Summary of Training and Validating a Treatment Recommender with Partial Verification Evidence, by Vishnu Unnikrishnan et al.
Training and Validating a Treatment Recommender with Partial Verification Evidence
by Vishnu Unnikrishnan, Clara Puga, Miro Schleicher, Uli Niemann, Berthod Langguth, Stefan Schoisswohl, Birgit Mazurek, Rilana Cima, Jose Antonio Lopez-Escamez, Dimitris Kikidis, Eleftheria Vellidou, Ruediger Pryss, Winfried Schlee, Myra Spiliopoulou
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Methodology (stat.ME)
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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 A novel approach is proposed for training and validating clinical decision support systems (DSS) using data from randomized clinical trials (RCTs), which are essential for evaluating the effectiveness of new treatments. The current DSS rely on observational data from target clinics, but this method fails to account for treatments validated in RCTs that have not yet been introduced in any clinic. To address missingness and verification evidence challenges, a multi-armed RCT with 240+ tinnitus patients is used as a dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to use data from RCTs to train and test decision support systems for treatments. Right now, these systems are trained on data from the clinics where they’ll be used, but this approach doesn’t account for new treatments that have been proven to work in studies, even if those treatments haven’t been introduced yet. The study uses a big dataset of patients with tinnitus to show how this can be done. |
