Daswin De Silva|Weranja Ranasinghe|Tharindu Bandaragoda|Achini Adikari|Nishan Mills|Lahiru Iddamalgoda|Damminda Alahakoon|Nathan Lawrentschuk|Raj Persad|Evgeny Osipov|Richard Gray|Damien Bolton
Machine learning to support social media empowered patients in cancer care and cancer treatment decisions
La Trobe University|Austin Hospital|La Trobe University|La Trobe University|La Trobe University|La Trobe University|La Trobe University|La Trobe University|Austin Hospital|North Bristol NHS Trust|Luleå University of Technology|La Trobe University|Austin Hospital
primary variant social media online support groups osg extend beyond standard definition incorporate dimension advice support guidance patients osg complementary yet significant adjunct patient journeys machine learning natural language processing techniques applied large volumes unstructured text discussions accumulated osg intelligent extraction patientreported demographics behaviours decisions treatment side effects expressions emotions new insights fusion synthesis diverse patientreported information expressed throughout patient journey diagnosis treatment recovery contribute towards informed decisionmaking personalized healthcare delivery development healthcare policy guidelineswe designed developed artificial intelligence based analytics framework using machine learning natural language processing techniques intelligent analysis automated aggregation patient information interaction trajectories online support groups alongside social interactions aspect patient behaviours decisions demographics clinical factors emotions subsequently expressed time extracted analysed specifically utilised platform investigate impact online social influences intimate decision scenario selecting treatment type recovery treatment side effects emotions expressed time using prostate cancer model results manifest three major decisionmaking behaviours among patients paternalistic group autonomous group shared group furthermore group demonstrated diverse behaviours postdecision discussions clinical outcomes advice expressions emotion twelve months following treatment time transition patients information emotional support seeking behaviours providers information emotional support patients also observedfindings study rigorous indication expectations social media empowered patients potential individualised decisionmaking clinical emotional needs increasing popularity osg confirms timely clinicians consider patient voices expressed osg successfully demonstrated proposed platform utilised investigate analyse derive actionable insights patientreported information prostate cancer support patient focused healthcare delivery platform extended applied effectively medical condition