Precision Health-Enabled Machine Learning to Identify Need for Wraparound Social Services Using Patient- and Population-Level Data Sets: Algorithm Development and Validation.

ABSTRACT: BACKGROUND:Emerging interest in precision health and the increasing availability of patient- and population-level data sets present considerable potential to enable analytical approaches to identify and mitigate the negative effects of social factors on health. These issues are not satisfactorily addressed in typical medical care encounters, and thus, opportunities to improve health outcomes, reduce costs, and improve coordination of care are not realized. Furthermore, methodological expertise on the use of varied patient- and population-level data sets and machine learning to predict need for supplemental services is limited. OBJECTIVE:The objective of this study was to leverage a comprehensive range of clinical, behavioral, social risk, and social determinants of health factors in order

SUBMITTER: Kasthurirathne SN 

PROVIDER: S-EPMC7380999 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature