TY - GEN
T1 - Predictive modeling of the severity/progression of Alzheimer's diseases
AU - Qiu, Robin G.
AU - Qiu, Jason L.
AU - Badr, Youakim
N1 - Funding Information:
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).
Funding Information:
ACKNOWLEDGMENT This work was done with great support and help from the Big Data Lab at Penn State and LIRIS at INSA-Lyon. The project of Big Data Platform (Massive Data) for Proactive Analyses of Behaviors of Users in Urban Worlds is financially supported by the Rhône-Alpes Region, France (Badr and Qiu, 2016-17). This project was also partially supported by IBM Faculty Award (RDP-Qiu2016). J. Qiu’s MMSE Modeling won the “Excellence in Student Science Research Award, Merck and Company Honorable Mention Award”.
Funding Information:
This work was done with great support and help from the Big Data Lab at Penn State and LIRIS at INSA-Lyon. The project of Big Data Platform (Massive Data) for Proactive Analyses of Behaviors of Users in Urban Worlds is financially supported by the Rhône-Alpes Region, France (Badr and Qiu, 2016-17). This project was also partially supported by IBM Faculty Award (RDP-Qiu2016).
Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/19
Y1 - 2017/10/19
N2 - Alzheimer's disease (AD) cannot be cured or slowed down with today's medication. Scientific studies have found that 1) the progression of AD is highly correlated to a cognition decline, 2) cognition drop is a precursor of Alzheimer's disease, and 3) making lifestyle changes and training the brain can slow down AD progression. This project aims to develop a predictive model to know the progression of an AD patient. Factors that influence the disease's severity and progression are determined, which would help facilitate developing a set of personalized care instructions to guide individuals in making the necessary lifestyle choices to retain or rejuvenate their brain's cognitive ability. Ultimately, the developed model can be potentially incorporated into a convenient self-diagnostic tool for the public to use at home.
AB - Alzheimer's disease (AD) cannot be cured or slowed down with today's medication. Scientific studies have found that 1) the progression of AD is highly correlated to a cognition decline, 2) cognition drop is a precursor of Alzheimer's disease, and 3) making lifestyle changes and training the brain can slow down AD progression. This project aims to develop a predictive model to know the progression of an AD patient. Factors that influence the disease's severity and progression are determined, which would help facilitate developing a set of personalized care instructions to guide individuals in making the necessary lifestyle choices to retain or rejuvenate their brain's cognitive ability. Ultimately, the developed model can be potentially incorporated into a convenient self-diagnostic tool for the public to use at home.
UR - http://www.scopus.com/inward/record.url?scp=85040128285&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040128285&partnerID=8YFLogxK
U2 - 10.1109/GSIS.2017.8077739
DO - 10.1109/GSIS.2017.8077739
M3 - Conference contribution
AN - SCOPUS:85040128285
T3 - 2017 IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2017
SP - 400
EP - 403
BT - 2017 IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2017
Y2 - 8 August 2017 through 11 August 2017
ER -