Jonas Kemp
Jonas is a research engineer in Google Health. He joined Google as an AI resident in 2017, investigating deep learning methods for modeling and understanding multimodal data in electronic health records. His research interests center on improving the quality, actionability, and reliability of clinical risk predictions, with a particular focus on natural language processing and representation learning methods. Jonas earned his BA in Human Biology and his MS in Computer Science from Stanford University.
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Google
AfriMed-QA: A Pan-African Multi-Specialty Medical Question-Answering Benchmark Dataset
Tobi Olatunji
Abraham Toluwase Owodunni
Charles Nimo
Jennifer Orisakwe
Henok Biadglign Ademtew
Chris Fourie
Foutse Yuehgoh
Stephen Moore
Mardhiyah Sanni
Emmanuel Ayodele
Timothy Faniran
Bonaventure F. P. Dossou
Fola Omofoye
Wendy Kinara
Tassallah Abdullahi
Michael Best
2025
Deciphering clinical abbreviations with a privacy protecting machine learning system
Alvin Rajkomar
Eric Loreaux
Yuchen Liu
Benny Li
Ming-Jun Chen
Yi Zhang
Afroz Mohiuddin
Juraj Gottweis
Nature Communications (2022)
User-centred design for machine learning in health care: a case study from care management
Birju Patel
Daniel Lopez-martinez
Doris Wong
Eric Loreaux
Janjri Desai
Jonathan Chen
Lance Downing
Lutz Thomas Finger
Martin Gamunu Seneviratne
Ming-Jun Chen
Nigam Shah
Ron Li
BMJ Health & Care Informatics (2022)
Instability in clinical risk prediction models using deep learning
Daniel Lopez-Martinez
Alex Yakubovich
Martin Seneviratne
Akshit Tyagi
Ethan Steinberg
N. Lance Downing
Ron C. Li
Keith E. Morse
Nigam H. Shah
Ming-Jun Chen
Proceedings of the 2nd Machine Learning for Health symposium, PMLR (2022), pp. 552-565
Analyzing the Role of Model Uncertainty for Electronic Health Records
Edward Choi
Jeremy Nixon
Ghassen Jerfel
ACM Conference on Health, Inference, and Learning (ACM CHIL) (2020)
