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:	Bo Lan, Perry Haaland, Ashok Krishnamurthy, David B. Peden, Patrick L. Schmitt, Priya Sharma, Meghamala Sinha, Hao Xu, Karamarie Fecho
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	open data open science machine learning conditional random forest conditional tree biostatistics Medicine R
Acceso en línea:	https://doaj.org/article/fa200d2750dc4b699791f5ec817ac702
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spelling	oai:doaj.org-article:fa200d2750dc4b699791f5ec817ac7022021-11-11T16:30:47ZOpen Application of Statistical and Machine Learning Models to Explore the Impact of Environmental Exposures on Health and Disease: An Asthma Use Case10.3390/ijerph1821113981660-46011661-7827https://doaj.org/article/fa200d2750dc4b699791f5ec817ac7022021-10-01T00:00:00Zhttps://www.mdpi.com/1660-4601/18/21/11398https://doaj.org/toc/1661-7827https://doaj.org/toc/1660-4601ICEES (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.Bo LanPerry HaalandAshok KrishnamurthyDavid B. PedenPatrick L. SchmittPriya SharmaMeghamala SinhaHao XuKaramarie FechoMDPI AGarticleopen dataopen sciencemachine learningconditional random forestconditional treebiostatisticsMedicineRENInternational Journal of Environmental Research and Public Health, Vol 18, Iss 11398, p 11398 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	open data open science machine learning conditional random forest conditional tree biostatistics Medicine R
spellingShingle	open data open science machine learning conditional random forest conditional tree biostatistics Medicine R Bo Lan Perry Haaland Ashok Krishnamurthy David B. Peden Patrick L. Schmitt Priya Sharma Meghamala Sinha Hao Xu Karamarie Fecho Open Application of Statistical and Machine Learning Models to Explore the Impact of Environmental Exposures on Health and Disease: An Asthma Use Case
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.
format	article
author	Bo Lan Perry Haaland Ashok Krishnamurthy David B. Peden Patrick L. Schmitt Priya Sharma Meghamala Sinha Hao Xu Karamarie Fecho
author_facet	Bo Lan Perry Haaland Ashok Krishnamurthy David B. Peden Patrick L. Schmitt Priya Sharma Meghamala Sinha Hao Xu Karamarie Fecho
author_sort	Bo Lan
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_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_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_sort	open application of statistical and machine learning models to explore the impact of environmental exposures on health and disease: an asthma use case
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/fa200d2750dc4b699791f5ec817ac702
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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

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