8 results about "Disease factors" patented technology

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.

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.