TY - GEN
T1 - A Machine Learning Approach to Understanding the Progression of Alzheimer’s Disease
AU - Peddinti, Vineeta
AU - Qiu, Robin
N1 - Funding Information:
Acknowledgments 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:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Alzheimer’s is a type of dementia that progressively destroys memory cells and other important mental functions. It is a degenerative process involving different stages and it is critical to predict the progression for developing lifestyle change guidance or treatments to slow it down given that there is no cure. Although there has been lot of research going on for developing prediction models using deep learning and machine learning techniques focusing on the severity staging prediction of Alzheimer’s Disease (AD), this paper investigates mainly on the time factor for progressing to the next stage. A machine learning model is applied to analyzing the factors contributing to the progression using the clinical and neuropsychological data provided by the National Alzheimer’s Coordinating Center. In this study, given the metrics to assess the AD stage and the clinical diagnoses of the patient’s historical visits, the number of months it takes for a patient to progress to the next stage is predicted. The most important factors that contribute to the progression of the disease to the next stage are uncovered, aimed at helping AD patients weaken their disease progression.
AB - Alzheimer’s is a type of dementia that progressively destroys memory cells and other important mental functions. It is a degenerative process involving different stages and it is critical to predict the progression for developing lifestyle change guidance or treatments to slow it down given that there is no cure. Although there has been lot of research going on for developing prediction models using deep learning and machine learning techniques focusing on the severity staging prediction of Alzheimer’s Disease (AD), this paper investigates mainly on the time factor for progressing to the next stage. A machine learning model is applied to analyzing the factors contributing to the progression using the clinical and neuropsychological data provided by the National Alzheimer’s Coordinating Center. In this study, given the metrics to assess the AD stage and the clinical diagnoses of the patient’s historical visits, the number of months it takes for a patient to progress to the next stage is predicted. The most important factors that contribute to the progression of the disease to the next stage are uncovered, aimed at helping AD patients weaken their disease progression.
UR - http://www.scopus.com/inward/record.url?scp=85126245321&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85126245321&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-75166-1_28
DO - 10.1007/978-3-030-75166-1_28
M3 - Conference contribution
AN - SCOPUS:85126245321
SN - 9783030751654
T3 - Springer Proceedings in Business and Economics
SP - 381
EP - 392
BT - AI and Analytics for Public Health - Proceedings of the 2020 INFORMS International Conference on Service Science
A2 - Yang, Hui
A2 - Qiu, Robin
A2 - Chen, Weiwei
PB - Springer Science and Business Media B.V.
T2 - INFORMS International Conference on Service Science, ICSS 2020
Y2 - 19 December 2020 through 21 December 2020
ER -