53 results about "Diagnostic reasoning" patented technology

The invention belongs to the technical field of medical artificial intelligence, and discloses an artificial intelligence-based auxiliary diagnosis and treatment system, which comprises a multi-modal data acquisition module, a dynamic learning module, a diagnosis reasoning module, a privacy protection module, an interactive decision module and an early warning monitoring module, the output end of the multi-modal data acquisition module is connected with the input end of the privacy protection module, the output end of the privacy protection module is connected with the input end of the dynamic learning module, the output end of the dynamic learning module is connected with the input end of the diagnostic reasoning module, and the output end of the diagnostic reasoning module is connected with the input end of the interactive decision module. And the early warning monitoring module monitors abnormal data in real time and performs bidirectional interaction with the diagnosis reasoning module. According to the method, multi-source medical data are integrated, and high-precision real-time auxiliary diagnosis is realized by adopting a dynamic incremental learning and privacy encryption technology; the medical worker cooperation efficiency is improved through an interactive interface, the safety is guaranteed in combination with real-time monitoring and early warning, and the system can remarkably improve the diagnosis and treatment efficiency and accuracy.

The invention discloses a fault diagnosis method and system for a coil cutting all-in-one machine based on a large language model, and relates to the technical field of equipment fault diagnosis, and the system comprises a table data intelligent processing module, a knowledge base construction module, a diagnosis reasoning module and a user interaction module. The table data intelligent processing module is used for carrying out automatic fault mode labeling and description standardization on a cutting and rolling all-in-one machine fault diagnosis case library provided by a user; the domain knowledge graph construction module is used for integrating the standardized table database and automatically constructing a domain knowledge graph; the diagnosis reasoning module is used for performing fault diagnosis reasoning based on a large language model and a knowledge graph; and the user interaction module provides a multi-modal user interaction interface and supports visual display of fault diagnosis. Therefore, by adopting the fault diagnosis method and system for the cutting and winding all-in-one machine based on the large language model, the intelligent level of fault diagnosis can be improved, the knowledge base construction and expansion cost can be reduced, and the usability and maintainability of the system can be enhanced.

The invention relates to the technical field of medical diagnosis processing, and discloses an intelligent disease diagnosis and differential diagnosis system based on a knowledge graph, and the system comprises a data input module which is used for receiving and standardizing clinical symptoms, signs, laboratory examination data and historical medical record data of a patient; the knowledge graph construction and updating module is used for constructing and updating a knowledge graph of diseases and symptoms according to the medical literature and the clinical data, and the knowledge graph automatically extracts an incidence relation between the symptoms and the diseases from the medical literature through a natural language processing technology; and the reasoning and diagnosis module is used for performing intelligent disease diagnosis and differential diagnosis. According to the method, by optimizing reasoning path selection and information gain calculation, the path with the most information content is selected from multiple reasoning paths for diagnosis reasoning, and the most representative path is selected by calculating the correlation degree between each symptom and the disease.

The invention relates to a fault judgment system, method and device based on a knowledge graph and a medium, and belongs to the technical field of reliability analysis, and the system comprises a data collection module which is used for obtaining to-be-diagnosed information, the to-be-diagnosed information comprises a device type, a working condition and real-time monitoring data of a target device, the real-time monitoring data comprises operation parameters and state information; the fault diagnosis reasoning module is used for inputting the to-be-diagnosed information into a pre-constructed knowledge graph and performing fault diagnosis on the to-be-diagnosed information by adopting a graph reasoning algorithm to obtain a reasoning result of the target equipment, and the reasoning result comprises a fault type, a fault reason and an influence factor of the target equipment; and the diagnosis result analysis module is used for evaluating the reasoning result to obtain an evaluation result, displaying the reasoning result and the evaluation result based on a visual tool, and generating a fault diagnosis report. According to the method, the accuracy of fault judgment is effectively improved, and misjudgment caused by insufficient experience in a traditional method is reduced.

The invention discloses an electric vehicle charging compatible fault diagnosis method based on a knowledge graph, and the method comprises the following steps: constructing the knowledge graph, carrying out the fault diagnosis reasoning, and updating the knowledge graph, and the construction of the knowledge graph comprises the following steps: collecting multi-source data in the charging process of an electric vehicle, knowledge extraction is performed by applying a natural language processing technology and a data mining algorithm, knowledge from different sources is fused, a knowledge graph is constructed in a graph form, nodes represent entities, edges represent relationships between the entities, and attributes are attached to the nodes or the edges. According to the method, the knowledge graph is constructed, the electric vehicle charging multi-source data is comprehensively collected, knowledge is extracted and fused by using natural language processing and a data mining algorithm, and during fault diagnosis reasoning, input data is preprocessed based on combination of rules and machine learning, for example, abnormal value detection by using Z-score, missing value filling by using linear interpolation and the like.

The invention discloses an electric power system equipment image anomaly detection and quality diagnosis method based on a multi-modal visual language model. The method comprises the following steps: constructing a large-scale multi-modal data set comprising an electrical equipment image, an object detection annotation, a pairing question and answer knowledge base and an official supervision document, constructing a basic diagnosis model based on a visual language model, and carrying out instruction tuning; carrying out post-training on the model by adopting group relative strategy optimized reinforcement learning, and generating an interpretable step-by-step diagnostic reasoning chain; in the reasoning process, related knowledge is dynamically retrieved based on a retrieval enhancement generation technology of a graph structure, and the accuracy and compliance of a diagnosis decision are enhanced; and finally, generating a diagnosis report containing the exception type, the root cause and the decision suggestion. Compared with a traditional method, the method solves the three problems of data scarcity, opaque reasoning and knowledge isolation in the field of electric power detection, and has the remarkable advantages that the diagnosis process can be explained, complex multi-step reasoning is supported, and domain knowledge can be dynamically integrated.

The invention provides an intelligent medical multi-round dialogue diagnosis reasoning method and system based on deep learning, and relates to the technical field of deep learning, and the method comprises the steps: constructing time sequence features through employing an attention mechanism for time sequence symptom description, and carrying out bidirectional sequence modeling to generate comprehensive symptom features; using the medical knowledge graph to detect logic contradictions and information loss to generate standardized features; calculating information gain of an inquiry direction based on a deep neural network to generate optimal inquiry content; and iteratively updating according to user feedback until the diagnosis information entropy is lower than a threshold value, and outputting a diagnosis result. According to the invention, the accuracy and efficiency of medical diagnosis are improved.

The invention discloses an image text report quality control method and system based on LLM and a structured report, relates to the technical field of medical text report quality control, and solves the problems that a current text report quality control method is difficult to accurately extract key fields and is difficult to judge complex diagnostic reasoning logic. According to the technical scheme, the method is characterized in that iconography feature description related to a diagnosis conclusion is extracted from a text report, cue words are constructed according to a matched structured report template to analyze the iconography feature description into structured data, and an interface is called to fill the structured data into the structured report template. And obtaining a template diagnosis conclusion according to the built-in judgment logic of the structured report template, and comparing the template diagnosis conclusion with the current diagnosis conclusion to realize quality control auditing of the text report.

The invention relates to the technical field of medical report generation, and discloses a medical image automatic annotation and report generation system which comprises a preprocessing module, a hierarchical feature extraction module, a semantic annotation module, a guide generation module, a diagnosis reasoning module and a man-machine cooperation interface. According to the method, through standardized preprocessing, hierarchical feature extraction and automatic generation of the annotation and the report, the processing efficiency is greatly improved, and the manual load is reduced; secondly, on the basis of hierarchical feature extraction and semantic and visual embedding alignment, feature support and semantic mapping precision of annotation and reporting are enhanced, and result accuracy and cross-scene consistency are improved; meanwhile, multi-modal alignment processing and multi-dimensional information fusion enhance the adaptability of the system to different image types, generate a structured report conforming to the clinical process and improve the clinical applicability; in addition, a man-machine cooperation feedback mechanism forms a parameter optimization closed loop, so that the system dynamically adapts to new scenes and specifications, and continuous improvement of generalization ability is realized.

The invention provides a diagnosis and treatment model training method and system and electronic equipment, and relates to the technical field of medical diagnosis and treatment. The method comprises the following steps: performing field pre-training on a basic large language model by utilizing a medical corpus to obtain a pre-trained diagnosis and treatment model; corresponding diagnosis reasoning process information is generated for each piece of target medical record data in the target medical record data set, a supervision fine tuning data pair set is obtained, and each supervision fine tuning data pair in the supervision fine tuning data pair set comprises medical record information and a diagnosis conclusion containing the diagnosis reasoning process information; and performing supervision fine tuning training on the pre-trained diagnosis and treatment model by using the supervision fine tuning data pair set to obtain a trained diagnosis and treatment model. According to the method, the diagnosis and treatment model can master professional contents such as disease classification, clinical symptoms, diagnosis processes and drug treatment, and the effect of simulating a doctor to gradually analyze the symptoms to obtain a diagnosis result is achieved, so that the accuracy of the diagnosis and treatment result is improved.

The invention provides a medical diagnosis reasoning method and system based on multi-agent coevolution, and the method comprises the steps: driving a large-scale language model to decompose a medical problem through a problem decomposition agent, and determining a to-be-retrieved sub-problem through a self-adaptive retrieval agent, and selecting a proper knowledge base and a proper retrieval algorithm to obtain multi-source retrieval data, and verifying that the intelligent agent performs de-reforming synthesis on the data to obtain an integrated abstract. The expert recruitment agent builds an expert team according to the abstract, the expert agent diagnoses and reasones to build a dynamic reasoning library, the reasoning library is iteratively optimized through judgment, cross reasoning and the auto-reflection physician agent, the dynamic convergence controller monitors the reasoning library, iteration is terminated when conditions are met, and a final diagnosis decision is output. According to the method, information is directionally obtained from a plurality of authoritative and professional medical knowledge bases through problem multi-granularity retrieval and population evolutionary reasoning, and reasoning efficiency can be improved while reasoning correctness is guaranteed.

The invention discloses a children cough variant asthma diagnosis system based on a decision tree, and relates to the technical field of asthma diagnosis, the children cough variant asthma diagnosis system comprises a diagnosis management platform, the diagnosis management platform is in communication connection with a data acquisition module, a diagnosis model construction module, a diagnosis reasoning module and an intervention suggestion module, and the modules are in electric signal connection; the data acquisition module is used for collecting and preprocessing child patient clinical data including symptoms, signs, medical history and multi-dimensional examination data. By integrating the multi-dimensional examination data and the medical history, symptom change trend and family history information of the child patient, the condition of the child patient can be comprehensively and deeply analyzed, reasoning and judgment are performed by applying a branch structure of a decision tree and accurately combining various characteristics, so that subjective and empirical deviations in a traditional diagnosis method are effectively avoided, and the diagnosis accuracy is improved. The accuracy of CVA diagnosis is remarkably improved, a child patient is helped to obtain correct treatment in time, and misdiagnosis and missed diagnosis can be effectively reduced.

The invention relates to the technical field of medical data processing, in particular to a multifunctional neurology intelligent diagnosis system based on 5G and Internet of Things. The system comprises a remote neurology data transmission module used for obtaining neurology data through a 5G vehicle-mounted and indoor base station; the edge data processing module is used for performing edge fusion on the neurology data to obtain neurology fusion data, and performing feature extraction on the neurology fusion data by using a preset edge feature extraction model to obtain neurology feature data; the intelligent diagnosis reasoning module is used for performing feature reasoning on the neurology feature data by using a preset deep feature analysis model to obtain feature reasoning data; and the terminal network regulation and display module is used for carrying out broadband distribution on the feature reasoning data so as to carry out data transmission operation and data visualization operation. According to the method, efficient collaboration between the edge and the cloud can be realized, and an interpretable auxiliary service is provided.

The invention provides a dynamic knowledge graph construction and diagnosis reasoning method oriented to intelligent inquiry, and relates to the technical field of knowledge graphs, comprising the following steps: acquiring multi-source medical data, performing entity recognition and semantic annotation, establishing a causal probability graph based on a structured entity set, and establishing a dynamic knowledge graph; and selecting an optimal questioning problem according to the information gain in the inquiry process, dynamically updating the causal probability by using the Bayesian rule, and finally propagating the conditional probability along the causal path to generate a diagnosis conclusion. According to the invention, personalized inquiry decision and accurate diagnosis reasoning are realized, and the diagnosis accuracy and efficiency of the intelligent inquiry system are improved.

The invention relates to a defect diagnosis and positioning method, which comprises the following steps of: acquiring data of each layer of a software system through a data acquisition agent, matching the data of the software system by a rule matching agent according to a preset rule and a case library, identifying and determining a rule error mode, the anomaly detection agent performs recognition detection on the data based on a machine learning algorithm, recognizes and determines an abnormal fluctuation mode, finally performs diagnostic reasoning according to the diagnostic reasoning agent in combination with the recognition result of the rule error mode and the abnormal fluctuation mode, determines a defect position, and performs cooperative driving through a plurality of agents. Different abnormal modes of a software system are detected, inference diagnosis is performed by fusing identification results of the abnormal modes, and finally the defect position is positioned, so that intelligent diagnosis and accurate positioning of software test defects are realized, and the efficiency and quality of software test are improved.

The invention discloses an automatic triage and accurate diagnosis method fusing multi-modal medical data, and relates to the technical field of automatic triage, and the method comprises multi-modal medical data acquisition, preprocessing and representation vector construction, and realizes department recommendation, priority determination and disease probability analysis through a triage decision network and a diagnosis inference engine. Generating an interpretable path; according to the method, multi-modal data such as texts, numerical values, images, time sequences and voices are fused, technologies such as a cross-modal attention fusion network and a medical knowledge graph are adopted, accurate triage and diagnosis are achieved, meanwhile, a structured report is generated through a confidence degree evaluation and grading processing flow, data feedback and model updating are supported, image-text report reminding is provided for a patient, and the accuracy of triage and diagnosis is improved. The medical efficiency and the diagnosis accuracy are improved.

The invention discloses a veterinary clinical first-aid decision support method and system based on a knowledge graph, and relates to the technical field of veterinary clinical first-aid decision support. Dynamic nature and accuracy of veterinary first-aid decision support are remarkably improved by constructing a static and dynamic combined knowledge graph and a two-way parallel reasoning mechanism; individual dynamic evolution of the knowledge graph is achieved, the actual effect feedback of field measures is fused into the graph through a state change weight algorithm, knowledge representation can adapt to the current case in real time, and the adaptability of the model is enhanced. In combination with forward diagnosis reasoning and backward traceability reasoning, treatment suggestions are deduced from symptoms, effectiveness of implemented measures can be evaluated, diagnosis hypotheses can be reversely corrected, a decision closed loop is formed, and reasoning reliability is improved; through conflict resolution and fusion of decision paths, a structured dynamic report is generated, the misdiagnosis risk is effectively reduced, and the disposal process is optimized.

The invention discloses a self-adaptive diagnosis model training method and system for OCT images. The system comprises an image data preprocessing module, a feature self-adaptive extraction module, a diagnosis model self-adaptive training module, a model performance dynamic evaluation module, a diagnosis reasoning and feedback module and a model iterative optimization module. According to the method, accurate adaptation of OCT images with different qualities and different equipment sources is realized through a hierarchical preprocessing and dynamic feature fusion mechanism, and heterogeneity interference of the images is effectively eliminated; based on hierarchical training of lesion complexity and dynamic loss function design, the model can focus on samples difficult to recognize and minority classes of lesions, and the diagnosis balance is improved.

The invention discloses a diagnostic reasoning RAG system for resisting retrieval noise based on a self-generated knowledge base, and the system comprises a knowledge retrieval module which carries out the retrieval from an external knowledge base according to a user question to obtain a plurality of target text blocks; the evaluation generation module is used for independently evaluating each target text block through an evaluation model so as to identify retrieval noise, generating a correlation judgment result and a noise degree judgment result of each target text block, and generating internal memory information as a self-generated knowledge base based on a user question; and the diagnosis reasoning module is used for integrating the user question, each target text block, the corresponding correlation judgment result, the noise degree judgment result and the internal memory information into enhanced prompt information and inputting the enhanced prompt information into a large language model to generate a diagnosis reasoning answer. According to the method, retrieval noise interference can be effectively resisted, model knowledge support is strengthened by means of the self-generated knowledge base, the precision and timeliness of medical diagnosis reasoning are improved, and the requirements of the medical field for high-precision and strong-timeliness clinical decision making are met.

The invention discloses a medical question and answer method and system based on a knowledge graph and an intelligent agent, and relates to the technical field of natural language processing. The method comprises the following steps: firstly, identifying a core entity related to patient description information from a medical knowledge graph, and obtaining an optimized entity set; performing association reasoning path and neighbor path exploration on entities in the optimized entity set to obtain an association reasoning path set and a second-order neighbor reasoning path set; then, paths in the associated reasoning path set and the second-order neighbor reasoning path set are converted into natural language description information based on prompt words of natural language conversion, and the natural language description information forms a knowledge text set; and finally, performing diagnosis reasoning on the patient description information and the information in the knowledge text set according to reasoning prompts, and outputting a diagnosis result. The method has remarkable advantages in the aspects of fact accuracy, disease diagnosis accuracy and drug recommendation accuracy.

The invention discloses a novel equivalence device structure closing and single-step repairing method and system in medical diagnosis, drug discovery and physiological status modeling, belongs to the field of medical diagnosis analysis, biological information processing and model verification, and ensures the reliability and traceability of the process through structural equivalence device detection and receipt track recording. The method monitors sequential equivalents (ABBA rectangles for checking consistency of operational orders) and conservation equivalents (triangles for checking information conservation and energy balance) during operation to find anomalies in diagnostic inference chains, drug action pathways, or physiological closed loops (such as predictor deviations, energy conservation imbalances, or model inconsistencies). When a non-equivalence condition is detected, the system selects a unique repair adapter from a geometric layer, a frame layer and a field layer for correction according to a preset priority, and the deviation metric mu is strictly reduced by one (delta mu = 1) during each repair, and is gradually converged to an equivalence closed state.

The invention discloses a path modeling and model optimization method and system for clinical diagnostic reasoning, and the method comprises the steps: obtaining real clinical medical record data, and constructing a diagnostic reasoning data set after preprocessing; performing diagnostic reasoning on samples in the data set by using a pre-trained large language model to obtain an absolute correct reasoning path and a simulated correct reasoning path so as to construct a correct reasoning path set; performing supervision and fine tuning on the large language model based on the correct reasoning path set; reasoning the data set again by using the fine-tuned model, and collecting samples with diagnosis errors and error reasoning paths corresponding to the samples; and constructing a preference sample pair set according to the wrong reasoning path set and the correct reasoning path set, and performing preference optimization training on the fine-tuned model based on the preference sample pair set to obtain an optimized large language model. According to the method, explicit modeling and large language model optimization are performed on the diagnosis reasoning path, so that the stability and interpretability of the model diagnosis reasoning process are improved.

The invention belongs to the technical field of engineering construction, and discloses an engineering machinery diagnosis method and system based on a maintenance project, and the method comprises the steps: obtaining the state data, operation data and fault codes of engineering machinery; constructing a dynamic triggering condition of the maintenance project, and judging whether to trigger maintenance reminding or not based on the state data, the operation data and the fault code; and constructing an expert knowledge base comprising a fault tree, a case base and a rule base, obtaining a fault phenomenon, calling the expert knowledge base, combining the state data, the operation data and the fault code, executing diagnosis reasoning combining forward reasoning and backward reasoning, and obtaining a diagnosis result. According to the invention, on the basis of the real-time state of the equipment, dynamic prediction and maintenance demand reminding, when the fault occurs, the expert knowledge base is utilized to quickly position the fault reason and provide maintenance guidance, so that the maintenance cost is reduced and the attendance rate of the equipment is improved.

This invention relates to the field of medical informatics, providing a medical-assisted reasoning method and system based on a small language model and Monte Carlo tree search. The method includes: standardizing raw patient medical data to obtain an initial training dataset; expanding candidate diagnostic reasoning paths within a Monte Carlo tree search framework based on a small language model to obtain multiple candidate reasoning paths; inputting these multiple candidate reasoning paths into a process reward model, scoring the intermediate reasoning steps of each path to obtain step scores; ranking and filtering the candidate reasoning paths based on the step scores to obtain a set of high-quality reasoning paths; jointly training the policy model and the process reward model to obtain an optimized reasoning model; and generating reasoning results from the optimized reasoning model based on the optimal reasoning path to obtain a comprehensive suggestion set. This invention improves the interpretability of medical-assisted reasoning and enhances the robustness of the diagnostic system.

The invention discloses a traditional Chinese medicine disease diagnosis auxiliary system based on a clinical mapping knowledge domain, and relates to the technical field of traditional Chinese medicine diagnosis. Searching syndrome nodes associated with the symptoms and tongue picture features from a traditional Chinese medicine clinical knowledge map to form an initial syndrome set; calculating a matching degree between the pulse condition waveform data and typical pulse condition features of each syndrome node, and performing sorting and screening to form a focused syndrome set; extracting associated treatment methods and prescriptions to generate a preliminary diagnosis reasoning chain; and performing logic verification and optimization on the reasoning chain through a graph neural network syndrome evolution deduction model, outputting an optimized diagnosis path, and recording related data to a diagnosis and treatment file. The system can assist in traditional Chinese medicine diagnosis, improve diagnosis pertinence and reasoning preciseness, and perfect diagnosis and treatment records.

The invention provides a medical industry intelligent diagnosis system based on a large model, which belongs to the field of intelligent medical application and comprises a data acquisition and preprocessing module, a feature extraction and selection module, a model training and optimization module and a diagnosis reasoning and explaining module. And the diagnosis accuracy and reliability are obviously improved. According to the method, the accuracy and efficiency of diagnosis are improved, personalized medical treatment and rare disease diagnosis are supported, medical resource configuration is optimized, the satisfaction degree of patients is improved, and important data support is provided for medical research and innovation.

The invention discloses a human-machine collaborative inquiry visual interaction system and method based on a medical large model. The system comprises a real-time knowledge base module, a data fusion module, a large model reasoning module and a visual interaction module. The real-time knowledge base module is used for acquiring medical literature data in real time and dynamically updating a basic medical knowledge graph; the data fusion module is used for acquiring multi-source medical data of a target patient and constructing a personalized knowledge graph; the large model reasoning module is used for receiving the personalized knowledge graph to perform preliminary diagnosis reasoning, responding to an intervention instruction to perform adjustment and then performing reasoning again to generate a final diagnosis conclusion; the visual interaction module is used for visually displaying the diagnosis information and receiving an intervention operation instruction input by a user; according to the method, the personalized knowledge graph is constructed, the reasoning logic chain is visually displayed, and real-time intervention of doctors is supported, so that transparent man-machine collaborative diagnosis is realized, and the interpretability of diagnostic reasoning and the accuracy of clinical decision are remarkably improved.

The application discloses a diagnostic mechanism construction method suitable for diversified collection of multi-source heterogeneous parameters, and specifically implements the following steps: step 1, state parameter collection and analysis coding in a sampling system; step 2, parameter fusion processing after coding; step 3, diagnostic knowledge acquisition and representation model; step 4, diagnostic reasoning; and step 5, diagnostic result representation. The application solves the problems in the prior art, such as poor adaptability of a sampling monitoring system, incapability of reflecting the hierarchy and correlation of equipment fault states, and incapability of realizing knowledge correlation between multiple subsystems.

The application discloses a kind of based on dynamic retrieval and chain reasoning's auxiliary diagnosis method, device and medium of dermatosis.It is described as follows:The method includes: obtaining skin disease image and user description text;First visual language model is input to target sample, and medical entity feature is extracted and primary diagnosis hypothesis is generated;According to the clinical diagnosis sequence of mechanism inference from feature perception, generate reasoning node sequence;Based on current node and reasoning context, construct retrieval query, retrieve reference information in skin disease knowledge base and integrate with reasoning node, form image-knowledge-reasoning chain triple knowledge structure;The integration result of the reasoning node sequence and the triple knowledge structure is input into second language model, and auxiliary diagnosis report containing diagnosis reasoning process and diagnosis conclusion is generated and output.No skin disease image-diagnosis report pairing training data can improve the explainability and reliability of zero sample diagnosis.The application also provides the corresponding diagnostic device and computer readable medium.