title: Statistical analysis of a low cost method for multiple disease prediction.
abstract: Early identification of individuals at risk for chronic diseases is of significant clinical value. Early detection provides the opportunity to slow the pace of a condition, and thus help individuals to improve or maintain their quality of life. Additionally, it can lessen the financial burden on health insurers and self-insured employers. As a solution to mitigate the rise in chronic conditions and related costs, an increasing number of employers have recently begun using wellness programs, which typically involve an annual health risk assessment. Unfortunately, these risk assessments have low detection capability, as they should be low-cost and hence rely on collecting relatively few basic biomarkers. Thus one may ask, how can we select a low-cost set of biomarkers that would be the most predictive of multiple chronic diseases? In this paper, we propose a statistical data-driven method to address this challenge by minimizing the number of biomarkers in the screening procedure while maximizing the predictive power over a broad spectrum of diseases. Our solution uses multi-task learning and group dimensionality reduction from machine learning and statistics. We provide empirical validation of the proposed solution using data from two different electronic medical records systems, with comparisons over a statistical benchmark.
authors.affiliation: 1 Graduate School of Business, Stanford University, Stanford, USA.|2 Department of Electrical Engineering, Stanford University, Stanford, USA.
authors.forename: Mohsen
authors.initials: M
authors.lastname: Bayati
authors.affiliation: 2 Department of Electrical Engineering, Stanford University, Stanford, USA.
authors.forename: Sonia
authors.initials: S
authors.lastname: Bhaskar
authors.affiliation: 2 Department of Electrical Engineering, Stanford University, Stanford, USA.|3 Department of Statistics, Stanford University, Stanford, USA.
authors.forename: Andrea
authors.initials: A
authors.lastname: Montanari
mesh_terms: D015415:Biomarkers; D002908:Chronic Disease / Q000639:trends*; D003365:Costs and Cost Analysis; D005544:Forecasting / Q000379:methods; D016015:Logistic Models; D015233:Models, Statistical
chemical_list: D015415:Biomarkers
keywords: Clinical disease prediction; feature selection; group regularization; multitask learning