19 results about "Disease entity" patented technology

The invention relates to the technical field of road detection, in particular to a multi-dimensional road disease detection method and system based on a ground penetrating radar. The method comprises the following steps: acquiring original ground penetrating radar data, performing DC component removal, gain adjustment and background denoising on the original ground penetrating radar data, positioning a suspected disease area and defining the suspected disease area as a disease entity; extracting a time domain feature, a frequency domain feature and a spatial context feature of each disease entity in parallel to form a multi-dimensional feature vector; constructing a road disease knowledge graph according to the multi-dimensional feature vectors, and distributing an initial weight for each graph relation; and performing node matching operation according to the multi-dimensional feature vector and a knowledge graph, performing logical reasoning according to a graph relation path, fusing similarity and reasoning confidence, and outputting a diagnosis tag signal and a confidence signal. According to the method, the positioning accuracy of the suspected disease area is improved, comprehensive utilization of multi-dimensional information is realized, and a visual causal link is provided for diagnosis.

Methods for identifying disease subgroups are described. The methods may comprise, for example, receiving subject data for a plurality of subjects diagnosed with the disease; creating a plurality of candidate best fit latent class or mixture models by: i) providing an estimate of a number of subgroups; ii) generating a set of models, each model of the set comprising the same estimate of the number of subgroups; iii) selecting a candidate best fit model from the set; and iv) repeating (i) - (iii) at least once using a different estimate of the number of subgroups to obtain a plurality of candidate best fit models; selecting a best fit model from the plurality of candidate best fit models based on a fit statistic; and applying the best fit model to the subject data to identify a number of subgroups for the disease and an associated genomic profile for each subgroup.

The invention relates to the technical field of artificial intelligence, in particular to a method and a device for training a large model in the field of health and care and value system identification application, comprising the following steps: based on UMLS entity relationship data, RugBank drug target data and clinical symptom records, distributing unique indexes for each drug, gene and disease entity, and identifying the value system of the drug, gene and disease entity; and distributing dimension coordinates for each relation type. According to the method, a structured knowledge vector is constructed by uniformly mapping multi-source heterogeneous health-care knowledge such as a UMLS entity relationship, a RugBank drug target and a clinical symptom record into a high-dimensional health-care knowledge tensor containing drug, gene, disease and relationship type dimensions. Furthermore, iterative projection is adopted to decompose the high-dimensional tensor into a low-dimensional core tensor and a multi-term factor matrix to form a compressed knowledge base component, so that physical overhead for storing large-scale relational data is reduced, and a data access structure is optimized.

The invention relates to the technical field of veterinary intelligent diagnosis, and discloses a beef cattle disease diagnosis-oriented hierarchical multi-dimensional knowledge retrieval method, which comprises the following steps of: firstly, receiving and analyzing a diagnosis query text in a natural language form, and extracting a symptom entity set and a background information set; semantic matching is carried out in a low-layer entity set of the layered beef cattle disease knowledge graph based on the symptom entity set, and a candidate disease entity set is reversely derived through a cause relation and a differential diagnosis relation in the graph; and respectively calculating the interpretation coverage degree of the candidate diseases on symptoms and the consistency degree of epidemiological attributes and background information of the candidate diseases, sorting the candidate diseases through multi-dimensional weighted scoring, and generating a structured diagnosis suggestion containing an evidence chain in combination with a reasoning path. According to the method, the problem of misdiagnosis caused by a traditional retrieval method is solved by fusing the clinical representation and the individual environment background information, and the accuracy and interpretability of beef cattle disease diagnosis are improved.

Embodiments of the present application relate to a physical examination knowledge graph construction method and device, computer equipment and a storage medium, comprising: extracting medical concept entities in existing medical documents to obtain a plurality of target medical concept entities, wherein the target medical concept entities include disease entities and feature entities; extracting a plurality of preset parameters between the disease entities and the feature entities; linking the plurality of target medical concept entities with medical concept entities in a first physical examination knowledge graph based on the plurality of preset parameters to obtain a second physical examination knowledge graph; performing data processing on physical examination knowledge in the second physical examination knowledge graph to obtain a third physical examination knowledge graph; and quality evaluating the third physical examination knowledge graph, and obtaining a target physical examination knowledge graph when the quality evaluation result meets a preset condition, thereby avoiding too much manual intervention in the physical examination knowledge graph construction process and reducing the construction cost.

The present application relates to a kind of medical entity identification and knowledge base alignment method for rare disease scene, belong to medical data processing technical field, solve the weak rare disease recognition ability in existing medical entity identification, serious mis-matching problem, single matching strategy, knowledge base alignment difficulty and lack of explainability problem.It includes obtaining medical text to be processed, the semantic analysis of the medical text, identify drug entity and disease entity, and based on the identified entity, obtain the candidate entity name list;The candidate entity name list is input into the pre-constructed medical standard terminology knowledge graph, and structured verification is carried out using a four-stage cascade matching strategy, including: for the current candidate entity, sequentially matching from the first strategy, if the current level strategy successfully matches the entity name in the knowledge base, then output the matching result and terminate subsequent matching, otherwise enter the next level strategy.Medical entity identification and knowledge base alignment are realized.

The invention relates to a method for applying a medical big language model of an integrated knowledge graph to drug analysis and diagnosis support. The method comprises the steps of performing streaming analysis and entity recognition on a structured pharmaceutical database file, generating a drug record set containing standardized biomedical entity annotations, and constructing a graph structure knowledge base and a vector index base in parallel; receiving a natural language query, extracting drugs and disease entities and mapping the drugs and disease entities into standardized identifiers; synchronously retrieving the structured evidence set and the text evidence set; assembling an enhanced prompt text containing the task instruction, the verified evidence and the query according to the template; and inputting the large language model subjected to biomedical task fine tuning, and generating a binding response. By adopting the method, the problems of static stiffness, pure language model illusion and insufficient interaction of a structured knowledge base of a traditional system can be solved, reliable decision assistance is provided for replacement treatment and interaction review under the condition of drug shortage, and the drug use risk is reduced.

The invention relates to a method and equipment for retrieving medical information and a storage medium. The method comprises the steps that based on original input information input by a user, a structured analysis sequence is generated through a pre-trained large language model, the structured analysis sequence at least comprises disease entity text representation information and query intention information, and the query intention information is associated with the disease entity text representation information; on the basis of the disease entity text representation information, coding is carried out through a pre-trained medical language model, multiple pieces of candidate standard disease entity information are obtained, and the pre-trained medical language model is a double-tower model sharing encoder weight; reordering the plurality of candidate standard disease entity information to obtain target standard disease entries; based on the target standard disease entries and the query intention information, constructing a structured semantic analysis result for retrieval; the disease entity and the query intention in the question input by the user can be accurately analyzed, and the accuracy of the retrieval result is remarkably improved.

The invention discloses a medical report interpretation method and device based on multi-modal dynamic knowledge fusion, and the method comprises the steps: recognizing a key region of a target medical report, and extracting a target physiological value index and a target text description index; inputting the baseline feature data into a physiological index interval prediction model to obtain a physiological index reference interval; calculating the physiological value index deviation degree according to the target physiological value index and the physiological index reference interval, and determining the abnormal degree of the physiological value index; key descriptors are extracted, and disease entities, standard text description and disease severity are inquired in the multi-dimensional medical knowledge graph; calculating the semantic similarity between the target text description index and the standard text description, and determining the abnormal degree of the text description index in combination with the disease severity; and determining a comprehensive anomaly score according to the physiological value index anomaly degree and the text description index anomaly degree. The method improves the accuracy and comprehensiveness of medical report interpretation, and can be widely applied to the technical field of artificial intelligence.

The application discloses a disease prediction method based on a disease diagnosis standard knowledge graph, comprising the following steps: constructing a disease diagnosis standard knowledge graph comprising clinical manifestation class entities, examination item class entities, examination result class entities, logic node class entities and disease class entities; finding a first entity set corresponding to the detection result of the examination item of a patient and the clinical manifestation of the patient; taking the entities in the first entity set as starting nodes in sequence, finding disease class entities reached through at least one joint judgment relationship; taking the disease class entities as root nodes in sequence, finding an N-ary tree composed of all entities and relationships pointing to the disease entities through the joint judgment relationship; and judging the logic state of each entity in the N-ary tree. The application has the advantages that the significance of medical examination items and examination results is represented, the whole disease diagnosis knowledge graph is more accurate and more detailed, and the disease diagnosis can be made independently without depending on the professional knowledge and experience of doctors.

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 invention discloses a chest X-ray image anomaly detection method, system and device, and relates to the field of image processing, and the method comprises the steps: obtaining a to-be-processed chest X-ray image; determining complete text description information according to the chest X-ray image, a preset universal cue word and a vision-language model, and encoding the complete text description information to obtain a first text feature; processing the complete text description information to obtain category label text information for describing a disease entity, and coding the category label text information to obtain a second text feature; extracting image features of the chest X-ray image; and according to the first text feature, the second text feature and a preset feature enhancement fusion strategy, determining a multi-modal feature so as to determine an anomaly detection result of the chest X-ray image according to the multi-modal feature. According to the scheme, self-prompt text guidance can be achieved, the detection efficiency is high, the detection precision is high, and the cross-domain generalization ability is higher.

The invention provides a high-speed railway line infrastructure intelligent operation and maintenance method, system and device and a medium. The method comprises the steps of obtaining disease text data; extracting a disease entity in the disease text data; identifying a relationship between the disease entities based on a trigger word; constructing a disease knowledge graph based on the relationship between the disease entities; based on the disease knowledge graph and the incidence relation between the operation and maintenance information and the high-speed railway line infrastructure model entity, the disease and service state view of the high-speed railway line infrastructure entity is displayed, rapid response to disease information is achieved, and visualized display of infrastructure entity diseases is achieved.

The invention discloses a big model dynamic quantification method and device based on disease semantics and a medium, and relates to the field of artificial intelligence. The method comprises the following steps: extracting a disease entity from medical data through a pre-trained named entity recognition model; calculating entity density and semantic relationship complexity of the disease entity based on the medical data; inputting the entity density and the semantic relationship complexity into a preset multi-layer perceptron to obtain an attention score; fusing the entity density and the semantic relationship complexity according to the attention score to obtain a fused feature; mapping the fusion feature into probability distribution data with different quantization precisions through a preset probability distribution mapping function; determining target quantization precision based on the probability distribution data; and adjusting a quantization parameter of a target model to be quantized according to the target quantization precision. According to the method, the accuracy and the reliability of the model can be ensured when the large model is quantified.

The present disclosure relates to a knowledge map construction method, a map encoder training method, and a drug research and development method. The knowledge map construction method comprises: extracting a plurality of entities and relationships among the plurality of entities from data related to drugs and diseases; and on the basis of the extracted entities and relationships, constructing an initial knowledge map, wherein the entities are used for constituting nodes in the initial knowledge map, and the relationships are used for constituting edges between the nodes in the initial knowledge map. The plurality of entities comprise prescription entities and drug entities that belong to a first group and disease entities belonging to a second group, and further comprise at least one of the following: one or more first entities that belong to the first group and are located at lower levels than the drug entities; or one or more second entities that belong to the second group and are located at lower levels than the disease entities.

The invention discloses a cultural relic building structure risk assessment method based on a knowledge graph. The method comprises the following steps: S1, collecting and preprocessing multi-source data of a cultural relic building; s2, analyzing the structural data, extracting components and disease entities, and identifying a connection and propagation relationship; s3, constructing a knowledge graph, setting node types, and executing relation standardization and multi-hop semantic completion; s4, performing propagation and aggregation based on neighborhood dependency and edge type weight by using a graph neural network; s5, traversing a multi-hop path, calculating state dependence intensity and a relation weight, and identifying a structure and a disease risk path; s6, constructing a risk feature group, and executing feature weighting to generate a component risk score; and S7, dividing risk grades according to the risk scores, and outputting risk alarms. According to the invention, multi-source correlation modeling and intelligent evaluation between the cultural relic building component and the disease risk can be realized, and the accuracy of structural risk identification and the timeliness of early warning response are improved.

The present disclosure relates to an explainable disease automatic diagnosis device based on knowledge graph enhancement, which comprises: a data acquisition module for acquiring the symptoms of a patient and the supplementary description of each symptom by a doctor; a data enhancement module for calculating the enhanced representation of each symptom in combination with the supplementary description; a path inference module for performing path inference in a knowledge graph based on the enhanced representation to obtain a path set corresponding to each symptom; wherein the entities in the knowledge graph include diseases, symptoms, operations, drugs, descriptions, related diseases and related symptoms, the relationships in the knowledge graph include being related to, having operation history and disease performance, and the paths in the path set are paths with disease entities as the terminal; a symptom inference module for calculating the disease occurrence probability and / or reward corresponding to all path terminals to obtain the diagnosis result of the symptom. The present application realizes the two purposes of disease diagnosis and cause explanation.

The invention discloses a medical report automatic generation method and system based on category guidance. The method comprises the following steps: acquiring a medical image; the medical image is analyzed through a pre-trained disease classification model, diagnosis category information corresponding to at least one disease entity is generated, and the diagnosis category information is used for representing the existence state of the disease entity in the image; based on the diagnosis category information, guiding prompt information is derived; and controlling a report generation model to convert the medical image into a structured diagnosis report text by utilizing the guide prompt information. Diagnosis category information is introduced to serve as a guiding mechanism, the model can more accurately pay attention to image features related to diseases, and the possibility of missed diagnosis or misdiagnosis is reduced; in the knowledge enhancement step, the latest medical evidence is fused into the generation process by retrieving and fusing structured clinical knowledge; according to the method, the professional, dynamic and interpretable report generation is realized through a multi-level guidance and fusion mechanism.

The present application relates to the technical field of data processing, in particular to a road engineering roadbed disease diagnosis method and system based on a knowledge graph. Geological exploration data, hydrological monitoring data and non-destructive testing data corresponding to a road stake number sequence are acquired, spatial topological edges are constructed using adjacent stake number nodes and disease entity nodes, time sequence evolution edges are constructed using the monitoring state of the same node at different time slices, and a space-time dynamic knowledge graph is generated. Disease characteristic entity sets of a roadbed region to be diagnosed at the current time and hydrogeological sequences within a preset time window are extracted, and in the space-time dynamic knowledge graph, the disease characteristic entity sets are used as a matching starting point, and a time sequence constraint subgraph isomorphism matching is performed in combination with the time labels of the hydrogeological sequences. The present application overcomes the defect that a static graph cannot reflect the dynamic spread path of a disease, and realizes time sequence tracing from the appearance to the historical geological cause of a roadbed disease.