62 results about "Disease entity" patented technology

A method of assessing risk for an individual to experience a specific outcome within a disease entity within a specified time frame is provided. Peer-reviewed scientific publications are analyzed to identify pertinent risk factors (212) for developing disease processes and their possible complications. Information that characterizes an individual (218) in relation to the identified risk factors is then received, preferably responsive to question (224) regarding any demographic values of an individual under test and questions regarding medical chracterisitics of the individual under test. An estimate of risk of the individual acquiring the outcome within a specified time frame is performed based on the identified plurality of risk factors (212). Assessment of medical risk and condition may include analyzing peer-reviewed scientific publications to identify populations affected by a medical outcome and for each population respective risk factors that affect risk of acquiring the medical outcome within a specified time-frame, associating an individual with one of the identified population, identifying information that characterizes the individual (218) in relation to the respective risk factors other associated population, and estimating risk of the individual having the medical outcome within the specified the time frame responsive to the identified information. Promotion of business on a site of a computer network is provided by supplying an on-line questionnaire (224) regarding characteristics of individual (218) under test, receiving information regarding characteristics of the individual under test, and responsive to the received information, providing an assessment of the individual under test having a medical outcome within a specified time frame.

The invention discloses an intelligent medical aid decision making system on basis of a cloud computing technique and a medical knowledge base technique, and belongs to the technical field of medical information. The intelligent medical aid decision making system includes an inquiry knowledge base module and a doctor-patient communication inquiry module; the doctor-patient communication inquiry module includes a consultation launching unit, an intelligent inquiry unit, a manual inquiry unit, a diagnosis and treatment scheme generation unit and a medical history generation unit; and the inquiry knowledge base module is a purchased third party knowledge base service or a self-established knowledge base, and includes basic data of an expert knowledge base which are collected based on departments or disease entities, a personalization inquiry module corresponding to doctors, and an intelligent inquiry path associated with inquiry questions and patient answers. Through a cloud server, a neural network technique, and an intelligent mobile device, the intelligent medical aid decision making system can improve the efficiency and the accuracy of a whole diagnosis process, and effectively relieve the difficulty of getting medical service.

The invention discloses an intelligent questioning-answering system construction method and system based on deep learning and a knowledge atlas. A crawler is utilized to obtain an interrogation medical dataset of the internet, and data preprocessing is conducted to obtain a labeled dataset; a word-splitting dictionary based on the medical field is constructed through the further utilization of a hospital electronic medical record, and is merged with a medical dictionary to serve as a word-splitting dictionary of the system; the knowledge atlas associated with diseases and symptoms is constructed, and disease entity aligning and symptom entity aligning are conducted; according to disease entity aligning, the labeled dataset is obtained; a language model based on deep learning is constructed; a query optimization algorithm which is combined with contextual information of a user and is based on the knowledge atlas is constructed; a training dataset merged by the language model and the knowledge atlas is constructed for model merging training, and a pre-diagnosis merging model based on the language model and the knowledge atlas is obtained. By means of the intelligent questioning-answering system construction method and system based on deep learning and the knowledge atlas, active interrogation interaction through the further utilization of self-reported information of the user anddisease pre-diagnosis according to the self-reported information and interrogation information of the user are achieved.

The present invention discloses a method and a device for constructing a medical knowledge graph and an assistant diagnosis method. The assistant diagnosis method based on a medical knowledge graph comprises the steps of: acquiring complaint data and examination data of a patient and processing the data to obtain symptom entities and sign entities of the patient; searching, from the medical knowledge graph, disease entities associated with the symptom entities and the sign entities, calculating a posterior probability of each disease entity separately under a set of its corresponding symptom entities and sign entities; and outputting a disease entity with a maximum posterior probability as well as data corresponding to its associated nodes. The present invention provides an intelligent assistant diagnosis for clinical medicine to reduce the work burden of medical staff, relieve the medical pressure and reduce medical accidents.

The invention relates to a disease prediction method based on automatic medical specialist knowledge extraction, and belongs to the technical field of intelligent medical treatment. The method comprises the following steps: firstly, constructing a disease relation network according to historical diagnosis record data, calculating the disease feature vectors on the network through the explicit andimplicit correlations between the disease entities by using the neural network model, and calculating the correlation matrix between the diseases through disease feature vectors to serve as medical specialist knowledge; secondly, designing a disease prediction model based on deep learning, and subjecting the original medical index data of the patient to dimensionality reduction through a noise reduction self-encoder stack model, and predicting the potential disease of the patient by taking the data as the input data of the multi-label disease prediction model; and finally, in the parameter learning part of the model, taking a disease similarity matrix which is automatically extracted in the first step as a medical background constraint condition, making an optimal parameter of the algorithm learning model, and taking a disease with relatively high incidence probability as a prediction result. Compared with the prior art, the disease prediction accuracy is improved.

The invention provides a multi-disciplinary clinical examination combination recommendation method and device, a combination of examination items that a patient should accept can be recommended, and adoctor can be assisted to make a diagnosis. The method comprises a step of obtaining symptom information of the patient, a step of inputting the symptom information into a constructed multi-label Bayesian classifier to obtain a prediction set of diseases of the patient, wherein the multi-label Bayesian classifier is obtained by training symptoms extracted from an electronic medical record and a diagnosis result, and a step of determining a recommendation item combination of medical examination from a medical knowledge map according to the prediction set, wherein the medical knowledge map includes a symptom entity, a disease entity, a single examination entity and an examination group entity, and the examination group entity is determined by calculating a frequent item set of the single examination entity.

The invention discloses a method for disambiguating entity names in a medical disease diagnosis record. Based on a heterogeneous concomitant disease network and a graph model, the entity names in the medical disease diagnosis record are disambiguated. The similarity between to-be-disambiguated entity names and candidate entity names is used as local information, and the contribution of other to-be-disambiguated entities in the same record to current to-be-disambiguated entities serves as global information, so that the accuracy of medical entity name disambiguation can be improved; the heterogeneous concomitant disease network is established according to the disease diagnosis record and annotation data, so that the relationships between the diseases and between the disease and the operation can be reflected more intuitively and credibly; and the entity names are subjected to standard name mapping accurately and efficiently, so that the problem of ambiguity of medical disease entity names in diagnosis information is solved, and the practical application demands are met.

The invention discloses an intelligent disease inquiry method, device and equipment and a storage medium, and the method comprises the steps: obtaining a natural inquiry statement input by a user, andextracting symptom keywords in the natural inquiry statement; identifying an inquiry category in the natural inquiry statement according to a preset neural network classification model; when the inquiry category is triage inquiry, inputting the symptom keyword into a preset triage model, and obtaining a triage department which is output by the triage model and corresponds to the symptom keyword;and when the inquiry category is symptom inquiry, inputting the disease keyword into a preset symptom question and answer model, and obtaining a disease entity which is output by the disease questionand answer model and corresponds to the disease keyword. According to the method, the triage precision can be improved, the diagnosis time wasted by a doctor due to the fact that the doctor hangs thedepartment number by mistake is saved, the hospitalizing efficiency is optimized, meanwhile, the patient can conduct disease inquiry to have more understanding on own diseases or symptoms, and the user experience is greatly improved.

The invention relates to a data processing method for clinical pathway quality evaluation. The data processing method comprises the steps of collecting and classifying clinical pathway quality evaluation index data of each hospital, combining similar terms of clinical pathway quality evaluation index data with same substances to acquire simplified quality evaluation index data, calculating the weight of the simplified quality evaluation index data by virtue of a gray relational degree analysis method, setting a grading value of the weighted quality evaluation index data, and calculating a combined score according to the grading value and the weight for the clinical pathway quality evaluation. The data processing method is suitable for sanitary administrative organs to evaluate the hospitals implementing clinical pathway management and execute emphasis management on later-ranked hospitals and weak links. The data processing method is also suitable for the hospitals to make transverse comparison on multiple disease entities in the clinical pathway management of each department, and the disease entities with relatively poor quality and the weak links of the disease entities can be rapidly identified and subjected to priority intervention, so that the management efficiency is improved, and the clinical pathway management quality is continuously improved.

The invention discloses a triage method, device and equipment based on a medical knowledge graph and a storage medium. The method comprises the steps of receiving disease description statements inputby a user; performing character encoding on the statement by using a pre-trained BERT model to generate a word vector, and decoding the word vector by using a BILSTM model and a CRF model to obtain adisease entity; linking the disease entities to standard disease entities in a knowledge graph by using an entity linking algorithm; vectorizing the standard symptom entity text, inputting the vectorized standard symptom entity text into a pre-trained XGBoost classification model based on a medical knowledge graph, and taking a model output result as a recommended doctor-seeing department; and returning the recommended doctor-seeing department to the user. According to the method, the main complaint of the user is directly regarded as the serialized text, the XGBoost classification model is adopted for classification, the classification result is the recommended doctor-seeing department, the trouble brought by multiple rounds of input of the user is reduced, the use experience is good, theadvantages in the aspect of spoken language recognition are obvious, and the triage result is accurate.

The embodiment of the invention provides a medical knowledge graph construction method and device. The method comprises: acquiring diagnosis evidence information, clinical manifestation information and disease information; according to the diagnosis evidence information, the clinical manifestation information and the disease information, utilizing a preset diagnosis standard, a clinical standard and a disease diagnosis method to establish an association relationship between the disease and the multi-layer clinical manifestation and an association relationship between the clinical manifestationof each layer and the diagnosis evidence; and according to the association relationship between the disease and the multi-layer clinical manifestation and the association relationship between the clinical manifestation of each layer and the diagnosis evidence, constructing a medical knowledge graph containing the association relationship among the diagnosis evidence entity, the clinical manifestation entity and the disease entity. According to the method, clinical manifestation information is added into the medical knowledge graph, and the association relationship between diagnosis evidences and diseases is established through multi-layer clinical manifestation, so that the medical knowledge graph can fully reflect the differential diagnosis thought of doctors.

The embodiment of the invention provides a disease inference system based on a medical knowledge graph, wherein the system includes an acquisition module for obtaining diagnosis evidence information of patients, and a disease inference module for querying the medical knowledge graph according to the diagnosis evidence information of the patients, obtaining the clinical manifestation correspondingto the diagnosis evidence information, querying the medical knowledge graph according to the clinical manifestation and obtaining disease information corresponding to the clinical manifestation; the medical knowledge graph is a mapping map established in advance according to a disease knowledge base and the professional knowledge of doctors and used for representing the interrelationship among thediagnosis evidence entity, the clinical manifestation entity and the suspected disease entity.

The present invention discloses a medical insurance data-based clinical pathway analysis method. The method comprises a medical insurance data cleaning step and a medical insurance data analyzing step. During the medical insurance data cleaning step, invalid hospitalization data and invalid prescription detail data are removed. Meanwhile, data are clustered based on identical disease entity numbers and identical hospital level numbers, so that a plurality of analysis units are established. During the medical insurance data analyzing step, medical insurance analysis items in the analysis unitsare subjected to empirical range analysis. The empirical range analysis is in the form of grouped empirical range analysis or non-grouped empirical range analysis, wherein the sample most aggregated distribution range of medical insurance analysis items is calculated. When the frequency sum of the most aggregated distribution range is larger than a preset frequency, the empirical range of the medical insurance analysis items is obtained. According to the invention, medical insurance data are cleaned and analyzed, and then the clinical pathway analysis is conducted based on medical insurance big data. The accuracy and the scientificity of the clinical pathway analysis are improved.

The invention provides a method and device for comparing practical clinical pathways of disease entities with standard clinical pathways of the disease entities. The method comprises the following steps of: obtaining a treatment sequence of a preset quantity of patients with a target disease entity; generating practical clinical pathways of the target disease entity according to the treatment sequence of the preset quantity of patients; associating each treatment unit in the practical clinical pathways of the target disease entity into a corresponding task unit in a standard clinical pathway of the target disease entity; and comparing the practical clinical pathways of the target disease entity with the standard clinical pathway of the target disease entity. According to the method, the treatment sequence of the preset quantity of patients with the target disease entity is automatically obtained, the practical clinical pathways of the target disease entity are generated according to the treatment sequence, and the practical clinical pathway of the target disease entity is compared with the standard clinical pathway of the target disease entity to automatic obtain the practical clinical pathways inconsistent with the standard clinical pathway, so that the execution efficiency of the standard clinical pathway is ensured, the implementation effect of the standard clinical pathway is improved and the unnecessary inspection and expenses are reduced.

The invention provides a diabetic foot knowledge graph generation method and device and a readable storage medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring disease corpus data of related fields of the diabetic foot; extracting a plurality of disease entities from the disease corpus data; obtaining attribute information of each disease entity, wherein the attribute information is used for representing feature information of each disease entity; utilizing the attribute information to construct an association relationship among a plurality of disease entities; creating a knowledge base of the diabetic foot based on the association relationship; and associating the knowledge base of the diabetic foot with a pre-created knowledgegraph architecture to generate a knowledge graph of the diabetic foot. According to the scheme, the knowledge graph is generated by constructing the knowledge base of the diabetic foot and then associating the knowledge base with the pre-created knowledge graph architecture, so that the knowledge graph can be constructed directly based on the knowledge graph architecture when the knowledge graphof the diabetic foot is constructed, and the knowledge graph can be constructed more quickly and conveniently.

The invention relates to a document marking method, apparatus and device, and a computer readable medium. The document marking method includes the steps: receiving a gene retrieval request, submittedby a user, including a target gene; searching a relationship between genes, variations and diseases in a database, matching the target gene, and source document information of the relationship; identifying the named entity for genes, variations, and diseases on the source documents corresponding to the source document information, and marking the identified named entity data; and in a co-occurrence statement of relationship types in which at least two of the named entity data are located, determining the relationship type between named entities and marking the relationship type in the source documents. The document marking method, apparatus and device, and a computer readable medium can provide an ability to locate mark genes, variations, and disease entities, and marking the relationshipsbetween genes, variations, and disease entities in the medical documents, and can provide fine-grained traceability of documentary evidence.

The embodiment of the invention belongs to the technical field of artificial intelligence, and relates to a named entity identification method. The method comprises the steps: receiving an identification request carrying an original text sent by a user terminal; carrying out preprocessing operation on the original text to obtain annotation data; inputting the annotation data into a trained entityprediction model to perform entity prediction operation, and obtaining an entity prediction result; taking the entity prediction result meeting a prediction threshold as an entity identification result; and outputting the entity identification result to the user terminal. The invention further provides a named entity recognition device, computer equipment and a storage medium. The generalization ability of the model is improved; disease entities which do not appear in the dictionary base can also be identified and processed; a new disease combination entity can also be identified; the model can identify the disease entity according to the context semantic information, and the recall rate and accuracy of the model are improved.

The invention relates to the technical field of artificial intelligence, in particular to the field of disease entity annotation of natural language processing, and discloses a pre-training model acquisition method and device, a disease entity labeling method and device and a storage medium, which can effectively improve the pre-training efficiency. The method comprises the steps of obtaining a first Chinese corpus, and performing word segmentation processing on the first Chinese corpus to obtain a preliminary word segmentation result; performing word segmentation on the non-common words of the preliminary word segmentation result to obtain a target word segmentation result; creating a Chinese vocabulary of a pre-training model according to the target word segmentation result; and pre-training the pre-training model by using the Chinese vocabulary to obtain a target pre-training model.

The invention discloses a knowledge graph-based old medicine new use analysis method. The method comprises the steps of obtaining a knowledge graph; extracting all disease entities, medicine entitiesand gene entities and relationships among the disease entities, the medicine entities and the gene entities in the knowledge graph; vectorizing all entities and relationships to obtain a feature vector of each entity and a feature vector of the relationship; determining one or more candidate machine learning models; training the candidate machine learning model; verifying and comparing the candidate machine learning models, and selecting an optimal machine learning model; and predicting a specific disease by applying the optimal model to obtain a plurality of medicine entities related to the specific disease.

The invention discloses a medical consultation dialogue system and method applying a heterogeneous graph neural network. The system comprises a dialogue history coding module, a medical entity prediction module and a graph guide dialogue generation module, wherein the dialogue history coding module carries out the hierarchical coding of a dialogue history through a neural network model, and obtains the feature vector representation of each statement and the whole dialogue history; the medical entity prediction module is used for constructing a heterogeneous graph containing medical entity nodes and statement information nodes according to the medical knowledge graph and the dialogue history, initializing the statement information nodes in the heterogeneous graph according to the obtained encoding vectors, spreading current information to related entity nodes on the heterogeneous graph by using a graph attention network, predicting symptoms or disease entities which may be inquired by doctors in the next round of dialogue; and the graph guide dialogue generation module is used for dynamically selecting and generating words from a common dialogue word list or using medical entity expression of related nodes of a heterogeneous graph according to the current state of the dialogue and the reasoning result of the heterogeneous graph, so that a more accurate and effective reply containing professional terms is generated.

The invention relates to the field of artificial intelligence, and discloses a department recommendation method which comprises the following steps: cleaning inquiry data in an inquiry text to obtain a standard text; recognizing disease entities in the standard text by using a disease entity recognition model, then constructing an entity relation graph, generating a first disease entity according to the entity relation graph, screening the disease entities from the standard text by using a disease entity regular expression, calculating the matching degree between the screened disease entities and the disease entities in the disease entity dictionary database, and generating a second disease entity according to the matching degree; collecting the first disease entity and the second disease entity after dimension reduction, calculating the correlation degree between the collected disease entities and the departments in the medical department library, selecting the departments with the correlation degree larger than the preset correlation degree from the medical departments, and obtaining a target department. In addition, the invention also relates to a block chain technology, and the target disease entity can be stored in a block chain. According to the invention, the department recommendation difficulty can be reduced.

The invention discloses an intelligent inspection / consultation system based on medical standard problem library, comprising a medical standard problem library and a medical consultation module, wherein the medical consultation module incudes a consultation request module, an intelligent consultation module, an artificial consultation module, a medical protocol generating module and a medical record making module. The medical standard problem library is a purchased third-party knowledge library service or a self-constructed knowledge library, including basic data of an expert knowledge librarycollected according to departments or disease entities, a personal inquiry template corresponding to a doctor, and an intelligent inquiry path related to inquiry problems and patient answers. The medical standard problem library is pre-set to collect and store various medical problems; the intelligent technique is utilized to reply conventional medical problems through various preset templates through the intelligent technology, and medical consultation efficiency is effectively improved.

The invention relates to artificial intelligence, and provides a medical misdiagnosis detection method and device, electronic equipment and a storage medium. The method can obtain main complaint dataand to-be-diagnosed diseases from a medical misdiagnosis detection request, determine a to-be-diagnosed user, obtain the current medical history of the to-be-diagnosed user, extract the main complaintdata and entities in the current medical history, and obtain a disease entity. A target entity associated with the disease entity is obtained, the weight of the target entity is obtained, the targetentity is converted into a medical knowledge feature vector based on the weight, the main complaint data is processed to obtain a text feature vector, a medical knowledge feature vector and a text feature vector are spliced to obtain a target vector, and the target vector is input into a discrimination model to obtain a disease list. When the to-be-diagnosed diseases do not exist in the disease list, the response result of the medical misdiagnosis detection request is determined as misdiagnosis, the misdiagnosis detection rate can be increased, and real-time early warning can be achieved. In addition, the invention further relates to a blockchain technology, and the response result can be stored in the blockchain.

The invention provides a drug capsule for treating gallbladder disease and a preparation method thereof. The drug capsule is a compound preparation combining traditional Chinese medicine with Western medicine and having good treatment effect. The drug capsule is prepared by processing oriental wormwood, longhairy antenoron herb, Chinese violet, curcuma root, loofah sponge and magnesium sulfate serving as raw materials. The drug capsule contains metabolic substances capable of dredging meridians and collaterals of liver and gall, inducing choleresis and discharging intraheptic stasis. 'Lu su Asking, Six Viscera State Theory' says that all eleven internal organs depend on gall. 64.7% of out-patients in twenty years have liver and gall disease through ear-point electrodiagnosis, and gall disease accounts for 97.3% while liver disease accounts for 2.7%. 53 chronic diseases are caused by functional disorder of gall and liver. The pathological mechanism is disorder in qi-blood circulation. 'Coffin, Meridian Ten' says that meridians can decide life and death, cure all diseases and regulate deficiency and excess, and have to be dredged. Practice of 20 years verifies that scientificity and practicability of the Meridian-Collateral Theory of traditional Chinese medicine are further verified, and disease entities and disease cases are attached.

The invention discloses a method for popularizing major chronic disease education and self-management education. The method comprises the steps of firstly, collecting and organizing major chronic disease education information into a plurality of items of concise characters and images; secondly, printing the organized concise characters and images on cards according to the items; thirdly, classifying the printed cards in the step two by using different disease entities as the basis of classification; fourthly, grouping the classified cards of the same class according to the content; fifthly, using each group of the grouped cards as an independent unit, wherein each group of the cards comprise asking cards and answering cards, and the education or the self-management education is carried out on participants in a mutual-assistance mode with exchange discussion generated by asking and answering questions. According to the method for popularizing the major chronic disease education and the self-management education, the most authoritative content is refined to be the concise characters and images, the chronic disease knowledge is accurately propagated in a simple and low-cost mode, prevention by all the people and early detection and early treatment of high risk groups are promoted, the education is carried out through lively activities, the morbidity of major chronic diseases in China is effectively reduced, and the economic benefits and social benefits of the method are remarkable.

The invention provides a pathological knowledge graph construction method and device. A pathological data knowledge base is built according to medical text data of different corpus sources, and the pathological data knowledge base comprises disease class entity pairs and a first entity relationship used for representing the association between two disease entities in the disease class entity pair; based on the medical data, a medical rule knowledge base is created according to a preset medical rule, and the medical rule knowledge base comprises interpretation type entity pairs and a second entity relationship used for representing association between two interpretation entities in each interpretation type entity pair; equivalent class entities in the disease class entity pair and the interpretation class entity pair are determined; based on the equivalence class entities, the pathological data knowledge base and the medical rule knowledge base are fused into a target knowledge base; and a target pathological knowledge graph is formed based on the target knowledge base for storage.

The invention relates to the field of intelligent decision, and discloses a neural network-based medical department recommendation method. The method comprises the following steps: receiving inquiry data input by users, and performing feature extraction on the inquiry data to obtain feature inquiry data; identifying disease tags of the feature inquiry data by using a tag classification network of a disease entity detection model, and identifying disease entities of the disease tags by using an entity regression network of the disease entity detection model to obtain first disease entities; searching second disease entities matched with the feature inquiry data from an inquiry database; selecting disease entities with the same disease type from the first disease entities and the second disease entities to obtain target disease entities; and matching the target disease entities with medical departments in a medical department library, and recommending the successfully matched medical departments to the users. In addition, the invention also provides a neural network-based medical department recommendation device, electronic equipment and a storage medium. The recommendation accuracy of the medical departments can be improved.

The invention relates to the technical field of artificial intelligence, relates to the technical field of blockchain, and is applied to the intelligent medical field; the invention discloses an entity recognition method and device based on a deep learning model, equipment and a medium. The method comprises the steps of obtaining a to-be-recognized medical text; inputting the to-be-recognized medical text into a preset entity recognition model, wherein the training set of the preset entity recognition model is a medical text training set marked by disease entities mentioned in different modesin the medical texts, obtaining an entity recognition result output by the preset entity recognition model, taking the entity recognition result as the disease entity mentioned in the medical text tobe recognized, and outputting the disease entity; according to the invention, disease entity labeling and recalling are carried out on the training set, and the preset entity recognition model is established in a recalling and natural language reasoning mode, so that the preset entity recognition model can effectively recognize discontinuous and parallel disease entities from the to-be-recognizedmedical text, and the accuracy of disease entity recognition is improved.