15 results about "Symptom profiles" patented technology

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 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.

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 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 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 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 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.

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.