Summary of How Consistent Are Clinicians? Evaluating the Predictability Of Sepsis Disease Progression with Dynamics Models, by Unnseo Park et al.
How Consistent are Clinicians? Evaluating the Predictability of Sepsis Disease Progression with Dynamics Models
by Unnseo Park, Venkatesh Sivaraman, Adam Perer
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Human-Computer Interaction (cs.HC)
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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 Machine learning educators can summarize the abstract as follows: Reinforcement learning (RL) has shown promise in generating treatment policies for sepsis patients, with retrospective evaluation metrics indicating decreased mortality. However, studies suggest that clinicians often ignore RL recommendations due to perceived spuriousness. The authors propose that this may be due to lack of diversity in training data and construct experiments to investigate predicting disease severity changes due to clinician actions. Preliminary results indicate that incorporating action information does not significantly improve model performance, suggesting that clinician actions may not yield measurable effects on disease progression. This has implications for optimizing sepsis treatment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sepsis patients in intensive care need better treatment policies. Reinforcement learning (RL) tries to create these policies by looking at what happened in the past. However, doctors don’t always follow RL’s recommendations because they seem unrealistic. The authors think this is because the training data isn’t diverse enough and that’s why it doesn’t work well. They did some tests to see if adding more information about doctor actions would help. So far, it hasn’t made a big difference. This means we need to rethink how we create better treatment plans for sepsis patients. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
