20 results about "Disease factors" patented technology

The invention discloses an intelligent inquiry dialogue method and device, computer equipment and a storage medium. The method comprises the steps of obtaining an inquiry dialogue of a target user through an intelligent inquiry dialogue interface; extracting disease feature information from the inquiry dialogue; querying a medical decision database based on the disease feature information to obtain a suspected disease set; calculating a suspicion degree corresponding to a suspected disease in the suspected disease set; screening a target suspected disease from the suspected disease set based on the suspicion; according to the disease feature information, determining whether the target suspected disease has an unconfirmed disease factor; if yes, generating a question generation task based on the unconfirmed disease factors, executing the question generation task through a large language model to obtain questions for the unconfirmed disease factors, and outputting the questions through an intelligent inquiry dialogue interface. According to the method, a transparent and controllable inquiry process is provided, and the risk of diagnosis errors is reduced.

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.

Based on the questionnaire information and health records, it is possible to search / analyze the user's cardiovascular diseases and / or disease history, and recommend customized health functional foods according to the search / analysis results. In addition, it is convenient to provide posts registered by other members, SNS posts, etc., and support sharing the provided posts, SNS posts, etc. with acquaintances. Moreover, it is possible to search for various information such as definitions / causes / related functions / related foods / TIP / TALK for cardiovascular diseases, and search for relevant departments by keywords for age, main symptoms, and secondary symptoms, or support a service that displays the location information of the departments searched in order of proximity to the user on an electronic map through a location-based service. Also, it is possible to search or be recommended for disease names, pharmaceutical ingredients, pharmaceuticals, types of health functional foods, sales information, etc. related to disease factors (nervous system, digestive / metabolic system, reproductive system, muscular system, sensory system, cardiovascular system, endocrine system, immune system, others), and support functions for creating purchase information (image, product name, consumer price, discount rate, selling price, quantity) and product reviews of the searched / recommended health functional foods.

The invention belongs to the technical field of data mining and knowledge discovery, and particularly relates to a rough entropy-based key pathogenic factor acquisition method, which comprises the following steps of: depicting disease factors by using an intuitionistic fuzzy information system, and establishing an effective granulation mechanism based on a dominant relationship in the intuitionistic fuzzy information system, enabling the information particles under the relation to conform to the granularity monotonicity; uncertainty measurement is carried out on the intuitionistic fuzzy information system based on rough entropy, and then the importance degree of pathogenic factors is described; all key pathogenic factors are obtained through the importance degrees of the pathogenic factors, so that the decision quality of medical diagnosis and the efficiency of knowledge discovery are improved. According to the method, redundant factors and data dimensions are reduced, computing resource consumption is reduced, and decision quality and knowledge discovery efficiency are effectively improved.

The present invention relates to the technical field of dynamic prediction models, and discloses a method for constructing a dynamic prediction model for tobacco diseases, which includes the following steps: S1: Dimensionless of meteorological factors. First, collect meteorological factor data and tobacco disease observation data in the pathological area, including survey records of brown spot and common mosaic virus disease, and meteorological observation station data including the average value and extreme value of meteorological data; S2: Calculation of disease factors; S3: Data processing. Analyze the calculated data through Python 2.0 programming for correlation analysis and stepwise regression analysis; S4: Establish a disease dynamic prediction model. The present invention uses a composite factor of meteorological factors and the previous disease condition, and at the same time considers the influence of wind, which is more targeted. In terms of the prediction period, taking 5 days as the prediction period, which is consistent with the step length of short-term weather forecasts, and the accuracy of short-term weather forecasts is relatively high. Therefore, the reliability of the disease prediction model proposed by the present invention is significantly improved and the practicability is stronger.

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 invention provides a longitudinal connecting plate type ballastless track refined bar planting modeling and renovation method and system, and the method comprises the steps: building a plate type ballastless track-bar planting geometric model, setting a track structure interlayer relation and a track-pin action relation, inputting a temperature load, setting a boundary condition, and calculating a ballastless track temperature effect. Performing deep mining analysis on model calculation data, evaluating the influence of the temperature load on the disease degree, predicting the possible disease development trend in the future, and establishing a disease prediction model; based on a deep mining analysis result, an improvement countermeasure is intelligently adjusted, an improvement scheme is determined, construction preparation, on-site construction, real-time monitoring and improvement effect evaluation are performed according to the improvement scheme, the construction quality and safety are ensured, and the improvement scheme is adjusted and optimized. According to the method, the accurate three-dimensional finite element model is constructed, the reasonable temperature load is applied, and the common embedded steel bar disease factors are introduced, so that the accurate prediction of the upward arch deformation of the track structure is realized.

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 present invention provides a system for assessing depression risk of ALS patients based on transfer learning, including: a data acquisition unit for collecting EEG data of ALS patients to be assessed; a source domain depression risk assessment unit, obtained based on pre-training of a sample set of EEG data of non-ALS patients, including a feature extraction module for extracting EEG features from the EEG data set and a depression risk classification module for predicting depression risk classification; a target domain feature adaptation module, arranged between the feature extraction module and the depression risk classification module, for screening and correcting the extracted EEG features of ALS patients to be assessed based on EEG feature data corresponding to the sample set of EEG data of non-ALS patients, and inputting the EEG features into the depression risk classification module to predict the depression risk of ALS patients to be assessed. The present invention solves the problem that the depression risk assessment in the prior art does not take into account the influence of ALS disease factors on depression judgment, resulting in poor accuracy in depression risk assessment for ALS patients.

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.

The invention discloses a neuroanatomical heterogeneity analysis method integrating a standard model and non-negative matrix factorization, and belongs to the field of biomedical signal processing. The method comprises the following steps: preprocessing structural magnetic resonance image data of a patient and a corresponding healthy control group to extract brain morphological indexes; establishing a brain morphological standard model of the patient through the migration pre-training model to estimate a deviation score matrix of the cortex thickness and the surface area of the patient, 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 recombination matrix; by rearranging the sampling deviation matrix, an average semi-decomposition stability coefficient and a reconstruction error are calculated to determine a disease factor and weight composition under an optimal component number. According to the method, the spatial pattern information of the group level is integrated while the individual heterogeneity information is reserved, and a new technical means is provided for accurate diagnosis of neuropsychiatric diseases.

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.