36 results about "Symptom profiles" patented technology

The invention provides a common mental disease intelligent diagnosis method and system based on multiple agents, and relates to the field of medical artificial intelligence. The method comprises the following steps: S1, extracting diagnosis standards and symptom characteristics of common mental disorders, and constructing a similar patient knowledge base after verification and evaluation of the similar patient knowledge base and expert calibration; s2, acquiring clinical data from a hospital information system, and performing large medical record structuring, clinical scale simplification and scale score analysis on the clinical data to form a similar patient database; and S3, performing symptom matching and scale performance analysis on the input clinical data, constructing a multi-agent mental disease diagnosis framework, and performing multi-agent diagnosis debate based on the multi-agent mental disease diagnosis framework. The technical problems of symptom overlapping and diagnosis subjectivity among common mental diseases are solved by constructing a multi-agent cooperative diagnosis framework, introducing particle size symptom analysis and dynamically integrating structured authoritative medical diagnosis standards.

The invention provides a mood disorder assessment system based on multi-level feature fusion, and the system comprises a data collection unit which is used for collecting electroencephalogram signals of a plurality of brain regions of a to-be-assessed patient; the electroencephalogram feature extraction unit is used for extracting electroencephalogram features corresponding to the electroencephalogram signals of the brain regions; the multi-level feature extraction unit is constructed on the basis of the symptom features of the multiple testees and the corresponding electroencephalogram features, and is used for performing multi-level electroencephalogram feature latent variable extraction on the electroencephalogram features of the brain regions of the patient to be evaluated to obtain multi-level electroencephalogram feature latent variables; and the feature fusion and classification unit is used for carrying out classification prediction based on the electroencephalogram feature latent variables to obtain a mood disorder assessment result of the patient to be assessed. The method solves the problem that a mood disorder assessment system in the prior art adopts a single feature extraction and learning strategy and has no constraint of symptom information, so that the recognition capability of a model for mood disorders of different functional abnormality types is limited.

The invention relates to a diagnosis and treatment result prediction method fusing a time sequence and traditional Chinese medicine multi-stage diagnosis and treatment, belongs to the technical field of traditional Chinese medicine diagnosis and treatment prediction, and solves the problem of lack of accurate whole-process prediction in the prior art. The method comprises the following steps: acquiring the current doctor-seeing symptom and historical doctor-seeing time sequence data of a to-be-predicted patient; the historical treatment time sequence data comprises symptom, syndrome, therapy and prescription data of each time step; constructing a graph structure corresponding to each time step of the historical doctor-seeing time sequence data; extracting a symptom feature sequence, a syndrome feature sequence, a therapy feature sequence and a prescription feature sequence by adopting a trained graph neural network model based on the graph structure and the current treatment symptom; and based on the symptom feature sequence, the syndrome feature sequence, the therapy feature sequence and the prescription feature sequence, performing multi-stage diagnosis and treatment result prediction by adopting a trained recurrent neural network model to obtain the prediction results of the syndrome, the therapy and the prescription of the current doctor seeing of the to-be-predicted patient. And accurate whole-process prediction is realized.

The invention provides a special disease first-aid full-process auxiliary decision-making method based on multi-modal perception and AI, and relates to the technical field of medical first aid, and the method comprises the steps: obtaining first-aid field multi-modal data, constructing a feature dependence graph, and recognizing the type and symptom features of a special disease; indexing special disease diagnosis and treatment data based on the symptom feature vector, and determining a reference case and a treatment path; the action space is constructed in combination with clinical specifications, the expected income is predicted by applying Monte Carlo search, and the optimal treatment scheme is generated, so that the first-aid special disease recognition accuracy can be improved, the treatment decision time can be shortened, and the first-aid intervention effect can be improved.

The invention relates to the technical field of knowledge maps, in particular to a veterinary treatment big data knowledge map construction method, which comprises the following steps of: acquiring animal case symptom characteristics, physical indexes, medical history records and intervention stage data, normalizing the symptom characteristics and encoding medical history to generate a case characteristic vector set; mapping symptoms and medicine nodes to establish a semantic relationship to calculate association strength, embedding physique and medical history to update node confidence to generate a personalized knowledge graph model, dynamically correcting edge weights in combination with feedback and medicine response, and extracting an effective intervention path to construct an association index to generate a veterinary treatment big data knowledge graph. According to the method, through normalization and sequential processing of multi-source case data, feature quantification and tracking are achieved, dynamic association is established based on semantic mapping and graph attention, confidence attenuation and an attribute weighting mechanism are fused, a node relation is optimized and self-adaptive evolution is carried out, individual difference and drug response capture is enhanced, and the updating performance of diagnosis and treatment knowledge is improved; and accurate and intelligent diagnosis and treatment analysis is promoted.

The invention discloses an intelligent prescription recommendation method based on shared expression fusion, a medium and equipment, and the method comprises the steps: firstly obtaining original symptom text data of a patient, and generating a potential embedded expression through a pre-training model; global and local relation graphs are constructed through full connection, nearest neighbor and farthest neighbor composition modes, and global and local features with the same dimension are extracted through a graph convolutional neural network; and utilizing the shared encoder, the global decoder and the local decoder to reconstruct features, optimizing parameters of the shared encoder, and obtaining fusion symptom feature representation. And initializing the prescription embedding vector and extracting the characteristics of the prescription embedding vector, calculating a correlation score matrix fusing the symptom characteristics and the prescription characteristics, and outputting a preset number of prescriptions with the highest score as a recommendation result. According to the method, the complex relationship between symptoms can be effectively mined, and the accuracy and stability of prescription recommendation are improved.

Embodiments of the present application disclose an information recommendation method, device, equipment, system and storage medium. In the method, first, medical text materials of a target object are acquired. Then, symptom entities in the medical text materials are determined. Next, based on the medical text materials, symptom entity feature information and context feature information corresponding to the symptom entities are extracted. Then, the symptom entity feature information and the context feature information are fused to obtain fusion features corresponding to the symptom entities. Finally, information is recommended based on the fusion features corresponding to the symptom entities in the medical text materials. By extracting the symptom entity feature information and the context feature information corresponding to the symptom entities from the medical text materials, richer symptom-related semantics is used as the basis for the recommended information. Compared with the prior art, the technical solution of the present application enriches the basis category of the recommended information, improves the utilization rate of information in the medical text materials, and thus makes the recommended information more accurate.

The application provides a syndrome type classification method and device based on syndrome element decomposition, equipment and medium. The method analyzes the syndrome type name and syndrome type description of the to-be-classified syndrome type through a syndrome element prediction model, splits and predicts the main syndrome element and the secondary syndrome element of the to-be-classified syndrome type as the symptom characteristics of the to-be-classified syndrome type. The target feature vector of the to-be-classified syndrome type is subjected to similarity matching through the retrieval of the known feature vector of the known syndrome type, so that the known syndrome type similar to the feature of the to-be-classified syndrome type is searched in the known syndrome type library to complete the classification of the to-be-classified syndrome type. The application relates to the technical field of model prediction. The feature vector of the to-be-classified syndrome type is obtained through the syndrome element decomposition of the syndrome type, and the vector matching is performed with the known syndrome type, so that the matching between the syndrome types with different standard names and the same symptoms can be realized, the accuracy of the syndrome type matching is improved, and the classification accuracy between the syndrome types with different standards is improved.

The application discloses a kind of based on knowledge graph's simulation clinical experiment environment interaction method, specifically related to medical artificial intelligence and clinical simulation technical field;Initial disease entity node and its corresponding symptom feature set are acquired, and initial pathological state graph with time factor is constructed;Based on user interaction behavior extraction operation feature vector, utilize graph neural inference model to carry out multi-hop semantic path inference, generate post-interaction state graph;Further calculate the semantic shift degree caused by user operation, dynamically adjust graph inference weight;Extract relevant etiological entity and generate pathological evolution chain, calculate response difference degree in combination with user subsequent behavior, dynamically adjust virtual patient feedback, trigger path rollback operation if necessary;The application can realize the precise modeling and response control based on knowledge graph in clinical simulation environment, improve the interaction intelligence, explainability and stability of system, applicable to medical teaching, virtual diagnosis training and the like scene.

The application discloses a farm veterinarian question and answer and auxiliary diagnosis method and system based on a large language model, which comprises the following steps: standardizing a colloquial query to obtain structured query information; based on the information, mixed retrieval is carried out from a hierarchical veterinarian knowledge base to obtain a candidate disease list and a multi-source evidence set; a preliminary diagnosis answer is generated according to the multi-source evidence set, semantic and evidence alignment evaluation is carried out, and evidence sufficiency scores are generated; the differential diagnosis attributes of each disease in the candidate disease list are compared to identify key differences, and information missing items are identified by comparison with standard symptom profiles; based on the scores, key differences and information missing items, a multi-round diagnosis enhanced retrieval framework and user interaction are adopted, the candidate disease list and the multi-source evidence set are updated, and a diagnosis report is generated in combination with veterinary drug compliance rules. The application integrates hierarchical knowledge graphs, multi-evidence alignment and multi-round diagnosis logic, improves the accuracy of veterinarian question and answer, reduces knowledge illusion, and ensures drug compliance.

The application discloses a pediatric emergency department hierarchical treatment guidance method based on symptom analysis and an electronic device, which comprises the following steps: acquiring age information of a target patient and detecting interaction data, wherein the interaction data comprises chief complaints and accompanying symptoms; based on the age information, a hierarchical evaluation rule library matched with the age information is called, and the interaction data is calculated by level through a preset logical judgment path in the hierarchical evaluation rule library, and a target pre-examination hierarchical result is output; the target pre-examination hierarchical result and the interaction data are combined, a preset pediatric clinical diagnosis and treatment mapping rule library is called for joint matching, a target examination item corresponding to the target pre-examination hierarchical result and the symptom characteristics is screened out, and a to-be-executed examination list is generated. In this way, the pre-examination hierarchical classification can be quickly and accurately performed, and the children and their families can be guided to efficiently complete the subsequent examination and treatment.

The application provides a disease-specific emergency whole-process auxiliary decision-making method based on multi-modal perception and AI, relates to the technical field of medical emergency, and comprises the following steps: acquiring multi-modal data of an emergency scene, constructing a feature dependency graph, and identifying disease types and symptom characteristics; indexing disease diagnosis and treatment data based on a symptom characteristic vector, determining reference cases and treatment paths; combining clinical norms to construct an action space, applying Monte Carlo search to predict expected returns, and generating an optimal treatment plan, which can improve the accuracy of emergency disease identification, shorten the treatment decision-making time, and improve the emergency intervention effect.

The invention discloses an LLM triage decision-making method based on animal injury, and the method comprises the steps: obtaining the related structural input information of animal injury, including animal types, exposure levels, basic information of patients and symptom description, and standardizing the symptom features through a natural language processing technology; multi-modal fusion is carried out on the large language model and directed acyclic medical logic map embedded features constructed according to diagnosis and treatment specifications, a Prompt input sequence containing medical logic constraints is generated, and the large language model is explicitly guided to carry out reasoning according to a medical decision path; according to the method, the path validity is dynamically tracked in the reasoning process, a structured log is generated, and finally the triage level and the processing suggestion conforming to the diagnosis and treatment specification are output, so that the standardization, the consistency and the traceability of the triage decision are improved, and the medical compliance and the clinical application value of the intelligent triage system are effectively enhanced.

The application discloses a traditional Chinese medicine prescription efficacy prediction method based on a prescription-symptom heterogeneous graph, first, the characteristics of traditional Chinese medicines are counted, the traditional Chinese medicine characteristics are initialized, the traditional Chinese medicine characteristics are aggregated based on a multiple attention mechanism, the prescription characteristics are initialized, the symptom characteristics are initialized based on a known prescription-symptom correlation matrix, and the dimensionality of the symptom characteristics is reduced through an encoder, then, a prescription-symptom heterogeneous network is constructed, feature learning is performed in the heterogeneous network by using a graph convolution, and finally, the efficacy is predicted by using the finally learned prescription and symptom characteristics. The method of the application uses a multiple attention mechanism, the feature representation of the prescription is more reasonable, the dimensionality of the symptom characteristics is reduced by using an encoder, the training and learning efficiency is improved, a prescription-symptom heterogeneous network is constructed, feature learning is performed by using a graph convolution, the efficacy of the prescription can be more intuitively and efficiently predicted, and the prescription efficacy prediction precision is improved.

The present application relates to a kind of fusion time series and traditional Chinese medicine multi-stage diagnosis and treatment diagnosis and treatment result prediction method, belong to traditional Chinese medicine diagnosis and treatment prediction technical field, solve the problem of lack of accurate whole process prediction in prior art.Method includes: obtaining the symptoms and historical visit time series data of the patient to be predicted current visit;Historical visit time series data includes the symptom, syndrome, therapy and prescription data of each time step;The graph structure corresponding to each time step of historical visit time series data is constructed;Based on the graph structure and the symptoms of current visit, symptom feature sequence, syndrome feature sequence, therapy feature sequence and prescription feature sequence are extracted using trained graph neural network model;Based on symptom feature sequence, syndrome feature sequence, therapy feature sequence and prescription feature sequence, multi-stage diagnosis and treatment result prediction is carried out using trained recurrent neural network model, and syndrome, therapy and prescription prediction results of the patient to be predicted current visit are obtained.Accurate whole process prediction is realized.

The invention discloses a lung cancer immunotherapy adverse reaction patient report management system, which relates to the technical field of medical information and comprises a data acquisition module, a phase deviation calculation module, a characteristic parameter calculation module, a real-time data processing module, a characteristic decomposition module and an evaluation generation module. The method comprises the following steps: firstly, acquiring a drug administration time sequence and multi-dimensional symptom data of a patient, calculating a phase offset at a symptom acquisition moment by using a drug administration period, constructing a periodic phase coordinate system, and mapping a symptom feature vector to the coordinate system, calculating a first characteristic parameter representing drug resonance intensity and a second characteristic parameter representing a time sequence topological relation between symptoms, constructing a multi-dimensional signal decoupling model, decomposing a symptom data vector into a first component vector and a second component vector, and generating a grading evaluation result according to module values of the two components; according to the invention, precise decoupling and identification of immune adverse reactions and tumor progression are realized, and the problem of misjudgment caused by confusion of signal sources is effectively solved.

The application discloses a traditional Chinese medicine recommendation method based on knowledge driving and residual attention network, and comprises the following steps: S1, a data collection and cleaning module: obtaining prescription text data, performing a standardization process on symptoms and traditional Chinese medicine terms in the prescription text data, and dividing the data set after the standardization process into a training set, a verification set and a test set; S2, an entity pre-training module: extracting symptom semantic features by using a Word2vec training model to extract feature of symptom entity in the prescription and obtain overall context semantic representation, constructing a traditional Chinese medicine attribute knowledge graph, and integrating natural attribute features of traditional Chinese medicine into a recommendation model as external knowledge; S3, a multi-graph construction and entity feature learning module: constructing a "symptom-traditional Chinese medicine" heterogeneous graph (SHHG), a "symptom-symptom" homogeneous graph (SSIG) and a "traditional Chinese medicine-traditional Chinese medicine" homogeneous graph (HHIG) according to the co-occurrence relationship between symptoms and traditional Chinese medicine, extracting symptom features, traditional Chinese medicine features and their interaction features from the "symptom-traditional Chinese medicine" heterogeneous graph (SHHG), the "symptom-symptom" homogeneous graph (SSIG) and the "traditional Chinese medicine-traditional Chinese medicine" homogeneous graph (HHIG) respectively by using a graph attention neural network, and enhancing entity features by using a residual structure; and S4, a feature fusion and traditional Chinese medicine recommendation module: fusing the symptom features and the traditional Chinese medicine features extracted from the "symptom-traditional Chinese medicine" heterogeneous graph (SHHG), the "symptom-symptom" homogeneous graph (SSIG) and the "traditional Chinese medicine-traditional Chinese medicine" homogeneous graph (HHIG) respectively, obtaining final symptom features and traditional Chinese medicine features, expressing syndrome features by using an MLP, and finally recommending a suitable traditional Chinese medicine set according to a given symptom set.

The invention relates to the technical field of multi-modal perception, in particular to a medical scene dynamic interactive decision system and method based on multi-modal perception, and the system comprises a main node recognition module, a coverage judgment module, a reconstruction guide module, a factor construction module and a path rearrangement module. According to the method, frames with significant structures are screened through edge and texture changes between images, comparison between symptom keywords in case texts and lesion tags is combined, semantic omission is recognized, inquiry content is directionally completed, and the completed content comprises missing symptom features, uncovered part information and potential lesion description. An abnormal factor set is constructed on the basis of voice speed change, physiological waveform jump and expression tension change, factors comprise abnormal voice features, key physiological signal fragments and expression tension change directions, and semantic coverage integrity, interaction response accuracy and multi-modal fusion efficiency are improved on the basis, so that medical perception and reasoning effects are enhanced.

The invention discloses a traditional Chinese medicine syndrome differentiation language model training and reasoning method based on natural language processing, and particularly relates to the technical field of medical information processing. The method comprises the following steps: performing sentence segmentation and symptom statement segmentation on a traditional Chinese medicine medical record text of a patient to construct a symptom statement sequence data set, and analyzing a co-occurrence relation and occurrence position distribution of symptom statements in a patient narrative context to generate context association features of the symptom statements; identifying functional role differences of the same symptom in different narrative contexts according to the context association features of the symptom statements to generate symptom context role annotation data; and dynamically redistributing the participation degree of the symptom characteristics in different symptom type reasoning processes based on symptom context role labeling data, carrying out constraint updating on the traditional Chinese medicine syndrome differentiation language model, and inputting a traditional Chinese medicine medical record text of a to-be-analyzed patient into the trained traditional Chinese medicine syndrome differentiation language model in a reasoning stage. And outputting a corresponding traditional Chinese medicine syndrome type judgment result.

The invention provides an intelligent triage method and system based on face recognition, and the method comprises the steps: completing the identity verification through the comparison of face recognition and certificate information, and calling the medical history data of a patient; performing asymmetric fuzzification processing on currently acquired text chief complaint data, vital sign data and medical history data of the patient to generate a department related symptom feature set; constructing a symptom-department association rule base, and based on the symptom-department association rule base, generating department triage suggestions through a dynamic department decision model; and performing confidence coefficient optimization on the department triage suggestion, and outputting a final department triage report. According to the method, multi-symptom co-scene misjudgment and triage deviation of special crowds can be effectively reduced, a high-credibility and high-adaptability pre-examination triage solution is provided for intelligent medical treatment, and compared with traditional manual triage, the triage accuracy and emergency pre-examination efficiency can be greatly improved.

The invention is suitable for the technical field of medical intelligent diagnosis and machine learning, and provides a pneumonia screening and risk prediction model construction method based on a reverse neural network, and the method comprises the steps: firstly carrying out the preprocessing of a bacterial pneumonia clinical symptom data set, and dividing a multi-classification problem into a plurality of binary classification tasks; secondly, constructing a CPT algorithm based on a random forest and a convex hull theory, calculating a feature weight through information gain, and screening out high-contribution symptom features; and finally, inputting the screened features into the optimized BPNN, and obtaining a prediction model through repeated iterative training. The method has good classification accuracy, the number of the selected features is obviously reduced compared with a traditional model, and the method can be used as a clinical auxiliary diagnosis tool, is applied to clinical bacterial pneumonia infection screening and severe risk early warning, and provides decision support for timely treatment.

This invention discloses an electronic medical record data analysis system for children with fever, including a data acquisition module to acquire information from queried cases; and a data processing and analysis module for executing electronic medical record data analysis methods for children with fever, matching and ranking febrile diseases based on information from queried cases. The method includes: extracting candidate symptoms from electronic medical record data using an intelligent large model to generate a complete symptom dictionary; loading the complete symptom dictionary, filtering unstructured text, and generating structured symptom features for patients; calculating the mutual information value between symptoms and febrile disease classifications; calculating the point mutual information value between positive symptoms and febrile diseases, and generating an inverted index matching symptoms and febrile diseases; calculating the comprehensive distance between newly input patient electronic medical record data and candidate medical record samples based on structured and unstructured symptom features, and ranking the candidate medical record samples according to the comprehensive distance to obtain the matched febrile disease ranking.

The invention discloses a traditional Chinese medicine menstrual prescription clinical teaching evaluation method based on information entropy. The method comprises the following steps: constructing a conditional probability knowledge base; initializing a diagnosis space and a feature space, determining each diagnosis feature in the diagnosis space and each symptom problem distribution initial prior probability in the feature space based on a conditional probability knowledge base, and calculating to obtain an initial information entropy; acquiring an input symptom feature, calling a corresponding conditional probability according to a conditional probability knowledge base, executing Bayesian updating, and calculating a diagnostic posterior probability; after each Bayesian update, calculating and diagnosing spatial information entropy, and determining entropy decrement according to the initial information entropy; recording the diagnosis probability distribution and entropy decrement; and traversing the candidate symptom features which are not input, calculating expected information gains of the candidate symptom features, and outputting recommendation questions according to the expected information gains. According to the invention, objective quantitative evaluation of student thinking paths is realized, and dynamic optimization and guidance of inquiry strategies are realized through information gain calculation.

The invention discloses an electronic medical record data analysis system for a fever child patient, and the system comprises a data collection module which obtains and inquires information in a medical record; the data processing and analysis module is used for executing an electronic medical record data analysis method of the fever child patient and matching a fever disease sequence according to the information in the inquired medical record; the method comprises the steps that candidate symptoms in electronic medical record data are extracted through an intelligent large model, and a complete symptom dictionary is generated; loading the complete symptom dictionary, screening the unstructured texts, and generating structured symptom features of the patient; calculating a mutual information value between the symptom and fever disease classification; calculating a point mutual information value between the symptom and the fever disease when the symptom is positive, and generating an inverted index of the symptom matched with the fever disease; and calculating a comprehensive distance between the newly input electronic medical record data of the patient and the candidate medical record samples based on the structured symptom features and the unstructured symptom features, and sorting the candidate medical record samples according to the comprehensive distance to obtain a matched fever disease sequence.

The application provides a mood disorder evaluation model construction method and equipment based on electroencephalogram and symptom-oriented fusion. The method comprises the following steps: obtaining multiple mood disorder related symptom characteristics of multiple subjects; collecting electroencephalogram characteristics of multiple brain regions of each subject to construct a first training sample set; based on the first training sample set, a multi-level symptom-oriented feature extraction module is constructed, and multiple levels of electroencephalogram characteristic latent variables oriented by symptoms are obtained; based on the electroencephalogram characteristic latent variables of each level, the corresponding mood disorder diagnosis label is labeled, and then the feature fusion module and the classification module are iteratively trained; based on the multi-level symptom-oriented feature extraction module, the feature fusion module and the classification module, a mood disorder evaluation model is obtained. The application solves the problem that in the prior art, a single feature extraction and learning strategy is adopted, and there is no constraint of symptom information, resulting in limited recognition ability of the model for mood disorders of different functional abnormal types.

The invention provides a traditional Chinese medicine physique intelligent identification method and system, a storage medium and equipment, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: collecting traditional Chinese medicine physique mass table data of a target user, and generating an N-dimensional symptom feature vector; performing UMAP dimension reduction processing on the N-dimensional symptom feature vector by taking a historical physique data sample set as a reference, and outputting a d-dimensional embedded vector; inputting the d-dimensional embedded vector into a pre-constructed Gaussian Mixture Model (GMM) soft clustering model, predicting the attribution probability of the d-dimensional embedded vector to a plurality of constitution clusters, and obtaining a constitution probability distribution vector of the target user; and determining and outputting constitution type information of the target user based on the constitution probability distribution vector. According to the technical scheme of the invention, a constitution classification technical framework based on data driving and unsupervised learning is constructed, so that the traditional Chinese medicine constitution identification is turned to a new intelligent and objective stage from the traditional dependence on personal experience, and the reliability and practicability of the traditional Chinese medicine constitution identification are greatly improved.

This invention discloses a knowledge graph-based interactive method for simulating a clinical experimental environment, specifically relating to the fields of medical artificial intelligence and clinical simulation technology. It involves acquiring initial disease entity nodes and their corresponding symptom feature sets to construct an initial pathological state graph with a time factor; extracting operation feature vectors based on user interaction behavior, performing multi-hop semantic path reasoning using a graph neural network reasoning model, and generating a post-interaction state graph; further calculating the degree of semantic offset caused by user operations and dynamically adjusting the graph reasoning weights; extracting relevant etiological entities and generating a pathological evolution chain, calculating response differences based on subsequent user behavior, dynamically adjusting virtual patient feedback, and triggering path rollback operations when necessary. This invention enables precise modeling and response control based on knowledge graphs in clinical simulation environments, improving the system's interactive intelligence, interpretability, and stability, and is applicable to scenarios such as medical teaching and virtual diagnostic training.

The present application relates to the technical field of rheumatism and immunity disease diagnosis, and particularly relates to a rheumatism and immunity disease activity intelligent scoring method and system, biomarker detection data and clinical symptom data are acquired, sparse feature dictionaries are generated by performing multi-scale sparse coding on the biomarker data, symptom feature vectors are obtained by performing feature alignment projection on the clinical symptom data according to the dictionaries, and mutual information matrices are calculated, feature correlation tensors are constructed based on the mutual information matrices, pathological mode factors are extracted by performing tensor decomposition, fusion feature representations are obtained after fusion weight adjustment and reconstruction, and then segmented nonlinear mapping is performed to obtain activity scores, activity levels are divided according to the scores, and evaluation reports are generated. The method realizes fusion and accurate quantification of multi-modal data, and improves the accuracy and objectivity of the activity scores.

This invention belongs to the field of medical and artificial intelligence integration technology, and provides a method, electronic device, and storage medium for predicting spinal muscle and ligament injuries. The method includes: multi-source data acquisition, data preprocessing, and muscle and ligament injury prediction. The construction process of the muscle and ligament injury prediction model includes: data pre-collection, multi-source data processing model construction, image feature vector extraction, sEMG feature vector extraction, symptom feature vector generation, feature alignment processing, multi-output DNN network calculation, and model iterative training. This invention achieves multi-dimensional data coverage of structure, function, and subjective symptoms by using medical images, surface electromyography signals, and symptom data, thus compensating for the information deficiencies of single data. By adopting three-branch channel feature extraction and feature alignment processing, the invention ensures the effectiveness of feature extraction while eliminating the heterogeneity of different types of output feature distributions, thereby improving the prediction accuracy and generalization ability of the model.

The invention provides an electroencephalogram and symptom-oriented fusion mood disorder assessment model construction method and equipment. The method comprises the following steps: acquiring a plurality of mood disorder related symptom characteristics of a plurality of testees; collecting electroencephalogram characteristics of a plurality of brain regions of each testee, and constructing a first training sample set; based on the first training sample set, constructing a multi-level symptom-oriented feature extraction module, and obtaining a plurality of levels of symptom-oriented electroencephalogram feature latent variables; based on the electroencephalogram feature latent variables of each level, marking corresponding mood disorder diagnosis tags and then carrying out iterative training on the feature fusion module and the classification module; and obtaining a mood disorder evaluation model based on a multi-level symptom-oriented feature extraction module, a feature fusion module and a classification module. According to the invention, the problem that the model has limited ability to identify mood disorders of different functional abnormality types due to the adoption of a single feature extraction and learning strategy and no constraint of symptom information in the prior art is solved.