TY - GEN
T1 - Diagnosis on Mild Cognitive Impairment Patients for Alzheimer Disease with Missing Data
AU - Gao, Fei
AU - Li, Jing
AU - Wu, Teresa
AU - Chen, Kewei
AU - Lure, Fleming
AU - Weidman, David
N1 - Funding Information:
ACKNOWLEDGEMENT Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health under Award Number R41AG053149. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Funding Information:
Data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI; Principal Investigator: Michael Weiner; NIH grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering (NIBIB), and through generous contributions from the following: Pfizer Inc., Wyeth Research, Bristol-Myers Squibb, Eli Lilly and Company, GlaxoSmithKline, Merck & Co. Inc., AstraZeneca AB, Novartis Pharmaceuticals Corporation, Alzheimer's Association, Eisai Global Clinical Development, Elan Corporation plc, Forest Laboratories, and the Institute for the Study of Aging, with participation from the U.S. Food and Drug Administration. Industry partnerships are coordinated through the Foundation for the National Institutes of Health. The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory of Neuro Imaging at the University of California, Los Angeles.
PY - 2017/9/8
Y1 - 2017/9/8
N2 - Mild cognitive impairment (MCI) is constructed as an intermediate stage between normal aging and Alzheimer disease (AD). Various clinical criteria have been developed to quantify the risk of MCI patients converting to AD. One risk assessment criterion in assisting clinical decision is based on the amount of cerebral amyloid measured with florbetapir-fluorine-18 positron emission tomography (18F-AV45-PET) imaging. However, PET imaging is not usually readily available. As a result, the advantages of these important imaging based biomarkers may not be fully utilized clinically. To tackle the problem where patients have these biomarkers missing, we propose to develop ensemble regression tree to estimate the biomarkers based on clinical and demographic features (Age, APOE status, cognitive test, etc.) and other imaging biomarkers such as MRI. The makeup dataset filled with these estimates are then used to develop a classification model to assess the risk of MCI patients converting to AD. Using dataset of 146 MCI patients from Alzheimer's disease neuroimaging initiative (ANDI), we conduct 16 sets of experiments with the missing ratios changing from 0.05 to 0.80 to test the performance of our proposed approach. The advantages of our model show well when the missing ratio ranges 0.2 to 0.6 with average 7.1% higher accuracy and 7.4% higher sensitivity comparing to the model without using the estimated fill-ins. This advantage diminishes as the missing ratio increases to 80% as expected.
AB - Mild cognitive impairment (MCI) is constructed as an intermediate stage between normal aging and Alzheimer disease (AD). Various clinical criteria have been developed to quantify the risk of MCI patients converting to AD. One risk assessment criterion in assisting clinical decision is based on the amount of cerebral amyloid measured with florbetapir-fluorine-18 positron emission tomography (18F-AV45-PET) imaging. However, PET imaging is not usually readily available. As a result, the advantages of these important imaging based biomarkers may not be fully utilized clinically. To tackle the problem where patients have these biomarkers missing, we propose to develop ensemble regression tree to estimate the biomarkers based on clinical and demographic features (Age, APOE status, cognitive test, etc.) and other imaging biomarkers such as MRI. The makeup dataset filled with these estimates are then used to develop a classification model to assess the risk of MCI patients converting to AD. Using dataset of 146 MCI patients from Alzheimer's disease neuroimaging initiative (ANDI), we conduct 16 sets of experiments with the missing ratios changing from 0.05 to 0.80 to test the performance of our proposed approach. The advantages of our model show well when the missing ratio ranges 0.2 to 0.6 with average 7.1% higher accuracy and 7.4% higher sensitivity comparing to the model without using the estimated fill-ins. This advantage diminishes as the missing ratio increases to 80% as expected.
KW - AD
KW - Ensemble
KW - MCI
KW - Missing data imputation
KW - Regression tree
KW - SVM
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UR - http://www.scopus.com/inward/citedby.url?scp=85032349458&partnerID=8YFLogxK
U2 - 10.1109/ICHI.2017.13
DO - 10.1109/ICHI.2017.13
M3 - Conference contribution
AN - SCOPUS:85032349458
T3 - Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017
SP - 547
EP - 551
BT - Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017
A2 - Cummins, Mollie
A2 - Facelli, Julio
A2 - Meixner, Gerrit
A2 - Giraud-Carrier, Christophe
A2 - Nakajima, Hiroshi
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Healthcare Informatics, ICHI 2017
Y2 - 23 August 2017 through 26 August 2017
ER -