23 results about "Disease description" patented technology

The invention provides a bridge disease named entity standardization processing method and device based on a large language model and a storage medium. The method comprises the steps that a bridge disease structured knowledge base is established; potential bridge disease names, corresponding disease positions and disease description information are extracted from the target bridge regular inspection report; traversing the standard disease name set for the potential bridge disease names to obtain a first standardized processing result; calculating the character string similarity between the potential bridge disease name and each standard disease name in a standard disease name set; extracting potential bridge disease names of which the character string similarity is greater than a set threshold value and corresponding standard disease names; aiming at the potential bridge disease names in the to-be-normalized relation group, constructing prompt words of a normalized task; and inputting a cue word into the large language model to obtain a second normalized processing result. By adopting the processing method provided by the invention, the bridge disease named entity can be quickly normalized without a special training model.

A consultation method and apparatus, and an electronic device and a computer-readable storage medium. The method comprises: acquiring target disease description information which is input by a user; and feeding back to the user a target diagnosis result about the target disease description information, wherein the determination of the target diagnosis result is related to the target disease description information and assistance diagnosis information, and the assistance diagnosis information comprises target profile information of a target consultation object that is indicated by the target disease description information. By means of the method, a high-quality consultation service can be provided for users.

The invention discloses a medical data storage method based on semantic recognition, and belongs to the technical field of data processing, and the method comprises the steps: receiving medical data, carrying out the parallel processing of an unstructured medical text in the medical data through a large language model, and generating a structured semantic triple and a semantic vector representing the overall semantics; constructing a semantic graph index and a vector index to form a mixed index structure; the method comprises the following steps: receiving a natural language query, analyzing the query into a structured query condition and a query vector, forming an executable multi-modal query instruction, executing atlas query and a vector similarity search process in parallel, and mapping the structured query condition to a semantic atlas execution path for matching. In the implementation process of the technical scheme, natural language processing and a large language model are introduced, entity recognition, relation extraction and semantic role labeling are conducted on medical unstructured texts, and structured conversion of key information such as illness state description, diagnosis conclusions and treatment schemes is achieved.

System and methods for computer modeling in medicine. A sort of period table of medical models is described for personalized diagnostics, prognostics and therapeutics, including at least 80 major categories of medical models. Generative artificial intelligence and geometric deep learning techniques, and algorithms including 2D and 3D graph machine learning and GenAI algorithms, are described, tailored and applied to diagnostic disease description, prognostic prediction and therapeutic development and management, including generation of novel synthetic drugs. The AI and machine learning techniques and algorithms are applied to understand each individual's genetic, RNA and protein anomalies that represent the source of many unique patient diseases. AI-enabled software agents assist physicians and researchers in building patient medical models. Several personalized medicine applications of individualized medical modeling include cardiovascular

The invention relates to the technical field of insurance and intelligent data processing, and discloses an internet health insurance underwriting method and device based on disease characteristics, and the method comprises the following steps: S1, obtaining to-be-underwritten historical insurance data and current insurance application data; s2, constructing a health insurance special word vector library based on the historical insurance data; s3, inputting the feature vector into a pre-trained hierarchical disease recognition model, and obtaining an output disease severity score; s4, generating an underwriting risk prompt according to the disease severity score, and sending the underwriting risk prompt to an underwriting operation terminal; and S5, collecting an artificial underwriting decision result for the underwriting risk prompt, and taking the artificial underwriting decision result as a new training sample to carry out incremental updating on the hierarchical disease recognition model. According to the method, the non-standard disease description analysis precision is improved by fusing the claim co-occurrence word vector and the vertical domain attention mechanism.

The application provides a disease probability prediction method and device and electronic equipment, at least one target keyword associated with a target disease and disease description information of a target patient are obtained, for each target keyword, the target keyword is marked according to whether the target keyword exists in the disease description information, and a first marking result is obtained, and the disease description information and the first marking result corresponding to each target keyword are input into a pre-trained BRF model to output the probability that the target patient has the target disease, in this way, at least one target keyword associated with the target disease is determined in advance, after each target keyword is marked based on the disease description information of the target patient, the first marking result corresponding to each target keyword and the disease description information are jointly used as the input of the BRF model, which can improve the comprehensiveness and accuracy of evaluating whether the target patient has the target disease.

This invention discloses a method and system for predicting the results of traditional Chinese medicine (TCM) consultations in urology using a large language model, belonging to the technical field of large language modeling. This invention preprocesses disease description data and obtains self-correlation parameters between the disease description data; then, based on the large language model, it constructs a prediction model; outputting core keyword data describing lesions enables the prediction of specific causes in consultations, improving accuracy; by recording feedback data related to the core keyword data in real time; inputting the feedback data into a self-correcting model and outputting adjustment values; and adjusting the parameters within the large language model in real time based on the adjustment values, through continuous use and adjustment based on feedback data, the prediction model for a specific knowledge domain in urology is further refined, thereby further improving the accuracy of predicting specific causes in consultations.

The application discloses an intelligent inquiry method and system based on a knowledge graph, equipment and a storage medium, and belongs to the technical field of intelligent medical treatment. The method can be applied to an intelligent inquiry scene and comprises the following steps: acquiring a disease description text; performing structural processing on the disease description text, performing semantic retrieval on the structural description data, and performing graph matching processing; fusing and enhancing the retrieved knowledge set and the graph matching data, and performing inquiry based on the fused and enhanced data. In the application, the knowledge graph is utilized, candidate knowledge sets are extracted from the structural description data and the knowledge graph, and the candidate knowledge sets and the knowledge graph are fused and enhanced to ensure that reasoning is based on real medical knowledge, the illusion rate of generated content is reduced, and the reasoning accuracy of existing medical intelligent reasoning is improved.

The application discloses a doctor-patient joint decision-making method based on LLM-Agent multidisciplinary dynamic cooperation and relates to the technical field of doctor-patient joint decision-making. The method comprises the following steps: acquiring a disease description, extracting symptoms and signs by an LLM, assigning a department according to the symptoms and signs by the LLM, acquiring a multidisciplinary team composed of doctor agents of the corresponding department, independently analyzing the disease description by each doctor agent, generating a scheme, then integrating the schemes, and determining whether to adopt the scheme as a treatment scheme by a voting review mechanism. Patient preferences are extracted from the disease description based on a predefined preference format and a pre-constructed lightweight LLM. Then, a patient model is constructed based on the patient preferences and the disease description. Potential conflicts between the treatment scheme and the patient preferences are identified by an LLM. The patient agent quantitatively evaluates whether the treatment scheme is satisfactory according to the potential conflicts. If not, the next iteration is entered, otherwise the negotiation is stopped. The treatment scheme when the negotiation is stopped is output.

The invention discloses a doctor-patient joint decision-making method based on LLM-Agent multidisciplinary dynamic cooperation, and relates to the technical field of doctor-patient joint decision-making. The method comprises the following steps: acquiring disease description, and extracting symptoms and signs from LLM; and according to the symptoms and the signs, performing department distribution by the LLM, and obtaining a multidisciplinary team composed of the doctor agents of the corresponding departments. Each doctor agent independently analyzes the disease description and generates a scheme, then integrates the schemes, and determines whether to adopt the scheme as a treatment scheme or not by a voting review mechanism. Patient preferences are extracted from the condition description based on a predefined preference format and a pre-constructed lightweight LLM. And then constructing a patient model by the patient preference and the condition description. Potential conflicts between treatment regimens and patient preferences are identified by LLM. And the patient intelligent agent quantitatively evaluates whether the treatment scheme is satisfied according to the potential conflict. And if not, entering the next iteration, otherwise, stopping negotiation. And outputting the treatment scheme when the negotiation is stopped.

The present application provides a kind of based on multi-view fusion graph convolution network parasitic disease drug association prediction method, to solve the problems such as data sparse, noise interference and incomplete feature information in drug development. The method is through "multi-view heterogeneous network construction-self-supervised learning-multilayer propagation and association prediction" process, accurately identify parasitic disease-drug association. First, collect data and construct benchmark dataset;Then, extract drug SMILES feature and disease MeSH descriptor, fuse drug similarity network, disease similarity network and drug-disease two-part network. Next, adopt self-supervised learning strategy to obtain node embedding representation, introduce neighbor information aggregation layer to enhance sparse relationship expression, and finally output association prediction score by weighted fusion multi-view features through attention mechanism. Compared with existing methods, the present application shows higher accuracy and stability in parasitic disease drug prediction task, and provides support for drug screening and new drug development.

This invention discloses an intelligent recommendation method and system for clinical pathways in cerebral hemorrhage based on knowledge graphs. It constructs a pathway knowledge graph and a disease severity classification system by analyzing treatment guidelines, identifying key nodes in pathway execution and disease severity level switching boundaries, and configuring differentiated decision rules for single and mixed disease severity areas. It collects multi-source disease data from patients in a dimensional manner, extracts disease features, and monitors dynamic indicator trends. Through weighted feature extraction and dynamic early warning analysis, it generates disease descriptions and calculates the matching degree between the current disease severity and candidate pathways. When a deviation in pathway execution is detected, it locates the source node of the deviation through cross-stage transitivity analysis and reverse tracing, generating targeted pathway correction instructions. Through node integrity verification and mandatory constraint correction, it ensures the standardization of the adjustment plan, outputting clinical pathway recommendation results adapted to the patient's actual condition, thus improving the adaptability and accuracy of clinical pathway recommendations.

The invention belongs to the technical field of intelligent medical treatment, and provides a digital intelligent medical service providing method and system. The method comprises the following steps: receiving basic medical information input by a patient, and carrying out matching analysis on the basic medical information and an illness state description guiding knowledge base to generate a personalized symptom guiding framework; performing semantic analysis and integrity verification on symptom description information fed back by the patient through the personalized symptom guide framework, and identifying missing core symptom dimension information; generating a supplementary guide instruction for the missing core symptom dimension information, and further obtaining complete symptom description data; and performing standardization processing on the complete symptom description data based on the symptom description specification term library to generate a structured illness state abstract, and pushing the structured illness state abstract to a doctor side. The method can effectively solve the problems of non-professional expression of the patient and information missing, shortens the inquiry time, reduces the information transmission error, assists the doctor to quickly capture the key illness state, improves the doctor-patient communication efficiency and diagnosis accuracy, and optimizes the medical experience.

The invention discloses a multi-modal small sample plant disease identification method, which comprises the following steps: acquiring a plant disease image through collection, and carrying out data preprocessing on the image; a VSF model is provided and comprises a semantic upgrading module, and plant disease definitions are upgraded by using a large language model and prompt words to generate more real disease descriptions conforming to an agricultural scene; a two-stage modal fusion module is designed, so that the consistency and discrimination capability of cross-modal information are effectively improved; inputting the preprocessed image data into the model for training and verification to obtain an optimal model; and finally, inputting a to-be-classified image into the optimal model to obtain a classification result. The multi-modal small sample plant disease identification method provided by the invention can ensure that the plant diseases can be rapidly identified and correct types can be obtained, and has a good application prospect.

The invention discloses a training method and device of a medical aid decision-making model. The method comprises the following steps: for each case text in a training sample set, calculating the similarity between the case text and all multi-granularity disease description texts by an initial auxiliary decision model, and screening K target disease description texts with the highest similarity score from N multi-granularity disease description texts according to the similarity; taking K target disease description texts corresponding to the case text as negative samples and taking actual disease description texts corresponding to the case text as positive samples by the initial aid decision model; and performing parameter adjustment on the initial auxiliary decision-making model according to the case text, the positive sample corresponding to the case text and the K negative samples to obtain the medical auxiliary decision-making model. According to the scheme, by introducing a comparative learning mechanism based on strong negative sample mining, the recognition capability of the model on easily-confused disease descriptions is effectively enhanced, the accuracy of decision suggestions output by the model is improved, and reliable support is provided for high-precision diagnosis and treatment recommendation.

The application provides a method, device, equipment and readable storage medium for extracting a clinical finding event, comprising: acquiring a disease description text, adding a string to the front of the disease description text to generate a first text; inputting the first text into a trained deep learning model for prediction and acquiring a prediction result; restoring the prediction result into a plurality of triplets, wherein each triplet comprises the start and end positions of a subject word in the disease description text and the start and end positions of a value corresponding to each category in the disease description text; and generating a clinical finding event composed of a plurality of four-tuple data according to the plurality of triplets. The problems that a corresponding case cannot be extracted according to the description of a patient or the extracted case is inaccurate are solved.

The invention discloses a crop disease diagnosis method and system based on multi-mode automatic prompt optimization. Acquiring crop disease images of each disease category, sequentially performing semantic correlation screening and visual diversity screening, and constructing a few-sample example set; the few-sample example set and the initial disease description text are combined to construct an initial multi-modal test prompt; optimizing the disease description text according to the initial multi-modal test prompt and the data set to obtain the optimized disease description text of each disease category; and updating the multi-modal test prompt, inputting the to-be-diagnosed crop disease image and the optimized multi-modal test prompt into the large language model, and processing to obtain the crop disease category of the to-be-diagnosed crop disease image. According to the method, model training is not needed, the diagnosis accuracy is remarkably improved, dependence on data and experts is reduced, excellent cross-crop, cross-dataset and cross-model generalization ability is shown, and an efficient and extensible solution is provided for a resource limited scene.

The invention discloses a construction method of an intelligent hospital guide model and an intelligent hospital guide method and device. According to the scheme, a sample training set comprising a plurality of training samples and a plurality of disease description texts corresponding to the training samples is obtained; according to each training sample and the plurality of disease description texts, calculating a measurement index value corresponding to each disease description text in the training sample, and setting a corresponding sample label for the disease description text based on the measurement index value; performing iterative training on the initial text filter according to the data query text and the multiple disease description texts corresponding to the training samples and the sample labels corresponding to the disease description texts to obtain a target text filter; and constructing an intelligent hospital guide model based on the target text filter and the large language model. By optimizing the information screening mechanism, the problem that the output hospital guide suggestion has deviation due to coarse-grained information screening of the existing intelligent hospital guide model is solved, and the medical assistance value of the intelligent hospital guide model is improved.

The present application relates to the field of medical health, and particularly relates to a method and system for determining remote intelligent risk of ankylosing spondylitis; in the scheme of the present application, the types of risk determination specifically include symptom information acquisition, posture information acquisition and thoracic expansion range determination; in the process of symptom information acquisition, pain site information and its property description are acquired, and a disease site cognition model and a disease description information analysis model are established to perform calculation and processing, so that the pain area can be accurately positioned and the pain property can be more accurately identified; after the disease identification information is converted into analyzable data, the data is processed through an XGBoost algorithm, and an accurate risk assessment result can be obtained; the remote intelligent risk determination scheme of the present application provides a comprehensive, accurate and easy-to-use tool for screening and monitoring of ankylosing spondylitis, and can significantly improve the efficiency and accuracy of diagnosis.

The invention discloses an adaptive path selection-based circRNA (Ribonucleic Acid) and disease association relationship prediction method, which comprises the following steps of: downloading a database for acquiring association relationships among circRNA, diseases and miRNA (Micro Ribonucleic Acid), and constructing an association matrix; downloading disease description information, calculating and obtaining a disease semantic similarity matrix, and averaging to obtain a disease fusion similarity; constructing a meta-path by using the association relationship and the disease fusion similarity; constructing an initialization graph according to the meta-path, and reconstructing a meta-path graph through a self-adaptive path selection method; the reconstructed meta-path diagram is sent to a noise diagram contrast learning algorithm for feature extraction and fusion; according to the method, multi-dimensional graph comparison features are fused through a multi-head attention mechanism, an MLP is used for classification, three losses in training are fused for iterative optimization until algorithm convergence, the MLP is used for final classification, and the incidence relation between circRNA ci and a disease d is predicted. The invention further discloses a system and application for realizing the prediction method, and the system and the application have wide application scenes.

The application discloses a kind of multi-modal small sample plant disease identification methods, the method is: through collection and acquisition plant disease image, data pretreatment is carried out to picture;Propose VSF model, including semantic upgrade module, utilize large language model and prompt word to plant disease definition upgrade generation more real and conform to agricultural scene disease description;Design two-stage modal fusion module, effectively improve the consistency and discriminant ability of cross-modal information;The image data after pretreatment is input into model and is trained and verified, obtains optimal model;Finally, the image to be classified is input into optimal model to obtain classification result.The multi-modal small sample plant disease identification method proposed in the application can ensure rapid identification of plant diseases and obtain the correct type, and has good application prospect.

The invention discloses a knowledge graph construction method and system based on novel safe medical registry warm pathology, and particularly relates to the technical field of traditional Chinese medicine knowledge graphs. The method comprises the following steps of: acquiring new safe warm disease classics and medical cases, performing term standardization on disease description and prescription fragments according to medical case numbers, and constructing an aligned text data set; counting the co-occurrence frequency of the disease and prescription terms by using a sliding window, and generating a disease-prescription candidate associated edge set through conditional probability calculation; extracting a pathogenesis stage sequence of the illness state description fragment according to a medical case time sequence, and establishing a pathogenesis stage conversion candidate sequence; calculating a medicine addition and subtraction difference vector between adjacent prescription segments, and performing matching degree calculation on the medicine addition and subtraction difference vector and the pathogenesis stage sequence to generate recessive syndrome conversion evidence data; and finally, constructing a recessive syndrome chain candidate knowledge graph, and applying a link consistency constraint to form a dominant recessive syndrome chain knowledge graph. According to the method, the accuracy and interpretability of the traditional Chinese medicine knowledge graph in clinical application of warm diseases are improved.

The invention relates to the technical field of medical information, and discloses a hospital intelligent number calling system which comprises a user side module, a server module, a doctor side module, a nurse side module and a display interaction module, and a medical risk keyword engine, a dynamic treatment time prediction engine and an associated learning model are deployed in the server module; according to the system, firstly, disease description submitted by a patient is analyzed through a medical risk keyword engine, a temporary diagnosis complexity weight is generated, and early warning is carried out on a high-risk patient; and then, the dynamic treatment time prediction engine calculates and dynamically updates the predicted treatment time of each patient in the queue by combining the weight, the historical average duration of the doctor and the current treatment progress. Through preliminary quantitative evaluation of the condition of the patient and closed-loop optimization of machine learning, advanced identification of the high-risk patient is realized, the accuracy of doctor-seeing time prediction is improved, and the distribution efficiency of medical resources is optimized.