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Open Application of Statistical and Machine Learning Models to Explore the Impact of Environmental Exposures on Health and Disease: An Asthma Use Case

ICEES (Integrated Clinical and Environmental Exposures Service) provides a disease-agnostic, regulatory-compliant approach for openly exposing and analyzing clinical data that have been integrated at the patient level with environmental exposures data. ICEES is equipped with basic features to suppor...

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Autores principales: Lan, Bo, Haaland, Perry, Krishnamurthy, Ashok, Peden, David B., Schmitt, Patrick L., Sharma, Priya, Sinha, Meghamala, Xu, Hao, Fecho, Karamarie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8582932/
https://www.ncbi.nlm.nih.gov/pubmed/34769911
http://dx.doi.org/10.3390/ijerph182111398
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author Lan, Bo
Haaland, Perry
Krishnamurthy, Ashok
Peden, David B.
Schmitt, Patrick L.
Sharma, Priya
Sinha, Meghamala
Xu, Hao
Fecho, Karamarie
author_facet Lan, Bo
Haaland, Perry
Krishnamurthy, Ashok
Peden, David B.
Schmitt, Patrick L.
Sharma, Priya
Sinha, Meghamala
Xu, Hao
Fecho, Karamarie
author_sort Lan, Bo
collection PubMed
description ICEES (Integrated Clinical and Environmental Exposures Service) provides a disease-agnostic, regulatory-compliant approach for openly exposing and analyzing clinical data that have been integrated at the patient level with environmental exposures data. ICEES is equipped with basic features to support exploratory analysis using statistical approaches, such as bivariate chi-square tests. We recently developed a method for using ICEES to generate multivariate tables for subsequent application of machine learning and statistical models. The objective of the present study was to use this approach to identify predictors of asthma exacerbations through the application of three multivariate methods: conditional random forest, conditional tree, and generalized linear model. Among seven potential predictor variables, we found five to be of significant importance using both conditional random forest and conditional tree: prednisone, race, airborne particulate exposure, obesity, and sex. The conditional tree method additionally identified several significant two-way and three-way interactions among the same variables. When we applied a generalized linear model, we identified four significant predictor variables, namely prednisone, race, airborne particulate exposure, and obesity. When ranked in order by effect size, the results were in agreement with the results from the conditional random forest and conditional tree methods as well as the published literature. Our results suggest that the open multivariate analytic capabilities provided by ICEES are valid in the context of an asthma use case and likely will have broad value in advancing open research in environmental and public health.
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spelling pubmed-85829322021-11-12 Open Application of Statistical and Machine Learning Models to Explore the Impact of Environmental Exposures on Health and Disease: An Asthma Use Case Lan, Bo Haaland, Perry Krishnamurthy, Ashok Peden, David B. Schmitt, Patrick L. Sharma, Priya Sinha, Meghamala Xu, Hao Fecho, Karamarie Int J Environ Res Public Health Article ICEES (Integrated Clinical and Environmental Exposures Service) provides a disease-agnostic, regulatory-compliant approach for openly exposing and analyzing clinical data that have been integrated at the patient level with environmental exposures data. ICEES is equipped with basic features to support exploratory analysis using statistical approaches, such as bivariate chi-square tests. We recently developed a method for using ICEES to generate multivariate tables for subsequent application of machine learning and statistical models. The objective of the present study was to use this approach to identify predictors of asthma exacerbations through the application of three multivariate methods: conditional random forest, conditional tree, and generalized linear model. Among seven potential predictor variables, we found five to be of significant importance using both conditional random forest and conditional tree: prednisone, race, airborne particulate exposure, obesity, and sex. The conditional tree method additionally identified several significant two-way and three-way interactions among the same variables. When we applied a generalized linear model, we identified four significant predictor variables, namely prednisone, race, airborne particulate exposure, and obesity. When ranked in order by effect size, the results were in agreement with the results from the conditional random forest and conditional tree methods as well as the published literature. Our results suggest that the open multivariate analytic capabilities provided by ICEES are valid in the context of an asthma use case and likely will have broad value in advancing open research in environmental and public health. MDPI 2021-10-29 /pmc/articles/PMC8582932/ /pubmed/34769911 http://dx.doi.org/10.3390/ijerph182111398 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lan, Bo
Haaland, Perry
Krishnamurthy, Ashok
Peden, David B.
Schmitt, Patrick L.
Sharma, Priya
Sinha, Meghamala
Xu, Hao
Fecho, Karamarie
Open Application of Statistical and Machine Learning Models to Explore the Impact of Environmental Exposures on Health and Disease: An Asthma Use Case
title Open Application of Statistical and Machine Learning Models to Explore the Impact of Environmental Exposures on Health and Disease: An Asthma Use Case
title_full Open Application of Statistical and Machine Learning Models to Explore the Impact of Environmental Exposures on Health and Disease: An Asthma Use Case
title_fullStr Open Application of Statistical and Machine Learning Models to Explore the Impact of Environmental Exposures on Health and Disease: An Asthma Use Case
title_full_unstemmed Open Application of Statistical and Machine Learning Models to Explore the Impact of Environmental Exposures on Health and Disease: An Asthma Use Case
title_short Open Application of Statistical and Machine Learning Models to Explore the Impact of Environmental Exposures on Health and Disease: An Asthma Use Case
title_sort open application of statistical and machine learning models to explore the impact of environmental exposures on health and disease: an asthma use case
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8582932/
https://www.ncbi.nlm.nih.gov/pubmed/34769911
http://dx.doi.org/10.3390/ijerph182111398
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