학술논문
Simultaneous spatial smoothing and outlier detection using penalized regression, with application to childhood obesity surveillance from electronic health records.
Document Type
Academic Journal
Author
Choi YG; Department of Statistics, Sookmyung Women's University, Seoul, South Korea.; Hanrahan LP; Department of Family Medicine, and Community Health, University of Wisconsin-Madison, Madison, Wisconsin.; Norton D; Department of Biostatistics, and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin.; Zhao YQ; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington.
Source
Publisher: Biometric Society Country of Publication: United States NLM ID: 0370625 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1541-0420 (Electronic) Linking ISSN: 0006341X NLM ISO Abbreviation: Biometrics Subsets: MEDLINE
Subject
Language
English
Abstract
Electronic health records (EHRs) have become a platform for data-driven granular-level surveillance in recent years. In this paper, we make use of EHRs for early prevention of childhood obesity. The proposed method simultaneously provides smooth disease mapping and outlier information for obesity prevalence that are useful for raising public awareness and facilitating targeted intervention. More precisely, we consider a penalized multilevel generalized linear model. We decompose regional contribution into smooth and sparse signals, which are automatically identified by a combination of fusion and sparse penalties imposed on the likelihood function. In addition, we weigh the proposed likelihood to account for the missingness and potential nonrepresentativeness arising from the EHR data. We develop a novel alternating minimization algorithm, which is computationally efficient, easy to implement, and guarantees convergence. Simulation studies demonstrate superior performance of the proposed method. Finally, we apply our method to the University of Wisconsin Population Health Information Exchange database.
(© 2020 The International Biometric Society.)
(© 2020 The International Biometric Society.)