@inproceedings{d87b300d8645411fb0f84d6a7f833cc4,
title = "Feasibility Study of a Machine Learning Approach to Predict Dementia Progression",
abstract = "We conducted a feasibility study of machine-learning to predict progression of cognitive impairment to Alzheimer's disease (AD) among individuals enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our approach uses diverse participant information including genetic, imaging, biomarker, and neuropsychological data to predict transition to dementia in three clinical scenarios: short-term prediction (half or one year) based on a single assessment (simulating a {"}new patient{"} visit), short-term prediction based on information from two time points (simulating a {"}follow up{"} visit), and long-term (multiple years) prediction (simulating ongoing follow-up with repeated opportunities for assessment).",
keywords = "data mining, dementia progression, machine learning",
author = "Chi, {Chih Lin} and Wonsuk Oh and Soo Borson",
year = "2015",
month = dec,
day = "8",
doi = "10.1109/ICHI.2015.68",
language = "English (US)",
series = "Proceedings - 2015 IEEE International Conference on Healthcare Informatics, ICHI 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Wai-Tat Fu and Prabhakaran Balakrishnan and Sanda Harabagiu and Fei Wang and Jaideep Srivatsava",
booktitle = "Proceedings - 2015 IEEE International Conference on Healthcare Informatics, ICHI 2015",
note = "3rd IEEE International Conference on Healthcare Informatics, ICHI 2015 ; Conference date: 21-10-2015 Through 23-10-2015",
}