Forecasting the Suitability of Online Mental Health Information for Effective Self-Care Developing Machine Learning Classifiers Using Natural Language Features
Bibliographic Data
| ID | 15499469 |
|---|---|
| Authors | Meng Ji (0000-0002-7463-9208, The University of Sydney, corresponding author), Wenxiu Xie (0000-0002-8528-5193, City University of Hong Kong), Riliu Huang (The University of Sydney), Xiaobo Qian (0000-0001-5819-4379, South China Normal University) |
| Year | 2021 |
| Volume | 18 |
| Issue | 19 |
| Pages | 10048-10048 |
| Publication date | 2021-09-24 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph181910048 |
| PMID | 34639348 |
| OpenAlex | W3204124655 |
| Language | EN |
| References cited | 33 |
Our study found that PAS had significant advantage over GEN mental health information regarding information actionability, engagement, and suitability for specific populations with distinct mental health issues. GEN is more suitable for general mental health information acquisition, whereas PAS can effectively engage patients and provide more effective and needed self-care support. The Bayesian machine learning classifier developed provided automatic tools to support decision making in the clinic to identify more actionable resources, effective to support self-care among different populations
Classifier (UML · Clinical decision support system · Decision support system · Health care · Machine learning · Mental health · Mental illness · Psychiatry · Support vector machine · Computer Science · Digital Mental Health Interventions · Health Literacy and Information Accessibility · Medicine · Mental Health via Writing · Psychology · Artificial Intelligence
| Highly cited | No |
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