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

Dan Halbersberg, Boaz Lerner

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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 languageEnglish
Title of host publicationProceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
EditorsM. Arif Wani, Taghi M. Khoshgoftaar, Dingding Wang, Huanjing Wang, Naeem Seliya
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages62-68
Number of pages7
ISBN (Electronic)9781728145495
DOIs
StatePublished - 1 Dec 2019
Event18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019 - Boca Raton, United States
Duration: 16 Dec 201919 Dec 2019

Conference

Conference18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
Country/TerritoryUnited States
CityBoca Raton
Period16/12/1919/12/19

Keywords

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

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