The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) Understanding the COVID-19 Data Quality Problem with Sherri Rose - #374
May 11, 2020
Sherri Rose, an Associate Professor at Harvard Medical School, delves into pressing data quality issues in healthcare during the COVID-19 pandemic. She emphasizes the critical need for reliable datasets and rigor in research methodologies. The discussion highlights the rise of algorithmic fairness, particularly its importance for marginalized communities, and critiques current standards in causal inference. Sherri also explores the nuances of risk adjustment in healthcare funding, urging a thoughtful engagement with research to better inform healthcare policies.
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Path to Machine Learning
- Sherri Rose's interest in science and math led her to biostatistics.
- She discovered machine learning's problem-solving potential during her graduate studies at UC Berkeley.
COVID-19 and Data Quality
- The COVID-19 pandemic highlights the importance of data quality in healthcare, which has long been an issue.
- Existing data sources like billing claims and clinical records weren't designed for research and have limitations.
Understand Data First
- Understand your data before applying machine learning tools.
- Throwing tools at data without understanding it, especially during a crisis like COVID-19, is crucial to avoid.