118 results about "Medical decision making" patented technology

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 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.

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 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 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 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.

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 present disclosure relates to medical and health decision making and, more particularly, to treatment based on tumor clonality estimates. Methods and systems include analyzing (230) genotypes of a tumor to identify clonality sub-types present in the tumor, using a machine learning model that is trained to learn a multilevel evolutionary process or genetic algorithm, by using a recursive Wasserstein objective to output the clonal sub-types, an ancestral structure, and a fitness model. A treatment is generated (240), tailored to the tumor using the clonal sub-types, and subclonal properties predicted by the model, such as subclone fitness.

The invention discloses a medical data fusion method, system and device and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring behavior data and physiological index data of a patient; performing time alignment processing, and respectively generating a first time sequence and a second time sequence; calculating numerical value volatility of the first time sequence and the second time sequence, and determining a corresponding credibility weight based on each numerical value volatility; performing time translation on the first time sequence according to each time offset, and calculating a correlation coefficient between the translated first time sequence and the second time sequence; determining an optimal time offset corresponding to the highest correlation coefficient, and taking the first time sequence translated according to the optimal time offset as a target time sequence; and performing weighted merging processing on the target time sequence and the second time sequence according to the corresponding credibility weight to obtain fused medical data of the patient. According to the invention, the medical decision accuracy is improved.

According to the multi-culture medical AI supervision and auditing platform provided by the invention, layered review is carried out on medical AI decisions by introducing a semantic sovereignty framework, so that the accuracy, fairness and compliance of the AI in a cross-culture situation are ensured. A multi-culture knowledge rule base is arranged in the platform, and the whole process of AI from data extraction, knowledge application to decision output is monitored. For each layer of semantics, the auditing unit automatically detects whether the AI has the problems of cultural semantic misunderstanding (such as misunderstanding of traditional terms such as qi deficiency), knowledge bias (such as neglecting local medicine by only adopting western medicine knowledge) or decision-making bias (systematic unfairness exists in suggestions for people with different cultural backgrounds) and the like. Once found, the platform generates an audit report alert and gives adjustment suggestions. Meanwhile, the platform checks ethical suitability of AI suggestions, and the situation that believes of patients and cultural custom taboo are violated is avoided; and through intention layer auditing, the AI decision is ensured to conform to value orientation and medical strategies of the same region. According to the method, the medical AI decision is more transparent and controllable, and the semantic rights and interests of each culture group in the AI era are maintained. As an innovative AI treatment tool, the platform can be used for a supervision institution and a medical institution to continuously audit various intelligent diagnosis and treatment systems so as to realize'controlled autonomous' AI: each cultural context is fully and autonomously learned, human expectation and ethical constraints are followed in output, and a solid guarantee is provided for safe and fair AI medical fusion in a global range.

Methods and systems for anomaly analysis include generating (502) an incident timeline graph for an anomaly, based on a graph of relationships sensors and temporal information relating to anomalous sensor readings. Documents are retrieved (504) relating to the anomaly using a signature based on the incident timeline graph. A prompt is generated (506) using an anomaly description based on the incident timeline graph and examples taken from the retrieved documents. A report is generated (508) describing the incident using with the prompt as an input to a large language model. An action is performed (510) responsive to the anomaly based on information in the report.

The invention provides a medical aid decision information processing method and device and a storage medium, and relates to the technical field of computers. The method comprises the steps of generating a question statement for a target medical application scene according to content information of the target medical application scene in response to a calling operation of the target medical application scene for a clinical medical decision application; according to the question statement, performing matching by adopting a preset clinical medical knowledge base to obtain target knowledge content matched with the question statement; filling a cue word template of the target medical application scene with the target knowledge content and the question statement to generate a target cue word of the target medical application scene; according to the target prompt word, calling a preset large language model to obtain reply prompt content of the target medical application scene; and displaying the reply prompt content of the target medical application scene in a display interface of the clinical medical decision application. The reply prompt content is obtained according to the clinical medical knowledge base, and the answer accuracy of the large language model in the medical field is guaranteed.

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.

The present invention relates to the technical field of artificial intelligence and medical decision-making systems, and discloses a method and system for constructing a reasoning agent for intelligent medical guidance. The method comprises: constructing a hierarchical modality completion network and a modality correlation knowledge graph; constructing a medical feature cross-modality mapping network based on contrastive learning to map medical data of different modalities to a shared feature space; constructing an uncertainty quantification model and automatically adjusting the diagnostic confidence threshold based on the uncertainty of the completed data; constructing a disease modality association matrix and a modality reliability assessment network for different disease types and symptom combinations; and realizing cross-validation and collaborative decision-making of multi-specialty knowledge through weighted voting, evidence aggregation and specialist authority assessment to form a final diagnostic recommendation. The present invention solves the problem of limited diagnostic capabilities caused by missing medical data in an environment with limited medical resources.

Methods and systems for patient stratification include learning (404) interdependent biomarkers as integrated time-series machine learning models. A disease stage is identified (424) for a patient based on collected biomarker data. A treatment for the patient is performed (430) based on the identified disease stage and a predicted future response of the patient.

Methods and systems for medical decision making include selecting a strategy from a strategy library, expressed in natural language, and selecting an improvement from an improvement library, expressed in natural language. The strategy is combined with the improvement using a large language model (LLM) to generate an improved strategy. The improved strategy is evaluated to generate feedback. The strategy library and the improvement library are updated based on the feedback. An action is performed based on the improved strategy.

Methods and systems for anomaly detection include encoding (206) a time series with a time series encoder and encoding (206) an event sequence with an event sequence encoder. A latent code (208) is generated from the outputs of the time series encoder and the event sequence encoder. The time series is reconstructed from the latent code using a time series decoder (210). The event sequence is reconstructed from the latent code using an event sequence decoder (212). An anomaly value (218) is determined based on a reconstruction loss of the reconstructed time series and a reconstruction loss of the reconstructed event sequence. An action (510) is performed in response to the anomaly value.

A system including a main system 100 having a controller 102, a system database 104, and multiple handlers 110-116 that cooperate with a medical knowledge library 120 that may be part of the system or have one or more components external to the main system 100. The handlers 110-116 are configmed to provide a communication path between the controller 102 and external systems 150-159. The medical knowledge library 120 includes a control module 122, a rules engine 124, and a serial knowledge manager 127. In at least one embodiment, the rules engine 124 is configured to analyze facts provided by a handler 118 (between the controller 102 and the control module 122) to assess and provide suggested treatments to be communicated to a user through a user interface.

The application discloses a medical data fusion method, system, device and storage medium, and relates to the technical field of data processing.In the method, the behavior data and physiological index data of a patient are acquired; time alignment processing is performed to generate a first time sequence and a second time sequence respectively; the numerical fluctuation of the first time sequence and the second time sequence is calculated respectively, and the corresponding confidence weight is determined based on the numerical fluctuation; the first time sequence is time-shifted according to each time offset respectively, and the correlation coefficient between the time-shifted first time sequence and the second time sequence is calculated; the best time offset corresponding to the highest correlation coefficient is determined, and the first time sequence shifted according to the best time offset is taken as a target time sequence; and the target time sequence and the second time sequence are weighted and merged according to the corresponding confidence weight to obtain the fusion medical data of the patient.The application improves the accuracy of medical decision.

Methods and systems for time series analysis include generating a text summary of a time series using a first large language model (LLM) agent. A prompt is generated using a multi-modal encoder with the time series and the text summary as inputs. An event prediction is generated using a second LLM agent with the text summary and the prompt as inputs. An action is performed responsive to the event prediction.

The invention relates to the field of medical decision, in particular to a path dynamic adjustment method based on medical decision. The method comprises the following steps: acquiring a training set for training a model, and inputting the acquired training set data into the model to enable the model to construct a medical decision path data set; obtaining a verification set for verifying the model, transmitting each piece of verification medical record information to the model, generating a plurality of pieces of output decision information and a plurality of pieces of output result information by the model according to each piece of verification medical record information, performing matching, and determining whether the construction of the model for the medical decision path data set is qualified or not according to the matching degree; when the output result information is unqualified, a corresponding adjustment mode is generated based on an unqualified reason, the reason comprises matching disqualification of the output result information and model disqualification, and the adjustment mode comprises adjustment of a preset matching value and adjustment of a sample size; according to the invention, the applicability of the medical decision data path is improved.

The invention provides a risk prediction system and method for lung cancer treatment immune adverse events, and belongs to the field of artificial intelligence assisted medical decision. The system comprises an information acquisition module used for acquiring patient information from a hospital information system and a patient self-reporting platform; the risk assessment module is used for predicting the occurrence risk level of the immune-related adverse events through a machine learning model based on the collected information; the report generation module is used for generating three types of personalized reports for doctors, clinical pharmacists and patients according to evaluation results; the report sending module is used for sending the report to each terminal; and the central management module is used for data verification, consistency check and real-time synchronization. According to the invention, early and accurate prediction and multi-role collaborative management of adverse risks in the immunotherapy process of the lung cancer patient are realized, and the treatment safety and medical collaborative efficiency are improved.

Methods and systems include embedding an input query, including contextual information. Performance and cost of executing the input query are predicted on each of a set of language models. The prediction is performed using a multi-armed bandit approach with each of the language models being represented by a respective arm. The input query is executed on a selected model that has a best balance of performance and cost.