A Comparison of Hypothesis-Driven and Data-Driven Research: A Case Study in Multimodal Data Science in Gut-Brain Axis Research.
Journal
Computers, informatics, nursing : CIN
ISSN: 1538-9774
Titre abrégé: Comput Inform Nurs
Pays: United States
ID NLM: 101141667
Informations de publication
Date de publication:
01 Jul 2023
01 Jul 2023
Historique:
pmc-release:
01
07
2024
medline:
10
7
2023
pubmed:
3
2
2023
entrez:
2
2
2023
Statut:
epublish
Résumé
Data science, bioinformatics, and machine learning are the advent and progression of the fourth paradigm of exploratory science. The need for human-supported algorithms to capture patterns in big data is at the center of personalized healthcare and directly related to translational research. This paper argues that hypothesis-driven and data-driven research work together to inform the research process. At the core of these approaches are theoretical underpinnings that drive progress in the field. Here, we present several exemplars of research on the gut-brain axis that outline the innate values and challenges of these approaches. As nurses are trained to integrate multiple body systems to inform holistic human health promotion and disease prevention, nurses and nurse scientists serve an important role as mediators between this advancing technology and the patients. At the center of person-knowing, nurses need to be aware of the data revolution and use their unique skills to supplement the data science cycle from data to knowledge to insight.
Identifiants
pubmed: 36730994
doi: 10.1097/CIN.0000000000000954
pii: 00024665-990000000-00058
pmc: PMC10102251
mid: NIHMS1814644
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
497-506Subventions
Organisme : Intramural NIH HHS
ID : Z99 CL999999
Pays : United States
Informations de copyright
Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.
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