09:30- 11:00 Challenge 1: Are we asking the right questions for maximum societal impact from health-related machine learning? Fitting more complex functions to a previously published objective is not a good use of machine learning effort; problems need better selection, framing and evaluation metrics.
- Chair: Iain Buchan
- Patient-centred research in a complex health data world: a new role for ML. John Holmes, University of Pennsylvania
- Learning from timestamped population-based epidemiological data. Myra Spiliopoulou, Universitat Magdeburg
- Going the last mile: why deep learning is not enough. Enrico Coiera, Macquarie University
11:00- 11:30 Coffee Break
11:30- 13:00 Challenge 2: Are we borrowing strength sufficiently across silos of inference to be patient-centred and population-serving? Studies that focus on prediction using offline datasets without collaborative feedback are limited; problems need addressing within systems at reasonable resolution (time, place, person).
- Chair: Danielle Belgrave
- Smartphone-based digital phenotyping. Jukka-Pekka Onnela, Harvard
- Adapting AI approaches to rare diseases. Anita Burgun, Paris Descartes University
- AutoPrognosis: Automating the design of predictive models for clinical risk and prognosis. Mihaela Van de Shar, University of Oxford
13:00- 14:00 Lunch
18:00- 18:45 Challenge 3: How does this community organise at global scale to share well-formed problems to advance health data science and avoid AI hype? Technology-led collaborations attract poorly framed problems; our field needs to embrace a more interdisciplinary approach and pursue more careful and meaningful collaborations, in concert.
- Chair: Saria Suchi
- Democratisation of data science. Blaž Zupan, University of Ljubljana
- Collaboration as an innovation to advance healthcare AI. Leo Celi, MIT
18:45- 19:30 Next steps and closing remarks
- Chair: Niels Peek
- AMIE panel: Riccardo Bellazzi, Anita Burgun, John Holmes, Suchi Saria and Allan Tucker