Press
Explainable machine learning
Scott M. Lundberg, and Su-In Lee. A unified approach to interpreting model predictions. Neural Information Processing Systems (NeurIPS) December, 2017 Oral Presentation [Paper in arxiv] [GitHub]
- Our SHAP paper received the Madrona Prize at the Allen School 2017 Industry Affiliates Annual Research Day.
- Our SHAP paper got cited 100 times within the first one year after publication.
Prescience
Scott M. Lundberg, Bala Nair, Monica S. Vavilala, Mayumi Horibe, Michael J. Eisses, Trevor Adams, David E. Liston, Daniel King-Wai Low, Shu-Fang Newman, Jerry Kim, and Su-In Lee*. Explainable machine learning predictions to help anesthesiologists prevent hypoxemia during surgery. [Paper] Nature BME 2, 749–760 (2018) - Featured on the Cover
- Nature BME Editorial - Towards trustable machine learning
- UW News - Prescience: Helping doctors predict the future
- GeekWire - Univ. of Washington researchers unveil Prescience, an AI system that predicts problems during surgery
- Allen School News - “Prescience” interpretable machine-learning system for predicting complications during surgery featured in Nature Biomedical Engineering
Cancer precision medicine
Su-In Lee*,C, Safiye CelikC , Benjamin A. Logsdon, Scott M. Lundberg, Timothy J. Martins, Vivian M. Oehler, Elihu H. Estey, Chris P. Miller, Sylvia Chien, Akanksha Saxena, C. Anthony Blau, and Pamela S. Becker. A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia. Nature Communications 9, Article number: 42 2018 [Paper] [MERGE website]
- KIRO-TV Su-In's interview
- Recommended in F1000Prime as being of special significance in its field.
- GeekWire - Using precision medicine to kill cancer — with artificial intelligence
- Allen School News - Researchers “MERGE” machine learning and medicine to enable targeted treatment of individual cancer patients
- UW Huddle - Personal beginnings for a personalized medicine breakthrough
- 1 year, 10 innovations from UW's Paul G. Allen School of CSE
Alzheimer's disease therapeutic target
- Our EMBARKER project on identifying therapeutic targets for Alzheimer's disease won the Madrona Prize at the Allen School 2018 Industry Affiliates Annual Research Day.
- GeekWire - From fighting Alzheimer’s to AR captions, UW computer science students show cutting-edge innovations
- BusinessWire - Madrona Awards 2018 Madrona Prize to UW Project That Applies Machine Learning to Fighting Alzheimer’s Disease
- Big data framework seeks treatment targets for Alzheimer’s disease
- What really causes Alzheimer's and how might we fix it?