TY - GEN
T1 - Unified modeling of imputation, forecasting, and prediction for AD progression
AU - Jung, Wonsik
AU - Mulyadi, Ahmad Wisnu
AU - Suk, Heung Il
N1 - Funding Information:
Acknowledgement. This work was supported by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-01779, A machine learning and statistical inference framework for explainable artificial intelligence). According to ADNI’s data use agreement (https://ida.loni.usc.edu/collaboration/access/appLicense.jsp).
Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - In this paper, we propose a novel deep recurrent neural network as an Alzheimer’s Disease (AD) progression model, capable of jointly conducting tasks of missing values imputation, phenotypic measurements forecast, and clinical state prediction of a subject based on his/her longitudinal imaging biomarkers. Unlike the existing methods that mostly ignore missing values or impute them by means of an independent statistical model before training a disease progression model, we devise a unified recurrent network architecture for jointly performing missing values imputation, biomarker values forecast, and clinical state prediction from the longitudinal data. For these tasks to be handled in a unified framework, we also define an objective function that can be efficiently optimized by means of stochastic gradient descent in an end-to-end manner. We validated the effectiveness of our proposed method by comparing with the comparative methods over the TADPOLE challenge cohort.
AB - In this paper, we propose a novel deep recurrent neural network as an Alzheimer’s Disease (AD) progression model, capable of jointly conducting tasks of missing values imputation, phenotypic measurements forecast, and clinical state prediction of a subject based on his/her longitudinal imaging biomarkers. Unlike the existing methods that mostly ignore missing values or impute them by means of an independent statistical model before training a disease progression model, we devise a unified recurrent network architecture for jointly performing missing values imputation, biomarker values forecast, and clinical state prediction from the longitudinal data. For these tasks to be handled in a unified framework, we also define an objective function that can be efficiently optimized by means of stochastic gradient descent in an end-to-end manner. We validated the effectiveness of our proposed method by comparing with the comparative methods over the TADPOLE challenge cohort.
KW - Alzheimer’s Disease
KW - Deep learning
KW - Disease Progression Modeling
KW - Longitudinal data
KW - Mild Cognitive Impairment
KW - Missing value imputation
KW - Recurrent neural networks
UR - http://www.scopus.com/inward/record.url?scp=85075641859&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32251-9_19
DO - 10.1007/978-3-030-32251-9_19
M3 - Conference contribution
AN - SCOPUS:85075641859
SN - 9783030322502
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 168
EP - 176
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
A2 - Shen, Dinggang
A2 - Yap, Pew-Thian
A2 - Liu, Tianming
A2 - Peters, Terry M.
A2 - Khan, Ali
A2 - Staib, Lawrence H.
A2 - Essert, Caroline
A2 - Zhou, Sean
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 17 October 2019
ER -