Mining Early Life Risk and Resiliency Factors and Their Influences in Human Populations from PubMed: A Machine Learning Approach to Discover DOHaD Evidence

The Developmental Origins of Health and Disease (DOHaD) framework aims to understand how early life exposures shape lifecycle health. To date, no comprehensive list of these exposures and their interactions has been developed, which limits our ability to predict trajectories of risk and resiliency i...

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Autores principales:	Shrankhala Tewari, Pablo Toledo Margalef, Ayesha Kareem, Ayah Abdul-Hussein, Marina White, Ashley Wazana, Sandra T. Davidge, Claudio Delrieux, Kristin L. Connor
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Developmental Origins of Health and Disease developmental programming machine learning natural language processing text mining Medicine R
Acceso en línea:	https://doaj.org/article/a838101fc2b0461d8266cd34590531e7
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spelling	oai:doaj.org-article:a838101fc2b0461d8266cd34590531e72021-11-25T18:06:50ZMining Early Life Risk and Resiliency Factors and Their Influences in Human Populations from PubMed: A Machine Learning Approach to Discover DOHaD Evidence10.3390/jpm111110642075-4426https://doaj.org/article/a838101fc2b0461d8266cd34590531e72021-10-01T00:00:00Zhttps://www.mdpi.com/2075-4426/11/11/1064https://doaj.org/toc/2075-4426The Developmental Origins of Health and Disease (DOHaD) framework aims to understand how early life exposures shape lifecycle health. To date, no comprehensive list of these exposures and their interactions has been developed, which limits our ability to predict trajectories of risk and resiliency in humans. To address this gap, we developed a model that uses text-mining, machine learning, and natural language processing approaches to automate search, data extraction, and content analysis from DOHaD-related research articles available in PubMed. Our first model captured 2469 articles, which were subsequently categorised into topics based on word frequencies within the titles and abstracts. A manual screening validated 848 of these as relevant, which were used to develop a revised model that finally captured 2098 articles that largely fell under the most prominently researched domains related to our specific DOHaD focus. The articles were clustered according to latent topic extraction, and 23 experts in the field independently labelled the perceived topics. Consensus analysis on this labelling yielded mostly from fair to substantial agreement, which demonstrates that automated models can be developed to successfully retrieve and classify research literature, as a first step to gather evidence related to DOHaD risk and resilience factors that influence later life human health.Shrankhala TewariPablo Toledo MargalefAyesha KareemAyah Abdul-HusseinMarina WhiteAshley WazanaSandra T. DavidgeClaudio DelrieuxKristin L. ConnorMDPI AGarticleDevelopmental Origins of Health and Diseasedevelopmental programmingmachine learningnatural language processingtext miningMedicineRENJournal of Personalized Medicine, Vol 11, Iss 1064, p 1064 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Developmental Origins of Health and Disease developmental programming machine learning natural language processing text mining Medicine R
spellingShingle	Developmental Origins of Health and Disease developmental programming machine learning natural language processing text mining Medicine R Shrankhala Tewari Pablo Toledo Margalef Ayesha Kareem Ayah Abdul-Hussein Marina White Ashley Wazana Sandra T. Davidge Claudio Delrieux Kristin L. Connor Mining Early Life Risk and Resiliency Factors and Their Influences in Human Populations from PubMed: A Machine Learning Approach to Discover DOHaD Evidence
description	The Developmental Origins of Health and Disease (DOHaD) framework aims to understand how early life exposures shape lifecycle health. To date, no comprehensive list of these exposures and their interactions has been developed, which limits our ability to predict trajectories of risk and resiliency in humans. To address this gap, we developed a model that uses text-mining, machine learning, and natural language processing approaches to automate search, data extraction, and content analysis from DOHaD-related research articles available in PubMed. Our first model captured 2469 articles, which were subsequently categorised into topics based on word frequencies within the titles and abstracts. A manual screening validated 848 of these as relevant, which were used to develop a revised model that finally captured 2098 articles that largely fell under the most prominently researched domains related to our specific DOHaD focus. The articles were clustered according to latent topic extraction, and 23 experts in the field independently labelled the perceived topics. Consensus analysis on this labelling yielded mostly from fair to substantial agreement, which demonstrates that automated models can be developed to successfully retrieve and classify research literature, as a first step to gather evidence related to DOHaD risk and resilience factors that influence later life human health.
format	article
author	Shrankhala Tewari Pablo Toledo Margalef Ayesha Kareem Ayah Abdul-Hussein Marina White Ashley Wazana Sandra T. Davidge Claudio Delrieux Kristin L. Connor
author_facet	Shrankhala Tewari Pablo Toledo Margalef Ayesha Kareem Ayah Abdul-Hussein Marina White Ashley Wazana Sandra T. Davidge Claudio Delrieux Kristin L. Connor
author_sort	Shrankhala Tewari
title	Mining Early Life Risk and Resiliency Factors and Their Influences in Human Populations from PubMed: A Machine Learning Approach to Discover DOHaD Evidence
title_short	Mining Early Life Risk and Resiliency Factors and Their Influences in Human Populations from PubMed: A Machine Learning Approach to Discover DOHaD Evidence
title_full	Mining Early Life Risk and Resiliency Factors and Their Influences in Human Populations from PubMed: A Machine Learning Approach to Discover DOHaD Evidence
title_fullStr	Mining Early Life Risk and Resiliency Factors and Their Influences in Human Populations from PubMed: A Machine Learning Approach to Discover DOHaD Evidence
title_full_unstemmed	Mining Early Life Risk and Resiliency Factors and Their Influences in Human Populations from PubMed: A Machine Learning Approach to Discover DOHaD Evidence
title_sort	mining early life risk and resiliency factors and their influences in human populations from pubmed: a machine learning approach to discover dohad evidence
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a838101fc2b0461d8266cd34590531e7
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Mining Early Life Risk and Resiliency Factors and Their Influences in Human Populations from PubMed: A Machine Learning Approach to Discover DOHaD Evidence

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