TY - GEN
T1 - Robust prediction of cognitive test scores in Alzheimer's patients
AU - Izquierdo, W.
AU - Martin, H.
AU - Cabrerizo, M.
AU - Barreto, A.
AU - Andrian, J.
AU - Rishe, N.
AU - Gonzalez-Arias, S.
AU - Loewenstein, D.
AU - Duara, R.
AU - Adjouadi, M.
N1 - Funding Information:
We acknowledge the critical support provided by the National Science Foundation under grants: CNS-1532061, CNS-1551221, CNS-1642193, CNS-0959985, HRD-0833093, IIP 1338922, and CNS-1429345. The generous support of the Ware Foundation is greatly appreciated. Harold Martin is supported through the NSF Graduate Fellowship Program (GRFP) DGE-1610348.
Funding Information:
We acknowledge the critical support provided by the National Science Foundation under grants: CNS-1532061, CNS- 1551221, CNS-1642193, CNS-0959985, HRD- 0833093, IIP 1338922, and CNS-1429345. The generous support of the Ware Foundation is greatly appreciated. Harold Martin is supported through the NSF Graduate Fellowship Program (GRFP) DGE-1610348.
Funding Information:
Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Predicting future cognitive status from current and past scores on objective cognitive tests and imaging measures would be useful in diagnosing Alzheimer's disease (AD) and to assess the progression of the disease. We used stochastic gradient boosting of decision trees on over 1,141 individuals whose clinical and imaging studies were available from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. The proposed method outperformed all the algorithms tested in all five cognitive scores (MMSE, CDRS, RAVLT, ADAS11 and ADAS13), outranking all other state-of-the-art algorithms in terms of both Pearson's correlation coefficient and root mean square error. All correlation measures between predicted and actual cognitive scores were higher than 0.9. Given the large number of subjects included in this study, all correlations were statistically significant. For the subset of MCI patients, we compared the proposed method with state of the art algorithms. Here, the proposed method outperformed all the algorithms tested in all five cognitive scores.
AB - Predicting future cognitive status from current and past scores on objective cognitive tests and imaging measures would be useful in diagnosing Alzheimer's disease (AD) and to assess the progression of the disease. We used stochastic gradient boosting of decision trees on over 1,141 individuals whose clinical and imaging studies were available from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. The proposed method outperformed all the algorithms tested in all five cognitive scores (MMSE, CDRS, RAVLT, ADAS11 and ADAS13), outranking all other state-of-the-art algorithms in terms of both Pearson's correlation coefficient and root mean square error. All correlation measures between predicted and actual cognitive scores were higher than 0.9. Given the large number of subjects included in this study, all correlations were statistically significant. For the subset of MCI patients, we compared the proposed method with state of the art algorithms. Here, the proposed method outperformed all the algorithms tested in all five cognitive scores.
UR - http://www.scopus.com/inward/record.url?scp=85050564093&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050564093&partnerID=8YFLogxK
U2 - 10.1109/SPMB.2017.8257059
DO - 10.1109/SPMB.2017.8257059
M3 - Conference contribution
AN - SCOPUS:85050564093
T3 - 2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017 - Proceedings
SP - 1
EP - 7
BT - 2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017
Y2 - 2 December 2017
ER -