Intra-person multi-task learning method for chronic-disease prediction.

ABSTRACT: In the medical field, various clinical information has been accumulated to help clinicians provide personalized medicine and make better diagnoses. As chronic diseases share similar characteristics, it is possible to predict multiple chronic diseases using the accumulated data of each patient. Thus, we propose an intra-person multi-task learning framework that jointly predicts the status of correlated chronic diseases and improves the model performance. Because chronic diseases occur over a long period and are affected by various factors, we considered features related to each chronic disease and the temporal relationship of the time-series data for accurate prediction. The study was carried out in three stages: (1) data preprocessing and feature selection using bidirectional recurrent imp

SUBMITTER: Kim G 

PROVIDER: S-EPMC9851106 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature