TY - GEN
T1 - TATC
T2 - 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018
AU - Li, Jia
AU - Lu, Zhihui
AU - Rong, Yu
AU - Kwok, Timothy
AU - Meng, Helen
AU - Cheng, Hong
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/7/19
Y1 - 2018/7/19
N2 - With the increase of elderly population, Alzheimer's Disease (AD), as the most common cause of dementia among the elderly, is affecting more and more senior people. It is crucial for a patient to receive accurate and timely diagnosis of AD. Current diagnosis relies on doctors' experience and clinical test, which, unfortunately, may not be performed until noticeable AD symptoms are developed. In this work, we present our novel solution named time-aware TICC and CNN (TATC), for predicting AD from actigraphy data. TATC is a multivariate time series classification method using a neural attention-based deep learning approach. It not only performs accurate prediction of AD risk, but also generates meaningful interpretation of daily behavior pattern of subjects. TATC provides an automatic, low-cost solution for continuously monitoring the change of physical activity of subjects in daily living environment. We believe the future deployment of TATC can benefit both doctors and patients in early detection of potential AD risk.
AB - With the increase of elderly population, Alzheimer's Disease (AD), as the most common cause of dementia among the elderly, is affecting more and more senior people. It is crucial for a patient to receive accurate and timely diagnosis of AD. Current diagnosis relies on doctors' experience and clinical test, which, unfortunately, may not be performed until noticeable AD symptoms are developed. In this work, we present our novel solution named time-aware TICC and CNN (TATC), for predicting AD from actigraphy data. TATC is a multivariate time series classification method using a neural attention-based deep learning approach. It not only performs accurate prediction of AD risk, but also generates meaningful interpretation of daily behavior pattern of subjects. TATC provides an automatic, low-cost solution for continuously monitoring the change of physical activity of subjects in daily living environment. We believe the future deployment of TATC can benefit both doctors and patients in early detection of potential AD risk.
KW - Actigraphy data
KW - Alzheimer's Disease
KW - Attention
KW - Circadian rhythm
UR - http://www.scopus.com/inward/record.url?scp=85051480032&partnerID=8YFLogxK
U2 - 10.1145/3219819.3219831
DO - 10.1145/3219819.3219831
M3 - Conference article published in proceeding or book
AN - SCOPUS:85051480032
SN - 9781450355520
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 509
EP - 518
BT - KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
Y2 - 19 August 2018 through 23 August 2018
ER -