Dr. Eric Davis walks us through what it means for a data model to be trustworthy, what common pitfalls predictive models run into, reproducibility issues, and what can be done. We chat about how subject area experts are expected to be many things: statisticians, computer scientists, and mathematicians, and how that can sometimes lead to mistakes. We also look at the COVID-19 pandemic and how data models affect decision-making.
https://www.imagwiki.nibib.nih.gov/ https://www.imagwiki.nibib.nih.gov/content/committee-credible-practice-modeling-simulation-healthcare-description https://www.biorxiv.org/content/10.1101/2020.08.07.239855v1 https://www.imagwiki.nibib.nih.gov/content/10-simple-rules-conformance-rubric
You can watch this episode on our Youtube Channel: https://youtube.com/c/BuildingBetterSystemsPodcast
Joey Dodds: https://galois.com/team/joey-dodds/
Shpat Morina: https://galois.com/team/shpat-morina/
Eric Davis: https://galois.com/team/eric-davis/
Galois, Inc.: https://galois.com/
Contact us: podcast@galois.com
The podcast Building Better Systems is embedded on this page from an open RSS feed. All files, descriptions, artwork and other metadata from the RSS-feed is the property of the podcast owner and not affiliated with or validated by Podplay.