Temporal modeling of deterioration patterns and clustering for disease prediction of ALS patients

Dan Halbersberg; Boaz Lerner

doi:10.1109/ICMLA.2019.00019

Temporal modeling of deterioration patterns and clustering for disease prediction of ALS patients

Dan Halbersberg, Boaz Lerner

Department of Industrial Engineering and Management

Ben-Gurion University of the Negev

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

Abstract

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease, lasting from the day of onset until death. Factors such as the progression rate and pattern of the disease vary greatly among patients, making it difficult to achieve accurate predictions about ALS. To accurately predict ALS disease state and deterioration, we propose a novel approach that combines: a) sequence clustering based on dynamic time warping for separation among patients with diverse ALS deterioration patterns, b) sequential pattern mining for discovery of deterioration changes that patients of the same type may have in common, and c) deterioration-based patient next-state prediction. Using a clinical dataset, we demonstrate the advantage of the proposed approach in terms of classification accuracy and deterioration detection compared to other classification methods and temporal models such as long short-term memory.

Original language	American English
Title of host publication	Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
Editors	M. Arif Wani, Taghi M. Khoshgoftaar, Dingding Wang, Huanjing Wang, Naeem Seliya
Pages	62-68
Number of pages	7
ISBN (Electronic)	9781728145495
DOIs	https://doi.org/10.1109/ICMLA.2019.00019
State	Published - 1 Dec 2019
Event	18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019 - Boca Raton, United States Duration: 16 Dec 2019 → 19 Dec 2019

Conference

Conference	18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
Country/Territory	United States
City	Boca Raton
Period	16/12/19 → 19/12/19

Keywords

Amyotrophic lateral sclerosis (ALS)
Classification
Deterioration
Prediction
Sequence clustering
Sequential pattern mining
Temporal models

All Science Journal Classification (ASJC) codes

Strategy and Management
Artificial Intelligence
Computer Science Applications
Decision Sciences (miscellaneous)
Signal Processing
Media Technology

Access to Document

10.1109/ICMLA.2019.00019

Cite this

Halbersberg, D., & Lerner, B. (2019). Temporal modeling of deterioration patterns and clustering for disease prediction of ALS patients. In M. A. Wani, T. M. Khoshgoftaar, D. Wang, H. Wang, & N. Seliya (Eds.), Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019 (pp. 62-68). Article 8999319 https://doi.org/10.1109/ICMLA.2019.00019

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title = "Temporal modeling of deterioration patterns and clustering for disease prediction of ALS patients",

abstract = "Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease, lasting from the day of onset until death. Factors such as the progression rate and pattern of the disease vary greatly among patients, making it difficult to achieve accurate predictions about ALS. To accurately predict ALS disease state and deterioration, we propose a novel approach that combines: a) sequence clustering based on dynamic time warping for separation among patients with diverse ALS deterioration patterns, b) sequential pattern mining for discovery of deterioration changes that patients of the same type may have in common, and c) deterioration-based patient next-state prediction. Using a clinical dataset, we demonstrate the advantage of the proposed approach in terms of classification accuracy and deterioration detection compared to other classification methods and temporal models such as long short-term memory.",

keywords = "Amyotrophic lateral sclerosis (ALS), Classification, Deterioration, Prediction, Sequence clustering, Sequential pattern mining, Temporal models",

author = "Dan Halbersberg and Boaz Lerner",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019 ; Conference date: 16-12-2019 Through 19-12-2019",

year = "2019",

month = dec,

day = "1",

doi = "10.1109/ICMLA.2019.00019",

language = "American English",

pages = "62--68",

editor = "Wani, {M. Arif} and Khoshgoftaar, {Taghi M.} and Dingding Wang and Huanjing Wang and Naeem Seliya",

booktitle = "Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019",

}

Halbersberg, D & Lerner, B 2019, Temporal modeling of deterioration patterns and clustering for disease prediction of ALS patients. in MA Wani, TM Khoshgoftaar, D Wang, H Wang & N Seliya (eds), Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019., 8999319, pp. 62-68, 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019, Boca Raton, United States, 16/12/19. https://doi.org/10.1109/ICMLA.2019.00019

Temporal modeling of deterioration patterns and clustering for disease prediction of ALS patients. / Halbersberg, Dan; Lerner, Boaz.
Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019. ed. / M. Arif Wani; Taghi M. Khoshgoftaar; Dingding Wang; Huanjing Wang; Naeem Seliya. 2019. p. 62-68 8999319.

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

TY - GEN

T1 - Temporal modeling of deterioration patterns and clustering for disease prediction of ALS patients

AU - Halbersberg, Dan

AU - Lerner, Boaz

PY - 2019/12/1

Y1 - 2019/12/1

N2 - Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease, lasting from the day of onset until death. Factors such as the progression rate and pattern of the disease vary greatly among patients, making it difficult to achieve accurate predictions about ALS. To accurately predict ALS disease state and deterioration, we propose a novel approach that combines: a) sequence clustering based on dynamic time warping for separation among patients with diverse ALS deterioration patterns, b) sequential pattern mining for discovery of deterioration changes that patients of the same type may have in common, and c) deterioration-based patient next-state prediction. Using a clinical dataset, we demonstrate the advantage of the proposed approach in terms of classification accuracy and deterioration detection compared to other classification methods and temporal models such as long short-term memory.

AB - Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease, lasting from the day of onset until death. Factors such as the progression rate and pattern of the disease vary greatly among patients, making it difficult to achieve accurate predictions about ALS. To accurately predict ALS disease state and deterioration, we propose a novel approach that combines: a) sequence clustering based on dynamic time warping for separation among patients with diverse ALS deterioration patterns, b) sequential pattern mining for discovery of deterioration changes that patients of the same type may have in common, and c) deterioration-based patient next-state prediction. Using a clinical dataset, we demonstrate the advantage of the proposed approach in terms of classification accuracy and deterioration detection compared to other classification methods and temporal models such as long short-term memory.

KW - Amyotrophic lateral sclerosis (ALS)

KW - Classification

KW - Deterioration

KW - Prediction

KW - Sequence clustering

KW - Sequential pattern mining

KW - Temporal models

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U2 - 10.1109/ICMLA.2019.00019

DO - 10.1109/ICMLA.2019.00019

M3 - Conference contribution

SP - 62

EP - 68

BT - Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019

A2 - Wani, M. Arif

A2 - Khoshgoftaar, Taghi M.

A2 - Wang, Dingding

A2 - Wang, Huanjing

A2 - Seliya, Naeem

T2 - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019

Y2 - 16 December 2019 through 19 December 2019

ER -

Temporal modeling of deterioration patterns and clustering for disease prediction of ALS patients

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Conference

Keywords

All Science Journal Classification (ASJC) codes

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