학술논문

Initial Results in Alzheimer's Disease Progression Modeling Using Imputed Health State Profiles
Document Type
Conference
Source
2016 International Conference on Computational Science and Computational Intelligence (CSCI) CSCI Computational Science and Computational Intelligence (CSCI), 2016 International Conference on. :7-12 Dec, 2016
Subject
Computing and Processing
Databases
Alzheimer's disease
Medical diagnostic imaging
History
Magnetic resonance imaging
Alzheimer's Disease
progression modeling
imputation
data analytics
Language
Abstract
This paper describes an initial step in developing a set of quasi-patient profiles, each representing a complete longitudinal medical history of Alzheimer's disease (AD) – from normal health to the clinical emergence of the disease and beyond. Quasi-patient is the term given to a unified medical record created through the optimal imputation of individual records, and the guided merger and completion of multiple patient records from the Alzheimer's Disease Neuroimaging Initiative (ADNI). In the present paper, imputation strategies and boosted ensemble decision trees are used to characterize the health states of patients in the ADNI database which consistently yield year-by-year health state predictions of 80% or greater accuracy. In addition, relative to ordinarily ignoring missing medical records in a patient's history, imputation and state estimation guided by globally-optimal decision criteria resulted in an accuracy increase from 76.1% to 81.9%.