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Insights into Amyotrophic Lateral Sclerosis from a Machine Learning Perspective
Objective: Amyotrophic lateral sclerosis (ALS) disease state prediction usually assumes linear progression and uses a classifier evaluated by its accuracy. Since disease progression is not linear, and the accuracy measurement cannot tell large from small prediction errors, we dispense with the linea...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6832919/ https://www.ncbi.nlm.nih.gov/pubmed/31581566 http://dx.doi.org/10.3390/jcm8101578 |
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author | Gordon, Jonathan Lerner, Boaz |
author_facet | Gordon, Jonathan Lerner, Boaz |
author_sort | Gordon, Jonathan |
collection | PubMed |
description | Objective: Amyotrophic lateral sclerosis (ALS) disease state prediction usually assumes linear progression and uses a classifier evaluated by its accuracy. Since disease progression is not linear, and the accuracy measurement cannot tell large from small prediction errors, we dispense with the linearity assumption and apply ordinal classification that accounts for error severity. In addition, we identify the most influential variables in predicting and explaining the disease. Furthermore, in contrast to conventional modeling of the patient’s total functionality, we also model separate patient functionalities (e.g., in walking or speaking). Methods: Using data from 3772 patients from the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database, we introduce and train ordinal classifiers to predict patients’ disease state in their last clinic visit, while accounting differently for different error severities. We use feature-selection methods and the classifiers themselves to determine the most influential variables in predicting the disease from demographic, clinical, and laboratory data collected in either the first, last, or both clinic visits, and the Bayesian network classifier to identify interrelations among these variables and their relations with the disease state. We apply these methods to model each of the patient functionalities. Results: We show the error distribution in ALS state prediction and demonstrate that ordinal classifiers outperform classifiers that do not account for error severity. We identify clinical and lab test variables influential to prediction of different ALS functionalities and their interrelations, and specific value combinations of these variables that occur more frequently in patients with severe deterioration than in patients with mild deterioration and vice versa. Conclusions: Ordinal classification of ALS state is superior to conventional classification. Identification of influential ALS variables and their interrelations help explain disease mechanism. Modeling of patient functionalities separately allows relation of variables and their connections to different aspects of the disease as may be expressed in different body segments. |
format | Online Article Text |
id | pubmed-6832919 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68329192019-11-25 Insights into Amyotrophic Lateral Sclerosis from a Machine Learning Perspective Gordon, Jonathan Lerner, Boaz J Clin Med Article Objective: Amyotrophic lateral sclerosis (ALS) disease state prediction usually assumes linear progression and uses a classifier evaluated by its accuracy. Since disease progression is not linear, and the accuracy measurement cannot tell large from small prediction errors, we dispense with the linearity assumption and apply ordinal classification that accounts for error severity. In addition, we identify the most influential variables in predicting and explaining the disease. Furthermore, in contrast to conventional modeling of the patient’s total functionality, we also model separate patient functionalities (e.g., in walking or speaking). Methods: Using data from 3772 patients from the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database, we introduce and train ordinal classifiers to predict patients’ disease state in their last clinic visit, while accounting differently for different error severities. We use feature-selection methods and the classifiers themselves to determine the most influential variables in predicting the disease from demographic, clinical, and laboratory data collected in either the first, last, or both clinic visits, and the Bayesian network classifier to identify interrelations among these variables and their relations with the disease state. We apply these methods to model each of the patient functionalities. Results: We show the error distribution in ALS state prediction and demonstrate that ordinal classifiers outperform classifiers that do not account for error severity. We identify clinical and lab test variables influential to prediction of different ALS functionalities and their interrelations, and specific value combinations of these variables that occur more frequently in patients with severe deterioration than in patients with mild deterioration and vice versa. Conclusions: Ordinal classification of ALS state is superior to conventional classification. Identification of influential ALS variables and their interrelations help explain disease mechanism. Modeling of patient functionalities separately allows relation of variables and their connections to different aspects of the disease as may be expressed in different body segments. MDPI 2019-10-01 /pmc/articles/PMC6832919/ /pubmed/31581566 http://dx.doi.org/10.3390/jcm8101578 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gordon, Jonathan Lerner, Boaz Insights into Amyotrophic Lateral Sclerosis from a Machine Learning Perspective |
title | Insights into Amyotrophic Lateral Sclerosis from a Machine Learning Perspective |
title_full | Insights into Amyotrophic Lateral Sclerosis from a Machine Learning Perspective |
title_fullStr | Insights into Amyotrophic Lateral Sclerosis from a Machine Learning Perspective |
title_full_unstemmed | Insights into Amyotrophic Lateral Sclerosis from a Machine Learning Perspective |
title_short | Insights into Amyotrophic Lateral Sclerosis from a Machine Learning Perspective |
title_sort | insights into amyotrophic lateral sclerosis from a machine learning perspective |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6832919/ https://www.ncbi.nlm.nih.gov/pubmed/31581566 http://dx.doi.org/10.3390/jcm8101578 |
work_keys_str_mv | AT gordonjonathan insightsintoamyotrophiclateralsclerosisfromamachinelearningperspective AT lernerboaz insightsintoamyotrophiclateralsclerosisfromamachinelearningperspective |