Reinforced risk prediction with budget constraint using irregularly measured data from electronic health records.

ABSTRACT: Uncontrolled glycated hemoglobin (HbA1c) levels are associated with adverse events among complex diabetic patients. These adverse events present serious health risks to affected patients and are associated with significant financial costs. Thus, a high-quality predictive model that could identify high-risk patients so as to inform preventative treatment has the potential to improve patient outcomes while reducing healthcare costs. Because the biomarker information needed to predict risk is costly and burdensome, it is desirable that such a model collect only as much information as is needed on each patient so as to render an accurate prediction. We propose a sequential predictive model that uses accumulating patient longitudinal data to classify patients as: high-risk, low-risk, or uncerta

SUBMITTER: Pan Y 

PROVIDER: S-EPMC10274334 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature