5 results about "Symptom profiles" patented technology

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 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.

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.