5 results about "Mass index" patented technology

The application discloses a coronary heart disease prediction method fusing cardiopulmonary exercise parameter time sequence information, and comprises the following steps: collecting multidimensional health data of a patient; preprocessing the multidimensional health data; extracting features from the preprocessed data; performing time sequence analysis on the extracted features by using a deep learning model; inputting the time sequence analysis result and static features into a classification model to perform coronary heart disease prediction, outputting a prediction result and generating a related health report. By acquiring demographic data, basic medical information and cardiopulmonary exercise test data of a target object, specifically including age, gender, body mass index, socio-economic information, past history, complications, clinical examination and inspection, exercise capacity, cardiovascular function and ventilation perfusion and the like, the coronary heart disease is predicted by fusing static parameters and time sequence information, so that dynamic changes in the health data of the patient can be better captured, and the prediction accuracy is improved.

The invention discloses a device and equipment for predicting the death rate of a patient with chronic obstructive pulmonary disease and a medium, and relates to the technical field of clinical medicine application. The device comprises an acquisition module and an input module. The acquisition module is used for acquiring physical sign parameters of a patient; the physical sign parameters at least comprise age, a body mass index, the percentage of FEV1 accounting for a predicted value, an mMRC score and the occurrence frequency of first-level acute exacerbation in a preset follow-up visit period; the input module is used for inputting the physical sign parameters into the chronic obstructive pulmonary disease patient death rate prediction model so as to obtain the death rate corresponding to the patient through the chronic obstructive pulmonary disease patient death rate prediction model; the model for predicting the death rate of the chronic obstructive pulmonary disease patient is a model generated according to the death rates corresponding to the patients with different physical sign parameters. According to the scheme, the model for predicting the death rate of the patient with chronic obstructive pulmonary disease is constructed on the basis of multiple predictive factors, and more comprehensive evaluation can be provided for prognosis of the chronic obstructive pulmonary disease compared with a single predictive factor; meanwhile, the physical sign parameters of the patient are simply and quickly collected, and the prediction efficiency is improved.

A method (400) and system (100) for predicting a fertility cycle of a user is disclosed A processor (302) acquires physiological biomarker data comprising at least one of a heart rate, a heart-rate variability, a skin temperature, and a skin perfusion. A rolling baseline is computed by processing the physiological biomarker data using a rolling-average technique. A stress-rhythm score that decays from a predefined value in response to non-fitness-related heart-rate elevations detected as deviations from the rolling baseline. Lifestyle-biomarker data of the user comprising at least one of a body-mass index (BMI), and an age is received. Historical fertility cycle data of the user is retrieved. A fertility cycle comprising an ovulation day and a fertile window of the user is predicted based on a weighted probabilistic analysis of the physiological biomarker data, the stress-rhythm score, the lifestyle-biomarker data, and the historical fertility cycle data of the user.

The present application relates to the technical field of medical data processing, in particular to a system for evaluating glutamic oxalacetic transaminase value, which comprises a data acquisition and standardization module, a feature engineering and proxy variable construction module, a multi-path joint regression prediction module, an interpretable risk stratification output module and a continuous learning and federal deployment module. The system takes blood lipids, body weight / body mass index and diastolic blood pressure as input features, innovatively constructs a thyroid function proxy score as an intermediate latent variable, realizes rapid regression prediction of glutamic oxalacetic transaminase value through a three-path parallel sub-network hierarchical fusion network, outputs point estimates and Bayesian confidence intervals; at the same time, based on the SHAP algorithm, an interpretable feature attribution report and a four-level risk stratification result are output, and multi-institutional compliance collaborative training is realized through horizontal federated learning and differential privacy mechanism.

1. The name of the design product: height and weight measuring instrument. 2. The use of the design product: for measuring the height, weight, body mass index and human body composition. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.