A0961
Title: Modeling multiple-criterion diagnoses by heterogeneous-instance logistic regression
Authors: Chun-Hao Yang - National Taiwan University (Taiwan) [presenting]
Abstract: Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD) that causes a significant burden in caregiving and medical costs. Clinically, the diagnosis of MCI is determined by the impairment statuses of five cognitive domains. If one of these cognitive domains is impaired, the patient is diagnosed with MCI, and if two out of the five domains are impaired, the patient is diagnosed with AD. This diagnostic procedure relates MCI/AD status modeling to multiple-instance learning, where each domain resembles an instance. However, traditional multiple-instance learning assumes common predictors among instances, but in this case, each domain is associated with different predictors. The aim is to generalize the multiple-instance logistic regression to accommodate the heterogeneity in predictors among different instances. The proposed model is dubbed heterogeneous-instance logistic regression. Two variants of the proposed model for the MCI and AD diagnoses are also derived. The proposed model is validated in terms of its estimation accuracy, latent status prediction, and robustness via extensive simulation studies. Finally, the national Alzheimer's coordinating center-uniform data set is analyzed using the proposed model, demonstrating its potential.
