Revolutionizing Healthcare: Machine Learning Interpretability – Making ML output useful and actionable for clinicians and researchers.Andreas BedorfFebruary 6, 2023
Invited talk at conference on interpretability, safety, and security in AIMihaela van der SchaarDecember 14, 2021
Research pillar: interpretable machine learningMihaela van der SchaarAlihan HüyükJonathan CrabbéZhaozhi QianNick MaxfieldJune 30, 2021
Million-patient study shows strength of machine learning in recommending breast cancer therapiesNick MaxfieldJune 24, 2021
Making machine learning interpretable: a dialog with cliniciansMihaela van der SchaarNick MaxfieldJune 9, 2021
Revolutionizing Healthcare: second roundtable on interpretability in ML/AI for healthcareNick MaxfieldMay 3, 2021
Revolutionizing Healthcare: roundtable on interpretability in ML/AI for healthcareNick MaxfieldMihaela van der SchaarApril 2, 2021
Presentation on AutoML and interpretability (Microsoft Research event)Mihaela van der SchaarOctober 6, 2020
Mihaela van der Schaar featured in Guardian article on reporting guidelines for AI clinical trialsNick MaxfieldSeptember 10, 2020
MiLeTS 2020: Machine Learning for Healthcare in the COVID-19 EraMihaela van der SchaarAugust 24, 2020
ICML 2020: Machine Learning for Healthcare: Challenges, Methods, and FrontiersMihaela van der SchaarJuly 13, 2020
Tortoise Media interview with Mihaela van der Schaar: “Can AI solve the big issues?”Nick MaxfieldMihaela van der SchaarMay 22, 2020
Center for Data Innovation: “5 Q’s for Mihaela van der Schaar”Nick MaxfieldMihaela van der SchaarMay 21, 2020
clairvoyance alpha: the first pipeline toolkit for medical time seriesMihaela van der SchaarJinsung YoonZhaozhi QianDaniel JarrettIoana BicaMay 15, 2020
Why medicine is creating exciting new frontiers for machine learningMihaela van der SchaarApril 30, 2020
Andreas Bedorf
Sam Holt
Nick Maxfield
Mihaela van der Schaar
Alihan Hüyük
Jonathan Crabbé
Zhaozhi Qian
Jinsung Yoon
Daniel Jarrett
Ioana Bica
