Please use this identifier to cite or link to this item: http://ahro.austin.org.au/austinjspui/handle/1/19642
Title: Machine learning to support social media empowered patients in cancer care and cancer treatment decisions.
Authors: De Silva, Daswin;Ranasinghe, Weranja K B;Bandaragoda, Tharindu;Adikari, Achini;Mills, Nishan;Iddamalgoda, Lahiru;Alahakoon, Damminda;Lawrentschuk, Nathan L;Persad, Raj;Osipov, Evgeny;Gray, Richard;Bolton, Damien M
Affiliation: Research Centre for Data Analytics and Cognition, La Trobe University, Victoria, Australia
Austin Hospital, Heidelberg, Victoria, Australia
Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden
North Bristol, NHS Trust, Bristol, United Kingdom
School of Nursing and Midwifery, La Trobe University, Victoria, Australia
Issue Date: 2018
EDate: 2018-10-18
Citation: PloS one 2018; 13(10): e0205855
Abstract: 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. 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. 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.
URI: http://ahro.austin.org.au/austinjspui/handle/1/19642
DOI: 10.1371/journal.pone.0205855
ORCID: 0000-0002-5145-6783
0000-0003-3878-5969
0000-0001-8553-5618
0000-0002-4006-0388
0000-0001-5047-3496
0000-0001-5112-5063
0000-0001-9694-4206
PubMed URL: 30335805
Type: Journal Article
Appears in Collections:Journal articles

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