title: A Longitudinal Support Vector Regression for Prediction of ALS Score.
abstract: Longitudinal studies play a key role in various fields, including epidemiology, clinical research, and genomic analysis. Currently, the most popular methods in longitudinal data analysis are model-driven regression approaches, which impose strong prior assumptions and are unable to scale to large problems in the manner of machine learning algorithms. In this work, we propose a novel longitudinal support vector regression (LSVR) algorithm that not only takes the advantage of one of the most popular machine learning methods, but also is able to model the temporal nature of longitudinal data by taking into account observational dependence within subjects. We test LSVR on publicly available data from theDREAM-Phil Bowen ALS Prediction Prize4Lifechallenge. Results suggest that LSVR is at a minimum competitive with favored machine learning methods and is able to outperform those methods in predicting ALS score one month in advance.
authors.affiliation: Center for Information Technology, National Institutes of Health, Bethesda, MD 20892-5624.
authors.forename: Wei
authors.initials: W
authors.lastname: Du
authors.affiliation: Center for Information Technology, National Institutes of Health, Bethesda, MD 20892-5624.
authors.forename: Huey
authors.initials: H
authors.lastname: Cheung
authors.affiliation: Intramural Research Program, National Institutes on Aging, Baltimore, MD 21224-6825.
authors.forename: Ilya
authors.initials: I
authors.lastname: Goldberg
authors.affiliation: Intramural Research Program, National Institutes on Aging, Baltimore, MD 21224-6825.
authors.forename: Madhav
authors.initials: M
authors.lastname: Thambisetty
authors.affiliation: Intramural Research Program, National Institutes on Aging, Baltimore, MD 21224-6825.
authors.forename: Kevin
authors.initials: K
authors.lastname: Becker
authors.affiliation: Center for Information Technology, National Institutes of Health, Bethesda, MD 20892-5624.
authors.forename: Calvin A
authors.initials: CA
authors.lastname: Johnson
keywords: ALS; longitudinal data; machine learning; support vector regression