@inproceedings{988531d177b14cacb9a2e682ab61b02e,
title = "Prediction of Incident Dementia Using Patient Temporal Health Status",
abstract = "Dementia is one of the most prevalent health problems in the aging population. Despite the significant number of people affected, dementia diagnoses are often significantly delayed, missing opportunities to maximize life quality. Early identification of older adults at high risk for dementia may help to maximize current quality of life and to improve planning for future health needs in dementia patients. However, most existing risk prediction models predominantly use static variables, not considering temporal patterns of health status. This study used an attention-based time-aware model to predict incident dementia that incorporated longitudinal temporal health conditions. The predictive performance of the time-aware model was compared with three traditional models using static variables and demonstrated higher predictive power.",
keywords = "deep learning, dementia, machine learning",
author = "Sunyang Fu and Ibrahim, {Omar A.} and Yanshan Wang and Maria Vassilaki and Petersen, {Ronald C.} and Mielke, {Michelle M.} and {St Sauver}, Jennifer and Sunghwan Sohn",
note = "Publisher Copyright: {\textcopyright} 2022 International Medical Informatics Association (IMIA) and IOS Press.; 18th World Congress on Medical and Health Informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021 ; Conference date: 02-10-2021 Through 04-10-2021",
year = "2022",
month = jun,
day = "6",
doi = "10.3233/SHTI220180",
language = "English (US)",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "757--761",
editor = "Paula Otero and Philip Scott and Martin, {Susan Z.} and Elaine Huesing",
booktitle = "MEDINFO 2021",
address = "Netherlands",
}