Summary of Stronger Baseline Models — a Key Requirement For Aligning Machine Learning Research with Clinical Utility, by Nathan Wolfrath et al.
Stronger Baseline Models – A Key Requirement for Aligning Machine Learning Research with Clinical Utility
by Nathan Wolfrath, Joel Wolfrath, Hengrui Hu, Anjishnu Banerjee, Anai N. Kothari
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the challenges of deploying Machine Learning (ML) models in high-stakes, clinical settings. Despite predictive modeling’s success across various domains, several barriers exist, including lack of model transparency, large training data requirements, and complicated metrics for measuring model utility. The authors empirically show that including stronger baseline models in healthcare ML evaluations has important downstream effects that aid practitioners in addressing these challenges. Through case studies, they find that omitting baselines or comparing against a weak baseline model obscures the value of proposed ML methods. The paper proposes best practices to enable effective study and deployment of ML models in clinical settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine Learning models are great at making predictions, but it’s hard to use them in hospitals because they’re not transparent enough, need too much data, and it’s hard to measure how well they work. Researchers often compare their new model to a very simple one that isn’t really doing anything good, which makes it seem like their new model is amazing when it’s not. This paper shows why this is a problem and proposes some ways to make it better so we can use ML models in hospitals more effectively. |
Keywords
* Artificial intelligence * Machine learning
