Summary of Clustering Of Disease Trajectories with Explainable Machine Learning: a Case Study on Postoperative Delirium Phenotypes, by Xiaochen Zheng et al.
Clustering of Disease Trajectories with Explainable Machine Learning: A Case Study on Postoperative Delirium Phenotypes
by Xiaochen Zheng, Manuel Schürch, Xingyu Chen, Maria Angeliki Komninou, Reto Schüpbach, Ahmed Allam, Jan Bartussek, Michael Krauthammer
First submitted to arxiv on: 6 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed approach combines machine learning for personalized risk prediction with unsupervised clustering techniques to identify distinct phenotypes in postoperative delirium (POD). This is achieved by training a predictive model and applying SHAP feature importance space, outperforming traditional clustering methods. The authors demonstrate their approach using synthetic data and real-world data from elderly surgical patients, showcasing the utility of this method in uncovering clinically relevant subtypes of complex disorders like POD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Identifying distinct phenotypes within postoperative delirium (POD) can help enhance our understanding of its pathogenesis and develop targeted prevention and treatment strategies. The authors propose a new approach that combines machine learning for personalized risk prediction with unsupervised clustering techniques to identify these phenotypes. They demonstrate this approach using both synthetic and real-world data, showing it can uncover clinically relevant subtypes of complex disorders like POD. |
Keywords
» Artificial intelligence » Clustering » Machine learning » Synthetic data » Unsupervised
