TY - GEN
T1 - Early Detection Models for Persons with Probable Alzheimer's Disease with Deep Learning
AU - Wang, Tingyan
AU - Qiu, Jason L.
AU - Qiu, Robin G.
AU - Yu, Ming
N1 - Funding Information:
ACKNOWLEDGMENT The dataset was support by NACC (Proposal ID #776). The NACC database is funded by NIA/NIH Grant U01 AG016976. NACC data are contributed by the NIAfunded ADCs: P30 AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P50 AG008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P50 AG047266 (PI Todd Golde, MD, PhD), P30 AG010133 (PI Andrew Saykin, PsyD), P50 AG005146 (PI Marilyn Albert, PhD), P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG016574 (PI Ronald Petersen, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Steven Ferris, PhD), P30 AG013854 (PI M. Marsel Mesulam, MD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P50 AG016570 (PI MarieFrancoise Chesselet, MD, PhD), P50 AG005131 (PI Douglas Galasko, MD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerdlow, MD) , P30 AG028383 (PI Linda Van Eldik, PhD), P30 AG010124 (PI John Trojanowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P50 AG005136 (PI Thomas Montine, MD, PhD), P50 AG033514 (PI Sanjay Asthana, MD, FRCP), P50 AG005681 (PI John Morris, MD), and P50 AG047270 (PI Stephen Strittmatter, MD, PhD).
Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/20
Y1 - 2018/9/20
N2 - Unfortunately, Alzheimer's disease (AD) cannot be cured or slowed with today's medication. Scientific studies have revealed that 1) a cognition drop is a precursor of AD, 2) the progression of AD is highly correlated to cognition decline, and 3) AD early detection and intervention becomes increasingly clear to be the best choice of improving quality of life for persons with probable AD as of today. This project aims to improve the predictive model we developed earlier by focusing on AD early detection. We present how recurrent neuron network (RNN) models can be adopted to AD early detection modeling (AD-EDM). Compared to models built from traditional approaches such as neuron networks, Bayesian networks, and tree-based algorithms, we demonstrate the prediction accuracy of RNN AD-EDM increases substantially.
AB - Unfortunately, Alzheimer's disease (AD) cannot be cured or slowed with today's medication. Scientific studies have revealed that 1) a cognition drop is a precursor of AD, 2) the progression of AD is highly correlated to cognition decline, and 3) AD early detection and intervention becomes increasingly clear to be the best choice of improving quality of life for persons with probable AD as of today. This project aims to improve the predictive model we developed earlier by focusing on AD early detection. We present how recurrent neuron network (RNN) models can be adopted to AD early detection modeling (AD-EDM). Compared to models built from traditional approaches such as neuron networks, Bayesian networks, and tree-based algorithms, we demonstrate the prediction accuracy of RNN AD-EDM increases substantially.
UR - https://www.scopus.com/pages/publications/85055703723
UR - https://www.scopus.com/pages/publications/85055703723#tab=citedBy
U2 - 10.1109/IMCEC.2018.8469380
DO - 10.1109/IMCEC.2018.8469380
M3 - Conference contribution
AN - SCOPUS:85055703723
T3 - Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018
SP - 2089
EP - 2092
BT - Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018
A2 - Xu, Bing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018
Y2 - 25 May 2018 through 27 May 2018
ER -