97 results about "Medical model" patented technology

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 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 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 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.

The application discloses an electronic medical record and a cataloging and classifying method, system and device thereof. The method comprises the following steps: obtaining standardized data by preprocessing multi-format medical record original data through a medical OCR model, a VAE anomaly detection algorithm, a medical knowledge graph word segmentation tool and a Transformer term standardization model; checking semantic rationality, extracting multi-dimensional features, and obtaining a comprehensive feature vector after strengthening and fusing; constructing a transfer learning classification model and training the model, inputting the feature vector to generate cataloging information; and finally, evaluating the classification result through an active learning mechanism, combining artificial labeling data and a causal forest dynamic updating framework, and incrementally learning and optimizing the model performance. The application solves the problems of low multi-format data extraction accuracy, insufficient term standardization and poor model generalization in the prior art.

The present application relates to the technical field of computers, and provides a three-dimensional model usage method and apparatus based on a medical model content library, an electronic device, and a storage medium. The method comprises: when performing medical simulation training, invoking a medical virtual content library, wherein the medical virtual content library is obtained by storing three-dimensional data files; and obtaining demand data, searching the medical virtual content library for a corresponding target three-dimensional data file on the basis of the demand data, and invoking a corresponding target three-dimensional model on the basis of a target three-dimensional medical file. The present application solves the problem in the prior art of how to quickly provide medical three-dimensional scenes and matched modes under various scenes.

The invention discloses a multi-dimensional quality cross validation evaluation method and system based on medical model training, and belongs to the technical field of medical file evaluation. Constructing a pathological feature sample library and a medical record archive library based on the pathological feature description words and the medical record archives, and classifying and recording the medical record archives through the pathological feature description words; according to the incidence process of the pathological characteristics, forming an incidence state transition relation chain and configuring incidence state verification nodes; in combination with a clinical pathology state time range of the pathology state verification node, screening associated cross-pathology features to form a cross-pathology label; and updating according to a verification node sequence to obtain a disease occurrence state cross relation chain, performing multi-dimensional quality evaluation on the medical record file, and outputting an evaluation value. According to the method, the accuracy and reliability of medical file quality evaluation are improved through multi-dimensional cross validation, high-quality medical record data support is provided for medical model training, and the method is suitable for medical record quality auditing, medical data treatment and medical model training data screening scenes of medical institutions at all levels.

The invention provides a method and system for improving reliability of medical decision and application. The method comprises the following steps: constructing a data set covering any disease according to medical data; training the medical vertical large model according to the data set to obtain a medical decision set; performing priority ranking on any decision in the medical decision set by constructing a multi-dimensional evaluation system to obtain a primary medical decision ranking result; and dynamically adjusting a weight parameter and a decision rule of any medical decision in the primary medical decision sorting result by taking the feedback suggestions and the actual diagnosis and treatment effects as reward signals to obtain a final medical decision sorting. According to the method, the problems of data fragmentation and illusion faced by the current large medical model and the industrial pain points of insufficient application suitability are effectively solved, and the clinical diagnosis and treatment efficiency and quality are remarkably improved.

This invention discloses a secure aggregation method and system for multi-party medical model parameters based on blockchain. Combining homomorphic pseudo-random number generator technology and one-time pad encryption technology, this invention designs a blockchain-based method for covertly distributing pseudo-random number generator seeds. This achieves privacy protection for local model parameters in a single round, random replacement of local model masks in multiple rounds, and efficient aggregation of global model parameters. It prevents the risk of privacy leaks due to unauthorized entities stealing medical model parameters. Furthermore, the invention designs a blockchain-based hierarchical summary record for model parameters, significantly reducing the communication and computational overhead of model training nodes and the recovery of erroneous model parameters while ensuring the consistency of the medical model.

This invention discloses a method and system for developing a medical dataset based on a large-scale model. The method includes: acquiring multiple medical data sets to be processed; performing privacy data identification and encryption on each medical data set based on a de-identification algorithm to obtain multiple de-identified medical data sets; labeling each de-identified medical data set with medical information according to multiple preset student models to obtain corresponding labeled data; sampling and verifying all labeled data according to the teacher model corresponding to the student model, and organizing the labeled data into a medical dataset based on the verification results; the medical dataset is a pre-training dataset, instruction fine-tuning dataset, and / or test dataset for large-scale model training. Therefore, this invention can effectively ensure privacy and security during large-scale medical information processing, providing high-quality and standardized data asset support for the robust training of subsequent large-scale medical models.

The invention discloses a medical consultation reply generation method, device and equipment based on multi-modal fusion, and belongs to the field of text generation, and the method comprises the steps: selecting a plurality of first agent experts and a plurality of second agent experts according to multi-modal data input by a user; extracting medical data features and numeric symbol features from the multi-modal data through a first agent expert, and performing joint coding on the medical data features and the numeric symbol features to obtain a multi-modal feature vector; inputting the output of the first agent expert and the multi-modal feature vector to a corresponding second agent expert, and calling the second agent expert according to a preset calling sequence; the second agent experts are connected according to a preset calling sequence; and if the consistency verification of the medical knowledge output by each agent expert and the mathematical logic passes, aggregating the output of each agent expert, and generating a medical consultation reply. The problem of low quality of medical consultation reply generated by a large medical model can be solved.

The invention discloses a curative effect prediction method based on multi-organ metastasis genome data, and belongs to the technical field of medical models, and the method specifically comprises the following steps: collecting clinical pathological characteristics, multi-organ metastasis genome data and a treatment scheme of a breast cancer patient, and recording a metastasis part and a load state; dimensionality reduction is conducted on high-dimensional genome data through a regularization algorithm, feature importance is evaluated in combination with a nonlinear model, and clinical, treatment and genome features related to treatment response are screened out; inputting the screened features into a machine learning and deep learning framework, randomly dividing a training set and a test set in a layered manner, optimizing hyper-parameters through cross validation, and constructing a classic machine learning set model and a deep learning model based on an attention mechanism; disturbing test queue treatment scheme data, evaluating the consistency of model recommendation and an actual scheme, and verifying the prediction capability and clinical practicability of the model; according to the method, multi-dimensional data are integrated, and the curative effect prediction accuracy of the metastatic breast cancer is improved.

This application provides a method, apparatus, device, and storage medium for self-correction of a medical visual language model based on a dynamic experience base (DEKB), relating to the field of medical visual language processing technology. The method includes: when the initial diagnostic result of the medical visual language model for a current clinical case does not match the fact label, constructing the current case as a structured experience unit and storing it in a dynamic experience knowledge base; upon receiving a query, retrieving historical experience cases related to the new query from the dynamic experience knowledge base; using the historical experience cases as contextual prompts to guide the medical visual language model in chain-like thinking, generating a corrected reasoning result. By constructing an endogenous dynamic experience knowledge base, designing a deep attribution analysis mechanism, and employing a dual-threshold retrieval algorithm, this application enables DEKB to transform the model's historical errors into structured knowledge that can be used for future reference, significantly improving the robustness and generalization ability of medical model image diagnosis reasoning.

The invention discloses an intelligent monitoring and early warning system for medical adverse events, and relates to the technical field of medical treatment, and the system comprises an ETL module which is used for extracting medical text data from a hospital clinical information system; the medical adverse event prediction module is used for predicting the medical text data and obtaining the medical adverse event category and grade corresponding to the medical text data; the medical adverse event prediction module comprises a medical adverse event model obtained through training; the medical adverse event model is obtained by training a medical model according to historical medical adverse event samples, and the medical adverse event model can output corresponding medical adverse event types and levels according to input medical text data; according to the medical adverse event reporting method and device, the medical adverse event reporting efficiency can be improved.

The invention discloses a clinical prediction model construction method and system for treating oligometastatic non-small cell lung cancer through radioactive particle implantation, and relates to the technical field of medical model construction, and the method comprises the steps: obtaining patient data, taking survival time as a main endpoint event, employing a Cox regression model to carry out single-factor analysis on factors affecting patient prognosis, and obtaining a single-factor analysis result; determining the relationship between each clinical feature and survival time, and incorporating the important factors influencing the prognosis into multi-factor Cox regression analysis to determine independent risk factors influencing the survival prognosis of the patient; generating a first clinical prediction model according to each independent risk factor nano-construction; cD3 + AC, CD4 + AC, CD8 + AC, combined treatment, complete treatment and the number of metastatic lesions are taken as variables together to be included in construction of a clinical prediction model, and a second clinical prediction model is manufactured and generated. Independent risk factors influencing the prognosis of a radioactive particle implantation treatment few-metastatic NSCLC patient are confirmed, the clinical prediction model is established, the treatment prognosis can be accurately predicted, and the treatment cost is reduced. And screening of dominant benefit people is facilitated.

The invention provides an artificial intelligence diagnosis and treatment analog simulation system and method, and the system comprises a knowledge graph construction module which is used for constructing the knowledge association relation among the medical information, the disease type and the treatment scheme of a patient, generating a medical knowledge graph, and carrying out the iteration; the medical model construction module is used for constructing a large medical model for disease recognition and treatment scheme generation; and the diagnosis and treatment aid decision-making module is used for performing personalized analysis on the medical information of the patient in combination with the knowledge graph construction module and the medical model construction module to generate diagnosis aid information and a treatment aid decision-making scheme. Therefore, by constructing the medical knowledge spectrogram and fusing the large-scale pre-training model ability, the medical information of the patient is automatically analyzed, the auxiliary information of the personalized diagnosis and treatment scheme is generated, and the auxiliary decision making function of the AI doctor role is achieved.

The invention relates to a method for constructing a chronic kidney disease risk prediction model after acute kidney injury, and belongs to the field of medical models. The method comprises the following steps: constructing a clinical complete prediction model by using an XGBoost algorithm, obtaining SHAP values of different factors, carrying out feature importance sorting by using the SHAP values, and identifying key prediction factors through sorting; performing a forward variable selection strategy, and expanding the number of candidate variables to a preset value by adopting a step-by-step forward selection method based on an SHAP value sorting result; cross validation and model optimization are carried out, the optimization objective is to maximize AUC of a training set, and generalization performance of different factor combinations is evaluated; constructing a simplified model, selecting a core prediction factor of which the prediction effect AUC drop range is smaller than a set value, and constructing a core prediction factor model; and carrying out simplified model verification. According to the method, a model can be constructed to accurately identify high-risk people suffering from chronic kidney diseases after acute kidney injury, a reliable basis is provided for clinical intervention, and the clinical application value of the model is finally improved.

Embodiments of the present disclosure disclose a medical model reply content evidence tracing method, device, medium and product. The method comprises: matching the question data with the knowledge text in the knowledge base to obtain a first confidence degree, which is the standard similarity between the question data and the knowledge text; based on the first confidence degree and the first best parameter, filtering out the actual candidate text from the knowledge base, combining the medical model to obtain the actual reply content; after the actual reply content is semantically segmented into multiple actual fine-grained segments, matching the actual fine-grained segments with the knowledge text to obtain a second confidence degree, which is the standard similarity between the actual fine-grained segments and the knowledge text; based on the second confidence degree and the second best parameter, filtering out the tracing evidence of the actual fine-grained segments from the knowledge base, and then the actual reply content tracing result can be displayed. Through RAG retrieval and tracing retrieval, the original source of the reply content can be checked reversely, high-precision tracing is realized, and the explainability is enhanced.

A knowledge-guided fine-tuning and optimization method for a large medical model, and a related apparatus. A method for enhancing the explainability of a large medical model on the basis of "multi-modal data plus expert knowledge" is constructed, with a focus on improving the diagnostic accuracy and explainability of models in the field of diseases. The method comprises: acquiring multi-modal data to be subjected to diagnosis; and inputting said multi-modal data into a knowledge-guided model to obtain a thought-guiding information part in a logic-chain prompt, so as to input the thought-guiding information part into a large medical model to obtain an output that has been normalized by means of chain-of-thought prompting.

The invention discloses a large medical model evaluation method executed by a computer, which comprises the following steps: receiving information of a large medical model input by a user, performing compliance evaluation, confirming filing information of the large medical model, if the information passes the filing information, performing basic model deployment and evaluation, otherwise, reminding the user to input information again, and if the evaluation of the basic model deployment passes the evaluation, performing evaluation on the filing information of the large medical model. If yes, basic model discrimination is carried out to determine the security of the source of the medical large model, otherwise, basic model deployment is carried out again, if basic model discrimination is passed, security evaluation is carried out, otherwise, a user is reminded to input information again, after security evaluation is completed, medical large model application services are deployed and evaluated, and if security evaluation is passed, the user is reminded to input information again. If yes, timeliness evaluation, performance evaluation and page safety evaluation are carried out, if not, medical large model application services are redeployed, and finally a comprehensive evaluation report is formed. The evaluation method runs through the whole life cycle of the large medical model, and is simple to operate and relatively high in efficiency.

The invention relates to the technical field of data processing, provides a method for designing a health scheme based on a medical large model, and aims to solve the problems of unstable evaluation accuracy and low intervention scheme generation efficiency caused by dependence on manpower in a traditional ICF health evaluation method. The method comprises the following steps: constructing an ICF vector space and an ICF causal map; a large language model which is realized on the basis of a Transform Decoder-Only framework is used as a medical large model; constructing an ICF evaluation instruction, a causal reasoning instruction and a health scheme generation instruction; constructing a first data set based on the ICF evaluation instruction and the ICF vector space, constructing a second data set based on the causal reasoning instruction and the ICF causal atlas, and constructing a third data set based on the health scheme generation instruction; training a medical large model based on the first data set, then training the medical large model based on the second data set, and then training the medical large model based on the third data set; and after training, health management scheme prediction service is provided for the user based on the large medical model.

The invention provides an intelligent optimization method and device based on a generative medical large model. The method comprises the following steps: acquiring an annotated medical image and annotation information; acquiring early warning information of the marked medical image; inputting the labeled medical image into the large generative medical model to obtain an optimized labeling result; the generative medical large model is obtained by training based on pre-training data containing error correction data. According to the method and the device, the marked medical image and the marking information output by the deep convolution model are optimized through the generative medical large model, so that the modified and optimized marking result is obtained.

System 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

The invention discloses an image segmentation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a to-be-processed original 3D image which comprises a plurality of 2D slice images; first prompt information is received, second prompt information is generated based on the first prompt information, and the first prompt information indicates a target object in the 2D slice image; and based on the second prompt information, performing segmentation processing on the original 3D image through the 3D medical basic large model to obtain a 3D segmentation result of the target object. According to the image segmentation method, a complete 3D segmentation result can be obtained only by giving a small amount of first prompt information on the 2D slice image, a user does not need to prompt each layer of image, the workload is reduced, a medical basic large model is adopted, the applicability is wider, and end-to-end training does not need to be carried out.

This invention provides a medical auxiliary diagnostic system based on a large medical model and speech recognition, including a doctor's workstation deployed on hospital terminal equipment for receiving and displaying diagnostic reports and draft medical records for doctors to review and confirm before submitting to the hospital information system (HIS); an AI doctor client installed on the same terminal device as the doctor's workstation for real-time acquisition of doctor-patient dialogue audio, displaying system-generated follow-up questions and auxiliary prompts, displaying draft medical records and diagnostic reports, and communicating with the AI ​​server and the hospital data interaction server; and a hospital data interaction server, deployed independently in each hospital, for establishing a WebSocket communication channel to achieve real-time data transmission between the AI ​​doctor client and the doctor's workstation within the hospital, forwarding diagnostic reports, draft medical records, and doctor instructions. This system reduces the workload of doctors in data collection, recording, and analysis, and overcomes the shortcomings of existing technologies that rely entirely on manual labor.

The invention provides a medical care and health care data processing method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining multi-source heterogeneous data of old people from a medical institution, a rehabilitation institution, an old-age care institution and a medical care and health care operation platform with data sharing authority; processing the multi-source heterogeneous data of the old people to generate standard fusion data of the old people; according to the standard fusion data of the elderly, the index identifier is generated, and the corresponding source information and the index identifier are sent to the block chain to be stored for use of the medical model, so that full-scene coverage of the input data of the high medical model is realized, and the accuracy of the medical model is improved.

This invention provides a digital fusion management method and system based on a large medical model, relating to the intersection of medical information technology and artificial intelligence. The digital fusion management method specifically includes: acquiring and generating multiple sets of prior information, including: prior information generated based on clinically relevant text, second and third prior information generated based on preliminary diagnostic information, and fourth prior information generated based on historical case knowledge base retrieval; processing examination videos based on the first set of prior information to locate one or more candidate video segments, and verifying the candidate video segments based on the second set of prior information to obtain a final set of retrieved segments; and generating a first conclusion for answering clinical questions and a second conclusion for reviewing preliminary diagnostic information based on the final set of retrieved segments and the multiple sets of prior information.

An example operation(s) includes defining a display position of an image plane proximate to a current position of an ultrasound probe instrument. An Augmented Reality (AR) situated view is rendered on the image plane, the situated view portrays ultrasound imagery captured by the ultrasound probe instrument. An AR display orientation of the image plane is determined based on one or more detected movements of the AR headset device. One or more portions of the ultrasound imagery are registered as being representative of respective portions of a three-dimensional (3D) medical model.

The application relates to a navigation method, system, computer device, storage medium and computer program product. The method comprises the following steps: obtaining a target medical model, the target medical model being reconstructed based on an initial image scanned by a scanning device; generating operation planning information based on the target medical model; obtaining a real-time image scanned by the scanning device, and registering the real-time image with the target medical model to obtain a first registration relationship; obtaining a first coordinate relationship between a coordinate system corresponding to an execution tool and an image coordinate system of the real-time image; and navigating the execution tool based on the first coordinate relationship, the first registration relationship and the operation planning information. The method can model during operation, does not require preoperative CT, and realizes real-time registration without the need of adding additional markers.