ArticleScientific reports2023
Machine learning algorithms distinguish discrete digital emotional fingerprints for web pages related to back pain.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Health-Related Quality of Life Among Patients With Ostomy Intestinal Stoma According to Sex and Ostomy Permanence: A Systematic Review and Meta-Analysis.International wound journal · 2026Pooled it
- Understanding the Impact of Multiple Sclerosis on Quality of Life: An Italian Pilot Study.Brain sciences · 2025Article
- Quality of Life and Psychophysical Consequences in Individuals with Intestinal Stoma: An Observational Study.International journal of environmental research and public health · 2025Observational
- The Contribution of Artificial Intelligence in Nursing Education: A Scoping Review of the Literature.Nursing reports (Pavia, Italy) · 2025Review
- Low Back Pain in Italian Nurses: A Statistical Analysis of Disability and Work Productivity Impairment-An Observational Study.Healthcare (Basel, Switzerland) · 2025Article
- Emotional recognition technologies applied to health: review and challenges.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2025Review
- Machine learning insights into scapular stabilization for alleviating shoulder pain in college students.Scientific reports · 2024Article
- Manifold-based sparse representation for opinion mining.Scientific reports · 2023Article
- MicroRNA-29a Mitigates Laminectomy-Induced Spinal Epidural Fibrosis and Gait Dysregulation by Repressing TGF-β1 and IL-6.International journal of molecular sciences · 2023Article
- Machine and deep learning based on magnetic resonance imaging to segment glioblastoma and predict the spread of recurrence: a multicenter retrospective protocol.Frontiers in neurologyArticle
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Authors and funding
12 authors.
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Abstract
Back pain is the leading cause of disability worldwide. Its emergence relates not only to the musculoskeletal degeneration biological substrate but also to psychosocial factors; emotional components play a pivotal role. In modern society, people are significantly informed by the Internet; in turn, they contribute social validation to a "successful" digital information subset in a dynamic interplay. The Affective component of medical pages has not been previously investigated, a significant gap in knowledge since they represent a critical biopsychosocial feature. We tested the hypothesis that successful pages related to spine pathology embed a consistent emotional pattern, allowing discrimination from a control group. The pool of web pages related to spine or hip/knee pathology was automatically selected by relevance and popularity and submitted to automated sentiment analysis to generate emotional patterns. Machine Learning (ML) algorithms were trained to predict page original topics from patterns with binary classification. ML showed high discrimination accuracy; disgust emerged as a discriminating emotion. The findings suggest that the digital affective "successful content" (collective consciousness) integrates patients' biopsychosocial ecosystem, with potential implications for the emergence of chronic pain, and the endorsement of health-relevant specific behaviors. Awareness of such effects raises practical and ethical issues for health information providers.
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