13 results about "Disease factors" patented technology

The invention discloses an online gynecological disease consultation and inquiry interaction system, and relates to the technical field of artificial intelligence, and the system comprises a user information and symptom collection module which collects the basic information of a user; the intelligent diagnosis module is used for analyzing data input by the user based on the trained gynecological disease diagnosis model; the disease analysis module is used for extracting disease factors based on the user information and the symptom data of the user, extracting gynecological disease characteristics of the user and establishing a gynecological disease similarity calculation model; the expert inquiry module is connected with online gynecological experts and performs real-time online inquiry interaction with the user; and the interaction feedback module supports real-time interaction between the user and the intelligent diagnosis system and between the user and the expert, and feeds the diagnosis result and the treatment suggestion back to the user. Through the intelligent diagnosis module, the disease analysis module, the expert inquiry module and the interactive feedback module, preliminary diagnosis and personalized treatment schemes are rapidly provided, the regional limitation is broken through, the medical experience is improved, and the medical resource utilization rate is increased.

The invention discloses a physical examination system based on medical data analysis, and belongs to the technical field of medical data processing and health management. The system comprises a data acquisition module which acquires user physical examination data including a user ID, physical examination time, a physical examination result and a physical examination basic index through an HL7FHIR protocol; the data integration module constructs a personal health basic archive set based on user physical examination data, and constructs a group disease factor model library containing n diseases and one health index mapping model through dynamic density segmentation processing; the intelligent analysis module performs similarity analysis on the user physical examination basic indexes and a group disease factor model library, generates a personal disease risk probability sequence and judges a risk level to obtain a user physical examination disease set; and the medical module matches a physical examination screening item scheme based on the user physical examination disease set and pushes the scheme to the physical examination user. According to the system, accurate risk assessment and targeted screening of diseases are realized, and the physical examination efficiency and the health management level are improved.

The invention discloses a traditional Chinese medicine constitution identification method and device for a coronary heart disease patient and a computer readable storage medium. The traditional Chinese medicine constitution identification method comprises the following steps: acquiring traditional Chinese medicine constitution identification data and a disease factor of a patient; performing classification based on the disease factors and the traditional Chinese medicine constitution recognition data, and determining the traditional Chinese medicine constitution types of the patients of the corresponding disease factors; fusing the disease factors, the traditional Chinese medicine constitution recognition information and the traditional Chinese medicine constitution types of the patients of the corresponding disease factors; training an LDA topic model through data obtained after fusion to obtain a traditional Chinese medicine constitution identification model; and inputting the target disease factor of the to-be-identified patient into the traditional Chinese medicine constitution identification model for identification to obtain a target traditional Chinese medicine constitution type of the to-be-identified patient. The technical problem that the correlation between the coronary heart disease and the traditional Chinese medicine constitution is not considered, so that the intelligent analysis of the coronary heart disease and the traditional Chinese medicine constitution is not accurate enough is solved.

The present invention may be an invention that can search for / analyze a disease of interest and / or disease history of a user on the basis of questionnaire information and health records, and recommends customized health functional foods according to the search / analysis results. In addition, the present invention may be an invention that provides support to make it easy to receive posts, SNS posts, etc. registered by other members and share the received posts, SNS posts, etc. with acquaintances. In addition, the present invention may be an invention that can search for various information such as definitions / causes / related functionality / related foods / TIP / TALK, etc. about the disease of interest and supports a service in which location information about searched medical departments is displayed on an electronic map in order of proximity to a user through a location-based service and a search of related medical departments using keywords about age, cardinal symptoms, and accessory symptoms. In addition, the present invention may be an invention that searches and receives recommendations for disease names related to disease factors (nervous system, digestive / metabolic system, reproductive system, muscular system, sensory system, cardiovascular system, endocrine system, immune system, etc.), medicinal ingredients, medicines, types of health functional foods, sales information, etc., and supports purchase information (image, product name, consumer price, discount rate, selling price, quantity) about the searched / recommended health functional foods, and functions related to writing product reviews.

The present disclosure provides an intelligent medical consultation dialogue method, including: acquiring a medical consultation dialogue from a target user through an intelligent medical consultation dialogue interface; extracting disease feature information from the consultation dialogue; querying a medical decision-making database based on the disease feature information to obtain a suspected disease set; calculating a degree of suspicion corresponding to the suspected disease in the suspected disease set; screening for a target suspected disease from the suspected disease set based on the degree of suspicion; determining whether an unconfirmed disease factor exists in the suspected disease based on the disease feature information; and initiating a question generation task based on the unconfirmed disease factor, performing the question generation task using a large language model to obtain a question targeting the unconfirmed disease factor, and outputting the question. The method provides a transparent and controllable consultation process, reducing risk of diagnostic errors.

The application discloses a neural anatomical heterogeneity analysis method integrating standard model and non-negative matrix factorization, and belongs to the field of biomedical signal processing. The application comprises: preprocessing structural magnetic resonance imaging data of patients and corresponding healthy control groups to extract brain morphological indexes; establishing a brain morphological standard model of the patient through a transfer pre-training model to estimate a deviation score matrix of the patient's cortical thickness and surface area, and extracting a non-positive part to obtain a brain morphological atrophy deviation matrix; performing non-negative matrix factorization on the atrophy deviation matrix to generate a preliminary disease factor matrix and a weight composition matrix; and rearranging and sampling the deviation matrix, calculating an average half-decomposition stability coefficient and a reconstruction error to determine the disease factor and the weight composition under the optimal component number. The method of the application integrates spatial pattern information at the group level while retaining individual heterogeneity information, and provides a new technical means for precise diagnosis of neuropsychiatric diseases.

An intelligent medical consultation dialogue method, including: acquiring a medical consultation dialogue from a target user through an intelligent medical consultation dialogue interface; extracting disease feature information from the consultation dialogue; querying a medical decision-making database based on the disease feature information to obtain a suspected disease set; calculating a degree of suspicion corresponding to the suspected disease in the suspected disease set; screening for a target suspected disease from the suspected disease set based on the degree of suspicion; determining whether an unconfirmed disease factor exists in the suspected disease based on the disease feature information; and initiating a question generation task based on the unconfirmed disease factor, performing the question generation task using a large language model to obtain a question targeting the unconfirmed disease factor, and outputting the question is provided. The method provides a transparent and controllable consultation process, reducing risk of diagnostic errors.

The invention relates to the technical field of personal health risk assessment, in particular to a pediatric severe nursing intelligent monitoring system, which comprises a data processor, and the data processor is used for executing the following pediatric severe nursing intelligent monitoring method: determining respiration data and a plurality of preset physiological data of a child in a target time period, comprising heart rate data and blood pressure data; determining an abnormal time period of the preset physiological data so as to screen and obtain a real abnormal time period; illness state mapping features represented by the preset physiological data are obtained according to the real abnormal time period; and according to the synchronization condition of the real abnormal time periods of the heart rate data and the blood pressure data in time, determining the heart rate and blood pressure abnormality collaboration, thereby obtaining the health condition of the child. According to the intelligent monitoring system for pediatric severe nursing, monitoring interference caused by non-illness factors can be avoided, and the reliability of pediatric severe nursing monitoring and the accuracy of child health condition monitoring are improved.

The invention provides a doctor recommendation method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of data mining. The doctor recommendation method comprises the steps that a heterogeneous information network model and medical history information of a target patient are acquired, and the medical history information comprises at least two target diseases and corresponding disease weights; according to a heterogeneous information network model and the medical history information, the matching degree between the target patient and each doctor is evaluated, and the heterogeneous information network model is used for representing the multi-dimensional connection relation between each disease and each doctor; the matching degrees are ranked from high to low, doctors corresponding to the first N matching degrees are selected and recommended to the target patient, and N refers to a preset number. The problem that in the prior art, only a single disease factor is considered, and similar doctors meeting multi-aspect treatment requirements of patients with multiple medical histories and complicated illness conditions cannot be accurately recommended is solved. The method is suitable for doctor matching and recommendation scenes.

This information processing device is for predicting the prognosis of a subject patient affected by a disease, and includes a model acquisition unit, a subject patient information acquisition unit, and a prognosis prediction execution unit. The model acquisition unit acquires a prognosis prediction model, being a machine learning model that takes time-series information indicating a time-series transition of factors of the disease as an input, and outputs a prognosis of the disease. The subject patient information acquisition unit acquires time-series information about the subject patient. The prognosis prediction execution unit executes prognosis prediction of the subject patient using the time-series information about the subject patient and the prognosis prediction model, and outputs a result of the prognosis prediction.

The invention discloses a global suicide rate prediction method based on multi-dimensional parameters and a random forest model, and belongs to the field of public health prediction. The method comprises the following specific steps: step 1, determining a national scope and a time sequence according to statistical data of websites such as a world health organization and a world bank; step 2, acquiring multi-dimensional data; 3, constructing a machine learning model and adjusting and optimizing the model; 4, predicting the suicide rate of the future global country scale; and step 5, result visualization. According to the method, multi-dimensional parameters such as natural environment, social economy, population structure and disease factors are integrated, a high-precision prediction model is constructed by using a machine learning algorithm, and a scientific basis can be provided for a global suicide prevention strategy.

The invention discloses an epidemic disease prevention and control method and system based on continuous monitoring of a feeding environment, and relates to the technical field of epidemic disease prevention and control, and the method comprises the steps: collecting short-term dynamic environment data and long-term static breeding data of a pig farm; extracting short-term environment fluctuation features and long-term epidemic disease associated features; predicting a short-term epidemic disease risk score based on the short-term environment fluctuation characteristics, and predicting a long-term epidemic disease risk score based on the long-term epidemic disease association characteristics; and fusing the short-term epidemic disease risk score and the long-term epidemic disease risk score to obtain a comprehensive risk score, and carrying out comprehensive assessment on the epidemic disease risk. According to the method, short-term dynamic environment data and long-term static breeding data of a farm are collected, short-term environment fluctuation and long-term epidemic disease associated characteristics are extracted, short-term epidemic disease risk scores and long-term epidemic disease risk scores are predicted respectively, and a comprehensive risk score is obtained through adaptive weight fusion. The key factors of epidemic diseases can be comprehensively captured by fusing the two types of data, and the influence of short-term environment mutation and long-term breeding rules can be balanced by the adaptive weight.

This invention discloses a method, computer device, and storage medium for mining key disease factors by combining multi-omics data. The method includes acquiring multiple omics data related to a specific disease; acquiring a first matrix and a second matrix related to the disease factors under investigation from the omics data; establishing a joint sparse optimization regularization model; and solving the joint sparse optimization regularization model to obtain key disease factors. This invention can obtain key disease factors for a specific disease, reducing the number of disease factors to be analyzed and improving efficiency. By integrating multi-omics data related to a specific disease to extract high-quality omics information, it treats all omics components of the lesions during the disease's development as a complete target group, realizing the quantification process of the regulatory effect of key factors on disease targets, and effectively improving the prediction accuracy of key factors. This invention has wide applications in the field of bioinformatics.