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Machine learning in the real-world evidence and HEOR fields: an interview with Turgay Ayer, Jag Chhatwal and Selin Merdan

In this joint interview feature, following their workshop delivered at the International Society for Pharmacoeconomics and Outcomes Research’s 2020 Asia Pacific meeting (Virtual ISPOR Asia Pacific 2020; 14–16 September), Turgay Ayer (Georgia Tech, GA, USA), Jag Chhatwal (Massachusetts General Hospital, MA, USA) and Selin Merdan (Value Analytics Lab, GA, USA) discuss the practicalities of applying machine learning (ML) in the real-world evidence and health economics and outcomes research (HEOR) fields.

Machine Learning: What’s on the Horizon for HEOR?

Ever wondered what machine learning is and how it might be applied to health economics and outcomes research (HEOR)? This informative workshop presented a technical overview of machine learning techniques, examples of machine learning conducted in healthcare and relevant to health economics, and some potential uses for machine learning moving forward.

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