176 results about "Medical decision making" patented technology

The invention provides a medical decision-oriented multi-modal data dynamic fusion and labeling method and system, and relates to the technical field of medical decision-making, and the method comprises the steps: collecting medical images, electronic medical records and physiological monitoring data of a patient, extracting multi-modal features such as a focus form, a disease type, a symptom, a treatment record and a physiological index, and carrying out the fusion and labeling of the multi-modal features; and constructing a dynamic fusion labeling model. According to the model, multi-modal features are coded, similarity is calculated, a feature association graph is constructed, feature aggregation is carried out, labeling is carried out in combination with a medical knowledge base, and a dynamic labeling vector is generated. And inputting the dynamic labeling vector into a medical decision support model, and based on similar case analysis and deep reinforcement learning, generating a diagnosis and treatment suggestion containing a treatment scheme and early warning information. By dynamically fusing multi-modal data and an online updating mechanism, the accuracy and efficiency of medical decision making can be improved, and personalized diagnosis and treatment are realized.

The invention discloses a medical adverse event risk prediction, prevention and control method based on causal inference. The method comprises the following steps: S1, collecting and preprocessing multi-modal medical data; s2, constructing a causal network through a time slicing technology; s3, introducing ethical factors to form an ethical causal network; s4, generating a causal enhancement vector; s5, constructing a dynamic risk prediction model, and outputting an evaluation result; s6, analyzing a path contribution degree, and screening high-risk paths and nodes; s7, generating a personalized prevention and control scheme; and S8, generating a transparent report, and displaying the transparent report to the medical personnel and the patient. According to the method, the dynamic causal relationship among the medical variables is accurately modeled through the dynamic causal network, the multi-modal data fusion and the ethical correction technology, the personalized prevention and control scheme is generated in combination with the optimization algorithm, comprehensive support is provided for medical decision making, and implementation of precise medical treatment is promoted.

The invention relates to the technical field of medical decision, and discloses a medical decision support system based on a knowledge graph, and the system comprises a knowledge graph construction module which constructs an initial knowledge graph based on a medical ontology library, and the knowledge graph comprises entities and association relationships of diseases, symptoms and drugs; the data acquisition module is used for acquiring data from an electronic medical record, wearable equipment, a medical literature library and a hospital information system and normalizing the data through a standardized protocol; the dynamic knowledge updating module is used for processing normalized data through an incremental graph neural network; a multi-source knowledge fusion module; a context awareness module; a dynamic deduction module; and a decision optimization closed loop module. And triggering a preset clinical rule in real time based on the pathological state of the patient, dynamically adjusting the intensity value of the related edge in the factor graph, and persistently storing the intensity value back to the knowledge graph, so that logic adaptation and individualized experience precipitation of general medical knowledge in a special pathological state are realized, and the individualized treatment accuracy is ensured.

The invention provides a medical decision support method and system based on big data. The method comprises the following steps: integrating and fusing patient information from different medical institutions, personal health monitoring equipment and a public health database to obtain a dynamic update library; a graph neural network algorithm is applied to analyze the relation between the patients and the relation between the patients and the environmental factors in the dynamic update library so as to predict the individualized disease development trend, and dynamic risk early warning signals are generated; based on the dynamic risk early warning signal, through feature importance analysis and a local interpretable model, revealing key factors and an interaction mechanism influencing disease progression, and applying a health economics evaluation method to generate a medical suggestion report; and in combination with the medical suggestion report, establishing a dynamic learning and feedback mechanism, optimizing personalized suggestions in the medical suggestion report, and constructing a medical decision support system. According to the technical scheme provided by the invention, the credibility of medical decision making is remarkably improved.

The invention relates to the technical field of artificial intelligence and medical decision systems, and discloses a reasoning agent construction method and system for intelligent medical treatment guidance, and the method comprises the steps: constructing a hierarchical modal completion network and a modal correlation knowledge graph; constructing a medical feature cross-modal mapping network based on comparative learning, and mapping different modal medical data to a shared feature space; according to the uncertainty of the complemented data, constructing an uncertainty quantitative model and automatically adjusting a diagnosis confidence threshold; aiming at different disease types and symptom combinations, constructing a disease modal incidence matrix and a modal reliability evaluation network; through weighted voting, evidence convergence and specialist authority evaluation, cross validation and collaborative decision-making of multiple specialist knowledge are realized, and a final diagnosis suggestion is formed; according to the invention, the problem of limited diagnosis capability caused by lack of medical data in a medical resource limited environment is solved.

The invention relates to a medical decision-making method and device based on a neural symbol hybrid model, and a storage medium, and relates to the technical field of medical information processing. The method comprises the following steps: firstly, fusing multi-modal clinical data of a target object through an attention weighting mechanism to obtain multi-modal fusion representation; then, on the basis of a medical mask enhancement mechanism of a high-risk index, associating the high-risk index of the structure perception type cross-modal attention network constructed on the basis of multi-modal fusion representation with a medical index mask to obtain a joint feature vector of the target object; and inputting the joint feature vector into a neural network decision layer to obtain a neural network recommendation vector, and inputting the joint feature vector into a medical knowledge graph to obtain a symbol rule recommendation vector. And finally, inputting the neural network recommendation vector, the symbol rule recommendation vector and the joint feature vector into a three-layer neural symbol fusion network to obtain a target decision suggestion. Therefore, the accuracy, interpretability and clinical suitability of medical intelligent decision making are improved.

The invention belongs to the technical field of data processing, and discloses a construction method and system based on a medical examination medical knowledge base and a medium. Comprising the steps that a basic medical data set is collected, data cleaning is conducted, and a structured medical data set is output; constructing a multistage medical dictionary and establishing semantic mapping with the structured medical data set to generate a standard medical data set; carrying out medical related entity extraction on the standard medical data set and establishing a semantic relationship of each medical entity to obtain a medical entity set; performing semantic alignment on the medical entity set to obtain a semantic alignment entity set; constructing a medical examination knowledge rule base and constructing an initial medical examination knowledge graph in combination with the semantic alignment entity set; performing entity association extension on the initial medical examination knowledge graph, and outputting an enhanced medical examination knowledge graph; real-time clinical data are collected, and the medical examination knowledge graph is updated and enhanced based on the real-time clinical data to obtain the medical examination knowledge base, so that convenience is provided for medical decision making and intelligent reasoning diagnosis.

The invention provides a medical agent middleware-based full-specialized collaborative diagnosis and treatment method and system, and relates to the technical field of medical agents, and the method comprises the steps: building a basic medical knowledge graph through reinforcement learning, converting expert diagnosis and treatment experience into diagnosis and treatment probability distribution through a personalized treatment decision model, and forming an enhanced medical knowledge graph; and calculating a matching probability with a disease based on the symptom feature vector of the patient to generate a preliminary diagnosis decision, and selecting and adjusting a treatment scheme through reinforcement learning. According to the invention, general and specialized collaborative diagnosis and treatment can be realized, and medical decision accuracy and diagnosis and treatment efficiency are improved.

Disclosed herein are deep-learning based systems, methods, and instrumentalities for medical decision-making. A system as described herein may implement an artificial neural network (ANN) that may include multiple encoder neural networks and a decoder neural network. The multiple encoder neural networks may be configured to receive multiple types of patient data (e.g., text and image based patient data) and generate respective encoded representations of the patient data. The decoder neural network (e.g., a transformer decoder) may be configured to receive the encoded representations and generate a medical decision, a medical summary, or a medical questionnaire based on the encoded representations. In examples, the decoder neural network may be configured to implement a large language model (LLM) that may be pre-trained for performing the aforementioned tasks.

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 discloses an artificial intelligence auxiliary medical decision-making system, and relates to the technical field of medical assistance. Through the detection suggestion module, preliminary reasoning and observation index judgment can be performed according to patient chief complaint in combination with basic information, and detection suggestions are given in combination with detection cost budget of a patient; through the diagnosis module, preliminary judgment can be made according to detection abnormity, disease classification is judged based on the current diagnosis result, further detection suggestions are given, and finally a doctor makes further diagnosis or treatment suggestions; through the treatment suggestion module, disease causes can be judged according to the diagnosis result, and detection cost budget of a patient is combined, so that the diagnosis and treatment efficiency is improved. Giving out treatment suggestions; the detection suggestion module, the diagnosis module and the treatment suggestion module jointly act to form a medical decision-making system, non-common diseases can be found easily, the problem of cross-department missed diagnosis is solved, the medical budget planning function is achieved, and doctors and patients can provide accurate treatment suggestions easily.

The invention provides a medical strategy generation method and system combined with data flywheel reinforcement learning in the field of artificial intelligence and clinical medical cross technology. The method comprises the following steps: step S1, creating a multimode data annotation model, and setting an annotation loss function and an annotation reward function; s2, acquiring a large amount of historical medical data to construct a data set; s3, carrying out training and compression on the multi-mode data labeling model; s4, creating a medical decision model, and setting a decision loss function and a decision reward function; s5, training and compressing the medical decision model; step S6, carrying out deployment on the trained model; step S7, labeling the real-time medical data through the multi-mode data labeling model, and performing incremental training on the medical decision model through the labeled real-time medical data; and S8, performing medical question and answer through the deployed medical decision model. The method has the advantages that the accuracy, timeliness and safety of medical decision making are greatly improved.

The invention belongs to the technical field of decision-making assistance, and provides a medical decision-making assistance method and system based on a large reasoning model. The method comprises the following steps: recording voice interaction content between a doctor and a patient, and extracting first key medical information from the voice interaction content; extracting second key medical information from an electronic medical record written by a doctor, and carrying out consistency analysis and conflict analysis on the first key medical information and the second key medical information by using the large reasoning model; and outputting corresponding abnormity prompt information to a doctor according to an analysis result. The method can achieve the automatic verification of the medical information, guarantees that the electronic medical record is consistent with the oral medical advice, reduces the drug use and re-visit risks of a patient, and improves the accuracy and safety of a medical decision.

The embodiment of the invention relates to the technical field of time sequence knowledge graph reasoning and medical treatment, and provides a chronic gastritis evolution trend prediction method and device, and the method comprises the steps: obtaining the current illness state data of a chronic gastritis disease prediction target patient; inputting the data into a pre-trained chronic gastritis evolution trend prediction model; deducing the data through a historical interaction mode to obtain first probability distribution of the predicted entity in a time sequence knowledge graph historical vocabulary; inference is carried out through the generation mode, and second probability distribution of the prediction entity in the clinical diagnosis and treatment related entity set is obtained; and fusing the first probability distribution and the second probability distribution to obtain a disease entity with the highest combination probability, and taking the disease entity as a future illness state prediction result of the target patient. Therefore, dynamic evolution trend prediction is carried out on the development of the current illness state of the target patient, the disease state of the target patient can be dynamically evaluated, and a doctor is assisted to make a medical decision.

The invention provides a method and a system for optimizing a medical decision based on artificial intelligence. The method comprises the following steps: acquiring multi-modal medical data; the multi-modal medical data comprises structured data, unstructured data and time series data; determining an initial causal tensor based on a causal relationship among the structured data, the unstructured data and the time sequence data; the initial causal tensor is a third-order tensor, and the dimensions of the third-order tensor are respectively expressed as a data modal quantity, a variable quantity and a time step length; performing low-rank tensor decomposition on the initial causal tensor to obtain a key causal mode, and updating the initial causal tensor based on the condition dynamic change of the patient in combination with the key causal mode to obtain a current causal tensor; and performing decision optimization on the current causal tensor based on reinforcement learning to obtain an optimized medical strategy. The problem that it is difficult to comprehensively and accurately make medical decisions is solved, and the accuracy of medical strategies is improved.

The invention relates to the technical field of medical decision assistance, and particularly discloses a doctor-patient co-decision multi-issue negotiation method and system and a readable storage medium, and the method comprises the following steps: firstly collecting preference information and behavior characteristics of a doctor and a patient for a plurality of negotiation issues; constructing a fuzzy constraint and satisfaction function based on preference information and behavior characteristics; constructing a proxy behavior model based on the fuzzy constraint and the satisfaction function, and forming a distributed negotiation problem; and dynamically optimizing the negotiation strategy through a value function by adopting an ADP (adaptive dynamic programming) algorithm until the negotiation is consistent to generate a common negotiation result or the negotiation fails and terminates the negotiation, and sending a recommendation scheme to the doctor agent and the patient agent. According to the doctor-patient co-decision multi-issue negotiation method and system and the readable storage medium, the negotiation success rate and efficiency are improved, the medical specialty and patient individuation requirements are balanced, the doctor-patient satisfaction is remarkably improved, and the doctor-patient co-decision multi-issue negotiation method and system are suitable for a multi-issue medical decision scene.

The invention discloses a medical decision assistance method and system based on medical rules, and relates to the technical field of medical decision assistance, and the method comprises the following steps: receiving medical data of a patient from various medical devices and information systems; standardizing the received data, and mapping the standardized data to a corresponding medical concept after the data is standardized; and in the data mapping process, acquiring and recording associated information of the data. According to the invention, through standardized processing and machine learning model analysis, the problem of misassociation during association of medical data from different sources is effectively solved, and the accuracy of diagnosis and treatment is improved. And misdiagnosis and excessive treatment are prevented by evaluating the data association quality and screening out misassociated data. And abnormal data identification and elimination are optimized by using a clustering algorithm, so that diagnosis is ensured to be based on reliable data, unnecessary medical intervention caused by data errors is reduced, and the overall nursing quality and the medical service level are improved.

An interactive platform, system and method for assisting with medical decision-making is presented herein. The system and method includes receiving diagnostic data, which is defined to include, for each of a plurality of diagnostic tests for a disease, a test sensitivity, a test specificity, and a test outcome. Using a diagnostic module, a posterior probability of the disease is determined based at least upon a prior probability of the disease and the diagnostic data. The system and method further includes receiving treatment data, which includes a patient preference for a cure, a patient aversion for a cure without harm, and a patient aversion for a cure with harm. Using the treatment data, a net benefit output is generated for each of a plurality of treatment options, the net benefit being used to provide assistance with the medical decision-making. For more accurate and applicable data, a synthetic dataset may be generated.

The invention discloses an emergency auxiliary decision-making system based on a large model agent and multiple knowledge bases, and the system comprises an input end which is used for obtaining patient information from multiple dimensions; the central processing module is used for intelligent agents of different scenes to construct based on different large models and cue word projects, and calling at an output server of the system through an embedded structure; the knowledge base comprises a knowledge graph base and a retrieval enhancement generation document base, a knowledge graph stock structured rule ensures reliable decision, and a retrieval enhancement generation document stock unstructured text realizes dynamic knowledge fusion; and the service node stores knowledge according to a fault-tolerant demand menu library or double libraries and outputs the knowledge to the server side. The server side comprises an intelligent triage auxiliary module and an intelligent diagnosis auxiliary module, and provides assistance in a mode of calling an intelligent agent service. The method has the advantages that the triage efficiency is improved, the triage decision-making time of nurses is shortened, the medical history combing and examination scheme making time of doctors is shortened, the reliability of medical decision-making is ensured, and the maintenance cost is lower.

The invention discloses an intelligent inquiry dialogue method and device, computer equipment and a storage medium. The method comprises the steps of obtaining an inquiry dialogue of a target user through an intelligent inquiry dialogue interface; extracting disease feature information from the inquiry dialogue; querying a medical decision database based on the disease feature information to obtain a suspected disease set; calculating a suspicion degree corresponding to a suspected disease in the suspected disease set; screening a target suspected disease from the suspected disease set based on the suspicion; according to the disease feature information, determining whether the target suspected disease has an unconfirmed disease factor; if yes, generating a question generation task based on the unconfirmed disease factors, executing the question generation task through a large language model to obtain questions for the unconfirmed disease factors, and outputting the questions through an intelligent inquiry dialogue interface. According to the method, a transparent and controllable inquiry process is provided, and the risk of diagnosis errors is reduced.

The invention provides an innovative personalized medical decision support system, and aims to generate highly-customized medical suggestions by comprehensively analyzing real-time physiological data and historical medical records of a patient in combination with latest clinical guidelines and expert experience. The system adopts an advanced data acquisition technology to monitor patient conditions in real time, uses a data warehouse technology to integrate historical medical data, and deeply mines data values through machine learning and deep learning algorithms. Besides, the system has adaptive learning ability, and can continuously optimize the decision model according to new medical data and user feedback, so as to realize more accurate and timely medical decision support. The design of the system not only improves the quality and efficiency of medical services, but also provides more personalized medical experience for patients, and is expected to bring revolutionary change in the medical field.

The invention relates to a model training method and device, computer equipment, a storage medium and a program product. The method comprises the steps of obtaining at least one symbol set from a medical special symbol library, and generating a corresponding training sample set according to each symbol set; for each symbol set, testing the response of the medical decision model to the symbol set through the training sample set corresponding to the symbol set to obtain response data corresponding to the symbol set; determining error distribution of error response of the medical decision model to the symbol set according to the response data corresponding to the symbol set, and calculating a confusion entropy value corresponding to the symbol set according to the error distribution; grading the symbol sensitivity of the medical decision model to at least one symbol set according to the confusion entropy value corresponding to each symbol set, and determining a symbol set scheduling strategy according to a sensitivity grading result; and performing reinforcement learning training on the medical decision model according to the symbol set scheduling strategy. By adopting the method, the performance of the medical decision model can be improved.

The invention relates to the field of artificial intelligence medical treatment, in particular to a medical AI decision interpretability enhancement method and system, and the method comprises the steps: obtaining the case data of a patient; predicting an explanatory diagnosis result which comprises a diagnosis result and an explanatory diagnosis feature; generating an interpretive diagnosis result confrontation sample and a background sample; screening data related to the explanatory diagnosis features; respectively training a medical AI decision interpretability enhancement model and an AI decision model related to patient diseases; a diagnosis result is predicted through a medical AI decision interpretability enhancement model, and the method further comprises the steps of constructing an uncertainty weight set, carrying out weight removal operation on interpretation features and model behavior data, obtaining interpretability medical image data, generating anti-fact interpretation and carrying out visualization processing. The transparency of AI auxiliary diagnosis is improved by innovatively introducing a double-sample cooperation mechanism and combining the technologies of interpretive diagnosis result generation, hierarchical screening multi-stage training sample set construction, uncertainty weight reconstruction model and the like.

The invention relates to the technical field of medical data processing, in particular to an AI-based anorectal operation auxiliary system, which comprises a patient health data analysis module, a patient health data analysis module, a data processing module, a data processing module, a data processing module, a data processing module, a data processing module and a data processing module, wherein the patient health data analysis module is used for classifying personal information of a patient and health data associated with anorectal diseases on the basis of basic physiological parameters of the patient, past anorectal disease history, medicine allergy history and operation records; and performing correlation analysis on the anorectal disease history and the physiological parameters of the patient to generate preoperative health feature information. According to the method, the physiological parameters of the patient and the past medical history are subjected to correlation analysis, key health indexes related to the operation can be accurately extracted, potential relations in the data can be mined, and the application effect of the data is optimized. By analyzing the combination mode of different health data, the probability of occurrence of common risks in the operation can be predicted, and the accuracy of medical decision making is enhanced. In addition, key operation steps of the surgery are matched with the health state of the patient, potential operation difficulties are recognized, and the scheme is optimized in a targeted mode.

The invention discloses a clinical decision-oriented medical data analysis system, and relates to the technical field of medical data analysis, the clinical decision-oriented medical data analysis system comprises a patient end, a medical diagnosis end and a medical decision-making system, the medical decision-making system comprises a data acquisition unit, a data analysis unit, a decision-making output unit and a decision-making optimization unit; according to the method, the medical data is acquired, the disease course prediction model is established according to the historical diagnosis and treatment data of the patient, the disease course prediction result is obtained in combination with the medical data, and the medical seeing ability evaluation coefficient of the patient is obtained through comprehensive calculation based on the personal economic data of the patient and the social income data of the patient. The method comprises the following steps: judging the doctor-seeing ability of a patient according to a preset doctor-seeing ability evaluation interval, outputting a target decision scheme which can be loaded by the patient according to the doctor-seeing ability level of the patient, and when the scheme adaptation degree reaches a preset scheme evaluation threshold value, marking the target decision scheme as an optimizable scheme. And controlling and outputting a target optimization scheme by simplifying the examination process and the drug dosage in the treatment process.

The invention discloses a drug use decision method and system based on deep learning, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly obtaining the basic information of a target symptom, and inputting a large language model to obtain the symptom and the description information of a to-be-matched drug; and then determining a target drug matched with the target symptom through a preset first expert model. And then integrating the target drug related information and converting the information into a standard format to obtain standardized drug recommendation information. And finally, the information is pushed to a preset medication decision interface through a preset interface, so that intelligent, efficient and accurate medication decision assistance is realized. The main purpose of the invention is to provide an intelligent medication decision-making auxiliary method, which matches symptoms and drug information through a large language model, accurately recommends a target drug in combination with an expert model, standardizes recommendation information and pushes the standardized recommendation information to a medication decision-making interface so as to improve medical decision-making efficiency and accuracy.

Methods and systems for image segmentation include initializing a student model and a teacher model using a labeled dataset. An initial mask is generated for an unlabeled image using the teacher model. The initial mask is refined to generate a refined mask using a pretrained foundation model. The student model is tuned using the unlabeled image and the refined mask as a pseudo-ground truth label. The teacher model is updated using the tuned student model.

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 provides a decision supervision support method based on evidence-based medicine knowledge management, and relates to the technical field of evidence-based medicine, and the method comprises the following steps: obtaining an evidence set for describing an entity relationship between exposure factors and / or intervention measures and an outcome index, using the evidence set to construct a first entity relationship graph as quantitative representation of the entity relationship; when the new evidence appears, updating the edge weight according to the quality score of the new evidence by adopting an incremental learning algorithm, and generating a second entity relation graph; the hospital system uses the second entity relation graph to carry out diagnosis and treatment to obtain real-time clinical feedback information; and adjusting the edge weight of the second entity relation graph according to real-time clinical feedback information of the multi-party hospital system to obtain a comprehensive decision support quantitative relation. According to the method, the problem that the medical decision supervision support is lagged due to the fact that the relation between the exposure factor and / or the intervention measure and the outcome index is difficult to dynamically update according to the retrieved new evidence in the prior art is solved.

The invention belongs to the technical field of medical treatment, and discloses an auxiliary decision-making method for accelerating rehabilitation surgery clinical path multi-decision multi-objective optimization. The method comprises the following steps: constructing a data set of preoperative states and medical decisions of a patient; unsupervised clustering is adopted, the clustering method is beneficial to dividing the patients into different phenotypes and risk levels according to unique features of the patients, and the obtained clusters provide valuable insights for heterogeneity of patient groups. And then, according to the decision data of each clustering phenotype, constructing a Bayesian network model, and adding some regulations in the construction process to trim the network, so that the constructed Bayesian network model better accords with clinic. And finally, inferring the state posterior probability of each node by using the Bayesian network, and inferring a decision scheme of each risk group by using the support degree so as to accelerate the postoperative rehabilitation process of the patient, reduce the hospitalization time of the patient and reduce the hospitalization cost.