Summary of How Should We Represent History in Interpretable Models Of Clinical Policies?, by Anton Matsson et al.
How Should We Represent History in Interpretable Models of Clinical Policies?
by Anton Matsson, Lena Stempfle, Yaochen Rao, Zachary R. Margolin, Heather J. Litman, Fredrik D. Johansson
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Applications (stat.AP)
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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 In this paper, researchers tackle the challenge of modeling clinical decision-making policies based on observational data. They focus on developing interpretable models that can accurately capture a patient’s state, either through sequence representation learning or carefully crafted summaries of medical history. The authors compare diverse approaches to summarizing patient history for interpretable policy modeling across four sequential decision-making tasks. They find that interpretable sequence models using learned representations perform well, while hand-crafted representations require incorporating recent and aggregated elements of patient history to be competitive. This work highlights the importance of evaluating policy models in the context of their intended use. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors make better decisions by creating computer models that can understand a patient’s medical history. The researchers want to know how to create these models so they can explain why certain treatments are given. They test different ways of representing a patient’s history and find that using learned representations is the best way to create accurate models. However, even with learned representations, it’s important to include recent and important information from a patient’s history. |
Keywords
» Artificial intelligence » Representation learning
