructing full-parameter digital twinning of a patient and performing treatment simulation, and aims to solve the problems that an existing digital twinning model cannot fuse multi-scale data, lacks dynamic optimization capability and is disjointed with a clinical decision process. The method comprises the following steps: acquiring and integrating multi-source heterogeneous data of a patient; performing cross-scale fusion modeling based on the data, and constructing a personalized digital twinborn initial parameter set containing genetic background correction; driving a multi-physics field coupling engine to simulate an intervention effect, constructing a reverse optimization problem taking real-time monitoring data as a dynamic constraint, and solving an optimal intervention scheme parameter; and carrying out verification simulation on the optimization scheme and generating a treatment report containing quantitative evaluation and risk early warning. According to the invention, by constructing a full-parameter and mechanism model and introducing a dynamic closed-loop optimization verification process, the personalized precision, safety and clinical decision support value of treatment scheme simulation are significantly improved.