Machine learning to support social media empowered patients in cancer care and cancer treatment decisions
Open accessAbstract
BACKGROUND: A primary variant of social media, online support groups (OSG) extend beyond the standard definition to incorporate a dimension of advice, support and guidance for patients. OSG are complementary, yet significant adjunct to patient journeys. Machine learning and natural language processing techniques can be applied to these large volumes of unstructured text discussions accumulated in OSG for intelligent extraction of patient-reported demographics, behaviours, decisions, treatment, side effects and expressions of emotions. New insights from the fusion and synthesis of such diverse patient-reported information, as expressed throughout the patient journey from diagnosis to treatment and recovery, can contribute towards informed decision-making on personalized healthcare delivery and the development of healthcare policy guidelines. METHODS AND FINDINGS: We have designed and developed an artificial intelligence based analytics framework using machine learning and natural language processing techniques for intelligent analysis and automated aggregation of patient information and interaction trajectories in online support groups. Alongside the social interactions aspect, patient behaviours, decisions, demographics, clinical factors, emotions, as subsequently expressed over time, are extracted and analysed. More specifically, we utilised this platform to investigate the impact of online social influences on the intimate decision scenario of selecting a treatment type, recovery after treatment, side effects and emotions expressed over time, using prostate cancer as a model. Results manifest the three major decision-making behaviours among patients, Paternalistic group, Autonomous group and Shared group. Furthermore, each group demonstrated diverse behaviours in post-decision discussions on clinical outcomes, advice and expressions of emotion during the twelve months following treatment. Over time, the transition of patients from information and emotional support seeking behaviours to providers of information and emotional support to other patients was also observed. CONCLUSIONS: Findings from this study are a rigorous indication of the expectations of social media empowered patients, their potential for individualised decision-making, clinical and emotional needs. The increasing popularity of OSG further confirms that it is timely for clinicians to consider patient voices as expressed in OSG. We have successfully demonstrated that the proposed platform can be utilised to investigate, analyse and derive actionable insights from patient-reported information on prostate cancer, in support of patient focused healthcare delivery. The platform can be extended and applied just as effectively to any other medical condition.
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AI-driven insights could help clinicians better understand behaviors, decisions, side effects, and emotional cues within support groups to derive actionable insights for patient-centered care.
“The findings highlighted how AI-driven insights could help clinicians better understand behaviors, decisions, side effects, and emotional cues within support groups to derive actionable insights for patient-centered care.”
From Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice (2026) · cites this paper as [33]
By identifying patient-reported behaviors, emotions, and treatment decisions, AI tools provided valuable insights into patient decision-making, emotional dynamics, and care preferences.
“By identifying patient-reported behaviors, emotions, and treatment decisions, AI tools provided valuable insights into patient decision-making, emotional dynamics, and care preferences.”
From Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice (2026) · cites this paper as [33]
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- Authors
- De Silva, D., Ranasinghe, W., Bandaragoda, T., Adikari, A., Mills, N., Iddamalgoda, L., Alahakoon, D., Lawrentschuk, N., Persad, R., Osipov, E., Gray, R., & Bolton, D.
- Affiliations
- La Trobe University (AU)
- Austin Hospital (AU)
- North Bristol NHS Trust (GB)
- Luleå University of Technology (SE)
- Corresponding author
- Ranasinghe, Weranja
- Bandaragoda, Tharindu
- Adikari, Achini
- Mills, Nishan
- Iddamalgoda, Lahiru
- Alahakoon, Damminda
- Lawrentschuk, Nathan
- Persad, Raj
- Osipov, Evgeny
- Gray, Richard
- Bolton, Damien
- ORCID
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-10-18
- Volume
- 13
- Issue
- 10
- Pages
- e0205855
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC6193663
- OpenAlex
- W2895961568
- Cited by
- 86
- References
- 88
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 10 · 2023: 12 · 2022: 14 · 2021: 21 · 2020: 13 · 2019: 3
- FWCI
- 14.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- La Trobe University (cc-by)
- Topics
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Social Sciences › Health › Social Media in Health Education
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- online support groups, social media, cancer treatment decision-making, prostate cancer, natural language processing, machine learning, patient behavior, social influence, treatment side effects, emotional expression, patient journey, personalized healthcare
- MeSH terms
- Machine Learning, Adult, Aged, Aged, 80 and over, Decision Making, Emotions, Humans, Male, Middle Aged, Patient Participation, Prostatic Neoplasms, Quality of Life, Self-Help Groups, Social Support, Community Networks, Internet, Information Dissemination, Social Media
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