research-article

Discovering, selecting and exploiting feature sequence records of study participants for the classification of epidemiological data on hepatic steatosis

Authors:

Tommy Hielscher,

Panagiotis Papapetrou,

Myra SpiliopoulouAuthors Info & Claims

SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing

Pages 6 - 13

https://doi.org/10.1145/3167132.3167162

Published: 09 April 2018 Publication History

Abstract

In longitudinal epidemiological studies, participants undergo repeated medical examinations and are thus represented by a potentially large number of short examination outcome sequences. Some of those sequences may contain important information in various forms, such as patterns, with respect to the disease under study, while others may be on features of little relevance to the outcome. In this work, we propose a framework for Discovery, Selection and Exploitation (DiSelEx) of longitudinal epidemiological data, aiming to identify informative patterns among these sequences. DiSelEx combines sequence clustering with supervised learning to identify sequence groups that contribute to class separation. Newly derived and old features are evaluated and selected according to their redundancy and informativeness regarding the target variable. The selected feature set is then used to learn a classification model on the study data. We evaluate DiSelEx on cohort participants for the disorder "hepatic steatosis" and report on the impact on predictive performance when using sequential data in comparison to utilizing only the basic classifier.¹

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Cited By

Bagattini FKarlsson IRebane JPapapetrou P(2019)A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical recordsBMC Medical Informatics and Decision Making10.1186/s12911-018-0717-419:1Online publication date: 10-Jan-2019
https://doi.org/10.1186/s12911-018-0717-4

Index Terms

Discovering, selecting and exploiting feature sequence records of study participants for the classification of epidemiological data on hepatic steatosis
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms

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Published In

cover image ACM Conferences

SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing

April 2018

2327 pages

ISBN:9781450351911

DOI:10.1145/3167132

Conference Chairs:
Hisham M. Haddad
Kennesaw State University
,
Roger L. Wainwright
University of Tulsa
,
Richard Chbeir
University of Pau & Pays Adour, France

Copyright © 2018 ACM.

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New York, NY, United States

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Published: 09 April 2018

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SAC 2018: Symposium on Applied Computing

April 9 - 13, 2018

Pau, France

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Cited By

Bagattini FKarlsson IRebane JPapapetrou P(2019)A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical recordsBMC Medical Informatics and Decision Making10.1186/s12911-018-0717-419:1Online publication date: 10-Jan-2019
https://doi.org/10.1186/s12911-018-0717-4

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