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dc.contributor.author
Tewari, Shrankhala
dc.contributor.author
Toledo Margalef, Pablo Adrian
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Kareem, Ayesha
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Abdul Hussein, Ayah
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White, Marina
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Wazana, Ashley
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Davidge, Sandra T.
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Delrieux, Claudio Augusto
dc.contributor.author
Connor, Kristin L.
dc.date.available
2022-06-16T14:12:20Z
dc.date.issued
2021-10-22
dc.identifier.citation
Tewari, Shrankhala; Toledo Margalef, Pablo Adrian; Kareem, Ayesha; Abdul Hussein, Ayah; White, Marina; et al.; Mining Early Life Risk and Resiliency Factors and Their Influences in Human Populations from PubMed: A Machine Learning Approach to Discover DOHaD Evidence; Multidisciplinary Digital Publishing Institute; Journal of Personalized Medicine; 11; 11; 22-10-2021; 1-13
dc.identifier.uri
http://hdl.handle.net/11336/159933
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Multidisciplinary Digital Publishing Institute
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
MACHINE LEARNING
dc.subject
TEXT MINING
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DEVELOPMENTAL ORIGINS OF HEALTH AND DISEASE
dc.subject.classification
Otras Ciencias de la Computación e Información
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Mining Early Life Risk and Resiliency Factors and Their Influences in Human Populations from PubMed: A Machine Learning Approach to Discover DOHaD Evidence
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-05-17T12:52:22Z
dc.identifier.eissn
2075-4426
dc.journal.volume
11
dc.journal.number
11
dc.journal.pagination
1-13
dc.journal.pais
Suiza
dc.journal.ciudad
Basilea
dc.description.fil
Fil: Tewari, Shrankhala. Carleton University; Canadá
dc.description.fil
Fil: Toledo Margalef, Pablo Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; Argentina
dc.description.fil
Fil: Kareem, Ayesha. Carleton University; Canadá
dc.description.fil
Fil: Abdul Hussein, Ayah. Carleton University; Canadá
dc.description.fil
Fil: White, Marina. Carleton University; Canadá
dc.description.fil
Fil: Wazana, Ashley. McGill University; Canadá
dc.description.fil
Fil: Davidge, Sandra T.. University of Alberta; Canadá
dc.description.fil
Fil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina
dc.description.fil
Fil: Connor, Kristin L.. Carleton University; Canadá
dc.journal.title
Journal of Personalized Medicine
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2075-4426/11/11/1064
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.3390/jpm11111064
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