Unified modeling of imputation, forecasting, and prediction for AD progression

Wonsik Jung; Ahmad Wisnu Mulyadi; Heung Il Suk

doi:10.1007/978-3-030-32251-9_19

Unified modeling of imputation, forecasting, and prediction for AD progression

Wonsik Jung, Ahmad Wisnu Mulyadi, Heung Il Suk

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
Editors	Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	168-176
Number of pages	9
ISBN (Print)	9783030322502
DOIs	https://doi.org/10.1007/978-3-030-32251-9_19
Publication status	Published - 2019
Event	22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: 2019 Oct 13 → 2019 Oct 17

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11767 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Country/Territory	China
City	Shenzhen
Period	19/10/13 → 19/10/17

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

Keywords

Alzheimer’s Disease
Deep learning
Disease Progression Modeling
Longitudinal data
Mild Cognitive Impairment
Missing value imputation
Recurrent neural networks

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-32251-9_19

Cite this

Jung, W., Mulyadi, A. W., & Suk, H. I. (2019). Unified modeling of imputation, forecasting, and prediction for AD progression. In D. Shen, P.-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (pp. 168-176). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11767 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-32251-9_19

Unified modeling of imputation, forecasting, and prediction for AD progression. / Jung, Wonsik; Mulyadi, Ahmad Wisnu; Suk, Heung Il.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. ed. / Dinggang Shen; Pew-Thian Yap; Tianming Liu; Terry M. Peters; Ali Khan; Lawrence H. Staib; Caroline Essert; Sean Zhou. Springer Science and Business Media Deutschland GmbH, 2019. p. 168-176 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11767 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jung, W, Mulyadi, AW & Suk, HI 2019, Unified modeling of imputation, forecasting, and prediction for AD progression. in D Shen, P-T Yap, T Liu, TM Peters, A Khan, LH Staib, C Essert & S Zhou (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11767 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 168-176, 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, China, 19/10/13. https://doi.org/10.1007/978-3-030-32251-9_19

Jung W, Mulyadi AW, Suk HI. Unified modeling of imputation, forecasting, and prediction for AD progression. In Shen D, Yap PT, Liu T, Peters TM, Khan A, Staib LH, Essert C, Zhou S, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2019. p. 168-176. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-32251-9_19

Jung, Wonsik ; Mulyadi, Ahmad Wisnu ; Suk, Heung Il. / Unified modeling of imputation, forecasting, and prediction for AD progression. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. editor / Dinggang Shen ; Pew-Thian Yap ; Tianming Liu ; Terry M. Peters ; Ali Khan ; Lawrence H. Staib ; Caroline Essert ; Sean Zhou. Springer Science and Business Media Deutschland GmbH, 2019. pp. 168-176 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{cf61db207d7845c6bf845892a273bbe1,

title = "Unified modeling of imputation, forecasting, and prediction for AD progression",

abstract = "In this paper, we propose a novel deep recurrent neural network as an Alzheimer{\textquoteright}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.",

keywords = "Alzheimer{\textquoteright}s Disease, Deep learning, Disease Progression Modeling, Longitudinal data, Mild Cognitive Impairment, Missing value imputation, Recurrent neural networks",

author = "Wonsik Jung and Mulyadi, {Ahmad Wisnu} and Suk, {Heung Il}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 ; Conference date: 13-10-2019 Through 17-10-2019",

year = "2019",

doi = "10.1007/978-3-030-32251-9_19",

language = "English",

isbn = "9783030322502",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "168--176",

editor = "Dinggang Shen and Pew-Thian Yap and Tianming Liu and Peters, {Terry M.} and Ali Khan and Staib, {Lawrence H.} and Caroline Essert and Sean Zhou",

booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings",

address = "Germany",

}

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 - 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 -

Unified modeling of imputation, forecasting, and prediction for AD progression

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this