The invention relates to the technical field of
remote management, in particular to a remote
patient tracking management method based on
big data, which comprises the following steps: periodically acquiring physiological data submitted by a patient; encoding the
time sequence characteristics of the physiological data by using an automatic
encoder to generate continuous characteristic representation; constructing a
global distribution model, calculating a probability value of newly submitted physiological data in
global distribution, and marking low probability points as distribution abnormity; according to the method,
data continuity features and distribution features are integrated, a comprehensive abnormal
score of each
data point is calculated through a weighted scoring method,
score weights are dynamically adjusted in combination with personalized health portraits of patients, index deviations related to health risks of the patients are preferentially concerned, threshold values are set according to the comprehensive abnormal scores, and comprehensive abnormal data points are identified. According to the method, continuity features and distribution features are combined, an
anomaly detection strategy is dynamically adjusted through a weighted scoring method, and significant anomaly points are marked.