Summary of Learning Explainable Treatment Policies with Clinician-informed Representations: a Practical Approach, by Johannes O. Ferstad et al.
Learning Explainable Treatment Policies with Clinician-Informed Representations: A Practical Approach
by Johannes O. Ferstad, Emily B. Fox, David Scheinker, Ramesh Johari
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Applications (stat.AP); Machine Learning (stat.ML)
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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 The paper proposes a novel pipeline for learning explainable treatment policies for digital health interventions (DHIs) enabled by remote patient monitoring (RPM). By incorporating clinical domain knowledge, the approach develops state and action representations that lead to more efficacious and efficient targeting policies. The authors apply their method in a real-world setting, improving glycemic control of youth with type 1 diabetes using an RPM-enabled DHI. Their key contribution highlights the importance of clinician-informed representations in developing effective DHIs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to make digital health interventions (DHIs) better by combining medical expertise with machine learning. It’s called remote patient monitoring (RPM). The authors try to improve DHIs for patients with type 1 diabetes, making sure they get the right treatment and care. They show that working together with doctors helps create policies that are more effective and efficient. |
Keywords
* Artificial intelligence * Machine learning
