Title | Multiple kernel learning with random effects for predicting longitudinal outcomes and data integration. |
Publication Type | Journal Article |
Year of Publication | 2015 |
Authors | Chen, Tianle, Donglin Zeng, and Yuanjia Wang |
Journal | Biometrics |
Volume | 71 |
Issue | 4 |
Pagination | 918-28 |
Date Published | 2015 Dec |
ISSN | 1541-0420 |
Keywords | Algorithms, Alzheimer Disease, Biometry, Computer Simulation, Disease Progression, Humans, Huntington Disease, Longitudinal Studies, Machine Learning, Models, Statistical, Statistics, Nonparametric, Support Vector Machine |
Abstract | Predicting disease risk and progression is one of the main goals in many clinical research studies. Cohort studies on the natural history and etiology of chronic diseases span years and data are collected at multiple visits. Although, kernel-based statistical learning methods are proven to be powerful for a wide range of disease prediction problems, these methods are only well studied for independent data, but not for longitudinal data. It is thus important to develop time-sensitive prediction rules that make use of the longitudinal nature of the data. In this paper, we develop a novel statistical learning method for longitudinal data by introducing subject-specific short-term and long-term latent effects through a designed kernel to account for within-subject correlation of longitudinal measurements. Since the presence of multiple sources of data is increasingly common, we embed our method in a multiple kernel learning framework and propose a regularized multiple kernel statistical learning with random effects to construct effective nonparametric prediction rules. Our method allows easy integration of various heterogeneous data sources and takes advantage of correlation among longitudinal measures to increase prediction power. We use different kernels for each data source taking advantage of the distinctive feature of each data modality, and then optimally combine data across modalities. We apply the developed methods to two large epidemiological studies, one on Huntington's disease and the other on Alzheimer's Disease (Alzheimer's Disease Neuroimaging Initiative, ADNI) where we explore a unique opportunity to combine imaging and genetic data to study prediction of mild cognitive impairment, and show a substantial gain in performance while accounting for the longitudinal aspect of the data. |
DOI | 10.1111/biom.12343 |
Alternate Journal | Biometrics |
Original Publication | Multiple kernel learning with random effects for predicting longitudinal outcomes and data integration. |
PubMed ID | 26177419 |
PubMed Central ID | PMC4713389 |
Grant List | R01 CA082659 / CA / NCI NIH HHS / United States U01 AG024904 / AG / NIA NIH HHS / United States R01 GM047845 / GM / NIGMS NIH HHS / United States NS073671 / NS / NINDS NIH HHS / United States R01 NS073671 / NS / NINDS NIH HHS / United States NS082062 / NS / NINDS NIH HHS / United States UL1 RR025747 / RR / NCRR NIH HHS / United States P01CA142538 / CA / NCI NIH HHS / United States P01 CA142538 / CA / NCI NIH HHS / United States NUL1 RR025747 / RR / NCRR NIH HHS / United States U01 NS082062 / NS / NINDS NIH HHS / United States |
Multiple kernel learning with random effects for predicting longitudinal outcomes and data integration.
Project: