175 results about "Medical model" patented technology

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 relates to the technical field of medical information processing, in particular to a health data processing method and system based on a distributed ledger library, and the method comprises the steps: a node firstly links a health data abstract to obtain a differential privacy budget token and a reversible tensor hash key; then, carrying out encryption training on local model parameters by using the key, injecting Gaussian noise according to budget to generate a differential protection gradient, and carrying out uplink together with zero-knowledge proof; the account book end decrypts the threshold value, uses a structural equation model to deduce a causal correction matrix according to the reputation weight aggregation gradient, and automatically adds a budget and turns the key when the concept drifts and the budget is insufficient; the operation mechanism splices the aggregation parameters and local parameters, generates a gating vector in combination with a causal correction matrix and a reputation weight, outputs disease risk prediction, and only uploads prediction hash and error information; chain-level audible privacy protection, dynamic budget management and hybrid deviation suppression are realized, and the safety and accuracy of a cross-institution medical model are improved.

The invention relates to the field of medical model training, and particularly discloses a medical treatment guide model training method and system, a terminal and a medium, and the method comprises the steps: carrying out the incremental pre-training of a selected large language model through priori knowledge, and obtaining a basic large language model; acquiring a public real doctor-patient dialogue data set; constructing a doctor-patient dialogue template, and generating and constructing a doctor-patient dialogue data set by using a third-party large language model based on the doctor-patient dialogue template; combining the real doctor-patient dialogue data set and the constructed doctor-patient dialogue data set into a data set, and recording the data set as an initial doctor-patient dialogue data set; performing duplicate removal preprocessing on the initial doctor-patient dialogue data set to obtain a doctor-patient dialogue training data set; and using the doctor-patient dialogue training data set to carry out supervision fine tuning training on the basic large language model to obtain a doctor-seeing and hospital-guiding model. According to the invention, the joint training data set is formed based on the public data set and the automatic construction data set, so that the accuracy of the medical treatment guide model is improved.

The invention discloses a tumor whole course intelligent management platform and method, and particularly relates to the technical field of medical model management. Carrying out semantic analysis and uniform format processing on the multi-modal data from different sources by utilizing a natural language processing technology and a standardized tool; using a feature selection algorithm to screen key features related to tumor types and treatment schemes; through analyzing mutual information of the key features and the target variables and mutual information among the key features, evaluating the degree of redundant hidden dangers existing in the screened key features; the semantic consistency of the multi-modal data from different sources is evaluated by comparing and analyzing the prediction label and the actual label of the multi-modal data; and comprehensively analyzing the redundant hidden danger degree of the screened key features and the semantic consistency of the multi-modal data from different sources, and judging whether the personalized treatment effect prediction model can be constructed or not.

The invention provides a separate hospital guidance method and system for a large medical model, electronic equipment and a storage medium. The method comprises the following steps: receiving symptom description information of a natural language of a patient; performing natural language processing on the symptom description information by utilizing a large medical model, and determining symptom entity information and basic information of the patient; inputting the symptom entity information into a pre-constructed medical knowledge graph for matching, and determining candidate diseases and departments thereof corresponding to the patient; generating personalized triage suggestions by using the medical big model according to the candidate diseases and the departments thereof corresponding to the patient and in combination with the basic information of the patient; that is to say, according to the medical big model and the medical knowledge graph, symptom entity identification is performed on the symptom description of the patient, and then the corresponding candidate disease and the department thereof are determined, so that a manual separate hospital guide mode is avoided, the separate hospital guide efficiency and accuracy are improved, the waste of medical resources is reduced, and the fairness and sustainability of medical services are realized.

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 relates to the technical field of medical image intelligent analysis and artificial intelligence auxiliary diagnosis, in particular to a medical image disease course prediction system based on an industrial neural network, and the system comprises a reference generation module which is used for obtaining static image data; processing the static image data by using a physical perception neural network to generate a pure ideal state reference; a perturbation simulation module; the industrial kinetic parameters are used as perturbation terms to be superposed to a pure ideal state reference, and a theoretical damaged state is generated; the projection verification module is used for acquiring real multi-modal observation data; generating a real residual error; generating a theoretical residual error based on the theoretical damaged state and the pure ideal state reference; calculating a manifold coupling confidence coefficient; the closed-loop correction module is used for performing inversion optimization on the industrial kinetic parameters; outputting a disease course prediction result according to the manifold coupling confidence coefficient; according to the method, the problem that a traditional medical model lacks physical consistency explanation is solved, and the credibility of artificial intelligence auxiliary diagnosis is remarkably improved.

The invention provides a case analysis method and device, equipment, a storage medium and a program product, and the method comprises the steps: obtaining to-be-analyzed case information and medical model information, carrying out the case keyword extraction and feature vector extraction of the to-be-analyzed case information, and obtaining a case keyword set and an initial feature vector; determining a screening analysis knowledge base cluster according to the medical model information and the case keyword set, and inputting the initial feature vector and the screening analysis knowledge base cluster into a preset analysis model for case information screening analysis to determine a case analysis result; according to the method, the knowledge base subset suitable for the current case is screened by combining the medical model information and the case keyword set, the analysis precision and the efficiency of the analysis process are improved, higher-level semantic understanding and logical reasoning are carried out through the feature vectors, the accuracy and reliability of case analysis are remarkably improved, and the user experience is improved. And the quality and the credibility of the answers are improved, so that the user experience is improved, and the practicability and the reliability are enhanced.

The invention relates to the technical field of intelligent medical treatment, and discloses a medical large model driven cross-department collaborative prescription generation method and system. According to the method, electronic medical records of patients and prescription data of multiple departments are acquired, key information is structurally extracted to generate directional vectors, and transfer learning and fine adjustment are performed by using historical conflict cases and a drug knowledge graph based on a medical basic large model and an AI chip, so that a collaborative prescription model is constructed. The model can identify drug incompatibility and dosage risks among departments and generate a prescription suggestion set. A doctor can dynamically correct a prescription based on feedback, a collaborative report containing a medication time sequence, a monitoring index and an emergency scheme is generated after multiple rounds of collaborative optimization, and the safety and effectiveness of multi-department combined medication of complex diseases are remarkably improved.

The invention discloses a medical model visual interaction control method and system based on gesture recognition, and relates to the technical field of medical image interaction, and the method comprises the steps: S1, achieving the three-dimensional imaging and processing of CT and MRI image data through VTK; s2, carrying out image acquisition on an operator, and capturing a gesture action image of the operator; s3, performing gesture segmentation on the captured gesture action image by adopting a gesture segmentation algorithm; s4, analyzing the gesture change of the operator based on gesture segmentation; and S5, performing real-time control on the medical three-dimensional model according to gesture change analysis. According to the medical model visual interaction control method based on gesture recognition, a doctor can operate through natural gestures, and dependence on traditional input equipment (such as a mouse and a keyboard) is reduced. The visual interaction mode can better conform to the operation habits of doctors, the working efficiency is improved, and the visual interaction mode is particularly important in medical scenes where quick response and complex information processing are needed.

The invention discloses a doctor service method based on a medical large model and a related device. The method comprises the following steps: acquiring service request data of a target doctor in a target service scene; wherein the target service scene is any one of the following medical service scenes: diagnosis decision suggestion, treatment scheme recommendation, medical record generation, medical record quality control and medical record information management; and analyzing the service request data based on the large medical model to obtain a medical service suggestion output by the large medical model to the target doctor in the target service scene. In this way, medical service suggestions can be automatically provided for doctors.

The invention discloses a medical model training system and method for protecting medical data privacy. The system comprises a central server and clients of all participants participating in federated learning. The client is used for training a local model by adopting local data; in the training process, a local difference privacy technology and target noise intensity are adopted to carry out noise disturbance on the gradient of each time of training; under the condition that training is finished, gradient updating of the local model is determined, and gradient data are obtained; encrypting the gradient data by adopting a homomorphic encryption technology, and sending the encrypted gradient data to a central server; the central server is used for globally aggregating the received encrypted gradient data in a pre-created trusted execution environment; in the global aggregation process, a global differential privacy technology is adopted to add noise to aggregated model parameters, model global parameters are generated, and the model global parameters are broadcasted to each client. By adopting the embodiment of the invention, the risk of medical data leakage can be reduced.

The invention discloses a full-cycle path chronic disease management system and method based on artificial intelligence, and belongs to the technical field of intelligent medical treatment, and the method comprises the steps: generating a model result based on an artificial intelligence chronic disease risk prediction model and a prescription through an active diagnosis and treatment module, and carrying out the active recognition, intervention and management of a target group; the personalized diagnosis and treatment module is used for providing refined follow-up visit, prescription and screening services according to health states and risk characteristics of different individuals; and semantic search, index generality identification and multi-source data integration capabilities of clinical data are provided through an intelligent data governance and decision support module. According to the invention, an intelligent medical new mode of active, personalized and intelligent chronic disease management is constructed, clinical decision is assisted, the management efficiency is improved, the chronic disease management level is improved, reasonable flow of medical resources is promoted, and chronic disease prevention, management and referral are promoted to develop towards the full-life-cycle management direction.

Systems and methods for computer modeling in medicine. A sort of period table of medical models is described for personalized diagnostics, prognostics and therapeutics, including at least 80 major categories of medical models. Generative artificial intelligence and geometric deep learning techniques, and algorithms including 2D and 3D graph machine learning and GenAI algorithms, are described, tailored and applied to diagnostic disease description, prognostic prediction and therapeutic development and management, including generation of novel synthetic drugs. The AI and machine learning techniques and algorithms are applied to understand each individual's genetic, RNA and protein anomalies that represent the source of many unique patient diseases. AI-enabled software agents assist physicians and researchers in building patient medical models. Several personalized medicine applications of individualized medical modeling include cardiovascular disease, cancer, neurological disorders, immune system disorders and genetic diseases.

The invention discloses a method for manufacturing a 3D printing medical model, which comprises the following steps of: acquiring detailed structure information of a specific part of a patient by adopting high-precision medical imaging equipment; converting the acquired two-dimensional medical image data into a three-dimensional digital model by utilizing three-dimensional reconstruction software, and in the reconstruction process, accurately distinguishing different tissues and organs by adopting image segmentation to generate a three-dimensional grid; smoothing the reconstructed three-dimensional digital model, and scaling and rotating the model as required to adapt to the requirement of 3D printing; the method has the beneficial effects that through high-precision medical image data acquisition and three-dimensional model reconstruction, the accuracy and integrity of a medical model are ensured; through model optimization and 3D printing parameter setting, the printing quality and efficiency are improved, and the manufacturing cost is reduced; the completeness of the post-treatment process enables the appearance, the hand feeling, the safety and the like of the medical model to be remarkably improved.

The embodiment of the invention discloses an evidence tracing method and device for reply content of a medical model, a medium and a product. The method comprises the following steps: matching question data with a knowledge text in a knowledge base to obtain a first confidence coefficient which is a standard similarity between the question data and the knowledge text; based on the first confidence coefficient and the first optimal parameter, screening out an actual candidate text from the knowledge base, and obtaining actual reply content in combination with the medical model; semantically segmenting the actual reply content into a plurality of actual fine-grained fragments, and matching the actual fine-grained fragments with the knowledge text to obtain a second confidence coefficient which is a standard similarity between the actual fine-grained fragments and the knowledge text; and based on the second confidence coefficient and the second optimal parameter, the tracing evidence of the actual fine-grained fragment is screened out from the knowledge base, so that the tracing result of the actual reply content can be displayed. According to the method, through RAG retrieval and traceability retrieval, the original source of the reply content can be reversely verified, high-precision traceability is achieved, and interpretability is enhanced.

The invention relates to a voice interaction large model family health assistant dialogue method and device, equipment and a medium. The method comprises the following steps: carrying out fragmentation processing according to an original voice stream of a user to generate an audio fragment with a medical mark, and carrying out voice recognition and entity extraction on the audio fragment to generate a dynamic entity map; generating an evidence-based decision prompt based on the map, inputting the prompt into a preset medical big model for processing, and outputting a result containing an essential symptom list; and according to the symptom matching degree of the necessary symptom list and the dynamic entity map, generating a diagnosis report or a question-asking list, if the diagnosis report is output, performing medical rule chain verification operation on the diagnosis report to generate a quality control report, and based on the question-asking list or the quality control report, generating a synthetic voice stream. According to the method, through medical intention directional screening, map entity analysis, large model diagnosis, voice synthesis and the like, the voice recognition accuracy, the diagnosis suggestion reliability and the inquiry interaction efficiency of the family health assistant in the medical scene are improved.

The utility model belongs to the technical field of medical models, and discloses an abdominal puncture scoring training model capable of being locally detached and replaced, which comprises a trunk model provided with a round hole, and the round hole is a blind hole; the sliding shaft block is clamped in the round hole in a sliding mode, one end of the sliding shaft block is connected with a driving piece used for driving the sliding shaft block to move along the round hole, the driving piece is arranged at the bottom of the round hole, and a puncture groove coaxially placed is formed in the end, away from the driving piece, of the sliding shaft block; the simulated leather is detachably connected to the end, away from the bottom of the round hole, of the sliding shaft block, and the simulated leather covers the puncture groove; the purpose of independently replacing the simulated leather on the sliding shaft block can be achieved, the problems that the puncture hand feeling is poor and puncture training is not in place due to the fact that the structure of the simulated leather is damaged are solved by replacing the simulated leather in time, meanwhile, the training model does not need to be overall replaced, and the maintenance and replacement cost is reduced.

The invention relates to the technical field of natural language processing, in particular to a multi-modal medical large model application and fine adjustment method and device and computer equipment. The multi-modal feature data is input into a pre-trained multi-modal medical large model, an output result is obtained, the multi-modal medical large model comprises a multi-modal semantic alignment model and an integrated enhanced medical large model, and the multi-modal semantic alignment model comprises a multi-modal semantic alignment model and an integrated enhanced medical large model; the multi-modal semantic alignment model is obtained by training an initial multi-modal semantic alignment model through a multi-modal feature sample vector, a representation extraction sample vector and a semantic tag corresponding to the multi-modal feature sample vector. Knowledge and capability of a plurality of source medical models are integrated, dependence on data resources is reduced, generalization capability of the models is improved, and accuracy and reliability of diagnosis are enhanced; multi-modal features are extracted, the semantic alignment effect is optimized, and information loss and semantic offset are avoided.

The invention discloses a medical information processing method, system and device based on video data and a storage medium. The quality and efficiency of medical services can be improved. The method comprises the following steps: acquiring medical processing data; preprocessing the medical processing data to obtain preprocessed data; constructing a medical knowledge graph, wherein the medical knowledge graph is used for representing the relationship among the disease, the symptom, the examination index and the medicine; constructing a medical model based on the medical knowledge graph, and performing training and cross validation on the medical model to obtain a target medical model; constructing and generating a health digital identity model according to the preprocessed data and the target medical model; acquiring real-time video data; processing the real-time video data to extract identity information and skeleton key point information of a user in the real-time video data; and inputting the identity information and the skeleton key point information of the user into the health digital identity model to obtain medical health information of the user.

The invention discloses a medical model for simulating vascular interventional therapy, and relates to the technical field of medical treatment. The blood vessel model is detachably connected to the upper surface of the model base, one end of the blood vessel model is closed, the other end of the blood vessel model is open, and a plug is installed at the opening of the blood vessel model in an interference fit mode; the catheter is arranged in the blood vessel model in a penetrating manner, one end of the catheter is arranged on the plug in a penetrating manner, and the other end of the catheter is connected with a thrombus model; and the adjusting piece is arranged at the end part of the catheter and is used for adjusting the distance between the thrombus model and the inner wall of the blood vessel model. According to the invention, the thrombus model is arranged in the blood vessel model, so that a new medical worker can perform simulation operation, and the adjusting part is arranged and can adjust the distance between the thrombus model and the inner wall of the blood vessel model, so that the position of thrombus in the blood vessel under a real operation condition can be simulated; therefore, medical staff can adopt different modes to carry out interventional therapy on thrombus.

The invention provides a medical metadata processing and de-identification method and system for a medical large model, and the method comprises the steps: carrying out the compression of medical metadata based on an initial data distillation model, and generating compressed data; training a data distillation model by using the compressed data to generate optimized model parameters; continuing to compress the compressed data by using the optimized model parameters; and the compression step and the training step are alternately iterated until model parameters converge. According to the method, efficient training of the model of a specific task can be supported with the minimum data volume under the condition that information is almost not lost, so that the model trained on the concentrated data set is similar to the model trained on the original data set in performance. In addition, by combining the difference model, privacy removal informatization can be achieved, and the data compressed and reconstructed through the method has universality and can support artificial intelligence model training of different architectures.

The invention provides a method and device for generating pre-training data of a large medical model and electronic equipment, and the method comprises the steps: obtaining initial medical data, and carrying out the format conversion of the initial medical data, and obtaining structured medical data; identifying a noise text in the structured medical data, and clearing the noise text to obtain de-noised medical data; in response to a repeated text in the de-noised medical data, determining a repetition type of the repeated text, and performing correction processing on the repeated text to obtain initial target medical data; filtering the initial target medical data based on a preset medical text to obtain target medical data; randomly extracting a preset number of target medical data from the target medical data as to-be-detected medical data, performing quality detection on the to-be-detected medical data, determining that a detection result is qualified, performing format conversion on the target medical data to obtain training medical data, and taking the training medical data as pre-training data of a medical large model; and the quality of the pre-training data is improved.

The invention discloses a disease diagnosis method, device and equipment and a storage medium, and relates to the technical field of intelligent diagnosis, and the method provides a disease intelligent diagnosis model to assist doctors in improving the accuracy and efficiency of clinical diagnosis. The problem of insufficient disease knowledge in a general medical large model is solved, and the knowledge potential of the large model can be fully mined. The guiding model verifies the accuracy of the pre-diagnosis result according to the existing medical guideline, and the interpretability of the medical decision is ensured. The model aims to improve the accuracy and efficiency of complex disease diagnosis by integrating multi-scale knowledge enhancement, multi-agent debate optimization and self-correction diagnosis cooperation verification, and based on the scientificity of a medical guideline analysis conclusion, the accuracy and efficiency of complex disease diagnosis are improved by combining a leading-edge natural language processing technology and a clinical medical knowledge system. And a new path is developed for assisting clinical medical diagnosis of diseases.

The invention discloses a multi-modal medical image data fusion analysis system based on deep learning, and relates to the field of medical images, and the system comprises a medical image module which is used for obtaining medical image data of different modals and multiple angles; the image recognition module is used for recognizing part contours in the medical image data of all the modalities, and the image recognition module aligns all the medical image data based on the part contours in all the medical image data so as to obtain fusion data of all the modalities at different angles; and the mapping module is used for extracting part contours of the medical image data of each modal from the image recognition module and generating a medical model of a corresponding dimension based on each part contour. Through efficient fusion of multi-modal image data, limitation of a single-modal image is effectively overcome, advantages of different modals are combined through introduction of multi-modal data fusion, and more comprehensive information support is provided for accurate identification of a focus area.

The invention provides a medical model training method and device and a computer readable storage medium, and the method comprises the steps: carrying out the low-rank decomposition of the gradient of a local model, and obtaining a local first low-rank matrix and a local second low-rank matrix; training the local first low-rank matrix and the local second low-rank matrix through the received updated global first low-rank matrix, the updated global second low-rank matrix and the local data until the training of the local first low-rank matrix and the training of the local second low-rank matrix reach a set iteration condition; and generating a local medical model according to the finally obtained global first low-rank matrix, the finally obtained global second low-rank matrix, the finally obtained local first low-rank matrix, the finally obtained local second low-rank matrix and the local model. According to the method, the training efficiency is improved and the communication consumption caused by model transmission is reduced by reducing the number of the training weights and the size of the transmitted model.

The invention belongs to the technical field of artificial intelligence, and more specifically relates to a medical large model migration training method based on multi-matrix aggregation multi-dimensional features. The method comprises the following steps: collecting professional medical data, and constructing a professional medical data set; federal migration training is performed on the pre-trained large model, and specific professional medical knowledge is injected into the pre-trained large model, specifically, for the pre-trained Baichuan large model, a low-rank matrix is initialized, and the low-rank matrix and the weight are quantized; the quantized low-rank matrix is adapted to a linear layer and an attention layer of a Baichuan large model; extracting three different dimensions of knowledge including original knowledge, low-rank knowledge and global complex knowledge from the professional medical data set through multi-round efficient migration training; and fusing the extracted knowledge of three different dimensions and carrying out weight aggregation. The problems that an existing training method needs a large amount of high-quality data and computing resources and is poor in performance in specific medical scenes with stricter requirements are solved.

The invention discloses a medical record structuring system based on a large model. The system comprises a hardware acquisition device, a data processing module, a large model structuring module, a rule engine module and a patient information integration module, the method for processing the paper medical record comprises the following steps of: S1, scanning the paper medical record by adopting a hardware acquisition device, and acquiring image data; s2, in a data processing module, converting the image data acquired in the S1 into medical record text data; s3, in a large model structured module, taking the text data obtained in the S2 as input of a large model, and generating structured data of a single medical record; s4, in a rule engine module, performing standardization processing on the structured data of the single medical record obtained in the S3; s5, in a patient information integration module, performing information integration on multiple medical records of the same patient to form complete patient diagnosis and examination information; in the step S3, a pre-trained medical base model is firstly obtained, then medical data structured training is carried out on the medical model, and a medical record structured model is obtained.

The invention relates to a medical big model based on DPO and application thereof.The medical big model collects and processes data through a data preprocessing module, the mapping relation between input and output of the medical big model is obtained based on the data, and a preference calibration module optimizes the data preprocessing module based on DPO and adjusts model parameters of the medical big model; establishing a medical preference alignment quality evaluation strategy and a preference alignment optimization strategy through a feedback optimization module; the large medical model can be applied to a medical diagnosis question-answering system. According to the method, the problem of preference alignment of an existing medical large model is solved, the precision and efficiency of the medical large model are improved, and the steps of reward model construction and reinforcement learning in traditional RLHF are avoided by directly optimizing the language model, so that human preferences are better met.

The invention relates to the technical field of education management, in particular to a health education content management system for gynecological nursing, which is constructed by the following steps: S1, analyzing original medical content to generate a professional expression structure, and identifying a core medical entity and a logic relation chain between entities; s2, on the basis of the professional expression structure and the user cognition level label, outputting a cognition adaptation expression through a hierarchical conversion engine; and S3, binding the cognitive adaptation expression with a professional expression structure, and constructing a bidirectional version topological relation in a content storage library, so that a reversible mapping relation is formed between the professional expression and the adaptation expression. According to the method, not only is the insufficient adaptation of a general medical model in terms of gynecology ambiguity and multi-stage dependence overcome, but also the subsequent conversion process is ensured to keep consistent in terms, relationships and contexts, and an interpretable and traceable basic expression framework is provided for cognitive adaptation.