Harnessing Machine Learning to Personalize Web-Based Health Care Content.
internet
machine learning
online health information
patient education
personalized content
Journal
Journal of medical Internet research
ISSN: 1438-8871
Titre abrégé: J Med Internet Res
Pays: Canada
ID NLM: 100959882
Informations de publication
Date de publication:
19 10 2021
19 10 2021
Historique:
received:
04
11
2020
accepted:
16
03
2021
revised:
19
01
2021
entrez:
19
10
2021
pubmed:
20
10
2021
medline:
29
10
2021
Statut:
epublish
Résumé
Web-based health care content has emerged as a primary source for patients to access health information without direct guidance from health care providers. The benefit of this approach is dependent on the ability of patients to access engaging high-quality information, but significant variability in the quality of web-based information often forces patients to navigate large quantities of inaccurate, incomplete, irrelevant, or inaccessible content. Personalization positions the patient at the center of health care models by considering their needs, preferences, goals, and values. However, the traditional methods used thus far in health care to determine the factors of high-quality content for a particular user are insufficient. Machine learning (ML) uses algorithms to process and uncover patterns within large volumes of data to develop predictive models that automatically improve over time. The health care sector has lagged behind other industries in implementing ML to analyze user and content features, which can automate personalized content recommendations on a mass scale. With the advent of big data in health care, which builds comprehensive patient profiles drawn from several disparate sources, ML can be used to integrate structured and unstructured data from users and content to deliver content that is predicted to be effective and engaging for patients. This enables patients to engage in their health and support education, self-management, and positive behavior change as well as to enhance clinical outcomes.
Identifiants
pubmed: 34665146
pii: v23i10e25497
doi: 10.2196/25497
pmc: PMC8564651
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e25497Informations de copyright
©Ahmad Guni, Pasha Normahani, Alun Davies, Usman Jaffer. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 19.10.2021.
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