An Application of Heterogeneous Mixture Learning for Mild Cognitive Impairment Subtyping

Masataka Kikuchi

[AP3-E1-1-02] An Application of Heterogeneous Mixture Learning for Mild Cognitive Impairment Subtyping

*Masataka Kikuchi¹, Kaori Kobayashi^1,2, Kensaku Kasuga³, Akinori Miyashita³, Takeshi Ikeuchi³, Eiji Yumoto⁴, Yasuto Fushimi², Toshihiro Takeda⁵, Shirou Manabe⁵, Kenichi Kamijo², Yasushi Matsumura⁵ (1. Department of Genome Informatics, Graduate School of Medicine, Osaka University, Japan, 2. Medical Solutions Division, NEC Corporation, Japan, 3. Department of Molecular Genetics, Brain Research Institute, Niigata University, Japan, 4. Biometrics Research Laboratories, NEC Corporation, Japan, 5. Department of Medical Informatics, Graduate School of Medicine, Osaka University, Japan)

Alzheimer Disease, Mild Cognitive Impairment, Decision Trees

Mild cognitive impairment (MCI) is known as a group at high risk of conversion to dementia, including Alzheimer's disease (AD). Individuals with MCI show heterogeneity in patterns of pathology, and do not always convert to AD. Detailed subtyping for MCI and accurate prediction of the patients who convert to AD may allow for new trial designs and may enable evaluation of the efficacy of a drug with a small number of patients during clinical trials. In this study, we applied the heterogeneous mixture learning (HML) method to identify subtypes of MCI. As a result, we identified eight subtypes of MCI using the HML approach and categorized into three groups in terms of AD conversion. The identification of these subtypes revealed varying conversion rates to AD, as well as differing levels of biological features.

第40回医療情報学連合大会（第21回日本医療情報学会学術大会）

[AP3-E1-1-02] An Application of Heterogeneous Mixture Learning for Mild Cognitive Impairment Subtyping