HST.953: Clinical Data Learning, Visualization, and Deployments
Course details forthcoming
People: Leo Anthony Celi
Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare
In our new paper, we describe a framework for continual monitoring and updating of AI algorithms in healthcare. Read it today on npj Digital Medicine and share your thoughts.
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A distributed approach to the regulation of clinical AI
No organization by itself, including the FDA, has the expertise to oversee the use of clinical AI.
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A systematic review of federated learning applications for biomedical data
It’s hard enough to prevent algorithmic bias when one can see all the data. It’ll be impossible to detect and mitigate bias when only the weights are shared during the modeling.
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HST.936 Health Disparities Research and Data Science for Disaster Resilience
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Acute kidney injury detection using refined and physiological-feature augmented urine output
Over the past 3 decades, the incidence of AKI has increased over 20-fold, making it an important problem in critical care medicine. The purpose ofthis paper was to investigate the complex factors mediating the relationship between urine output and creatinine in AKI, and to develop a time varying multivariable model that identifies factors mediating the relationship based on augmentation of urine output with physiological features.
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HST.953: Clinical Data Learning, Visualization, and Deployments
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Patient Harm During COVID-19 Pandemic: Using a Human Factors Lens to Promote Patient and Workforce Safety
Effective response to a pandemic requires policy makers and professionals to acknowledge and examine associated challenges such as the absence of evidence-based tests and treatments and the reassignment of healthcare staff to unfamiliar work environments.
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Textbook: Global Health Informatics
Principles of eHealth and mHealth to Improve Quality of Care.
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Textbook: Secondary Analysis of Electronic Health Records, 2016
MIT Critical Data published an open access textbook walking through the process for analyzing health data in 2016 which has been downloaded more than 1.3 million times.