M
Marzyeh Ghassemi
Researcher at Massachusetts Institute of Technology
Publications - 176
Citations - 6123
Marzyeh Ghassemi is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Computer science & Health care. The author has an hindex of 31, co-authored 131 publications receiving 2986 citations. Previous affiliations of Marzyeh Ghassemi include Canadian Institute for Advanced Research & Stanford University.
Papers
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Journal ArticleDOI
Do no harm: a roadmap for responsible machine learning for health care.
Jenna Wiens,Suchi Saria,Mark Sendak,Marzyeh Ghassemi,Vincent X. Liu,Finale Doshi-Velez,Kenneth Jung,Katherine Heller,David C. Kale,Mohammed Saeed,Pilar N. Ossorio,Sonoo Thadaney-Israni,Anna Goldenberg +12 more
TL;DR: In this Perspective, the authors present a framework, context and guidelines for accelerating the translation of machine-learning-based interventions in health care.
Posted Content
COVID-19 Image Data Collection: Prospective Predictions Are the Future
TL;DR: This dataset currently contains hundreds of frontal view X-rays and is the largest public resource for COVID-19 image and prognostic data, making it a necessary resource to develop and evaluate tools to aid in the treatment of CO VID-19.
Journal ArticleDOI
The false hope of current approaches to explainable artificial intelligence in health care.
TL;DR: In this article, the authors argue that explainability is a false hope for explainable AI and that current explainability methods are unlikely to achieve these goals for patient-level decision support, and advocate for rigorous internal and external validation of AI models as a more direct means of achieving the goals often associated with explainability.
COVID-19 Image Data Collection: Prospective Predictions are the Future
TL;DR: Covid-Chest X-ray (CXR) dataset as discussed by the authors contains hundreds of frontal view X-rays and is the largest public resource for COVID-19 image and prognostic data, making it a necessary resource to develop and evaluate tools to aid in the treatment of COVID19.
Proceedings ArticleDOI
Unfolding physiological state: mortality modelling in intensive care units
Marzyeh Ghassemi,Tristan Naumann,Finale Doshi-Velez,Nicole J. Brimmer,Rohit Joshi,Anna Rumshisky,Peter Szolovits +6 more
TL;DR: This work examined the use of latent variable models to decompose free-text hospital notes into meaningful features, and found that latent topic-derived features were effective in determining patient mortality under three timelines: in-hospital, 30 day post- Discharge, and 1 year post-discharge mortality.