Summary of Empirical Investigation Of Multi-source Cross-validation in Clinical Ecg Classification, by Tuija Leinonen et al.
Empirical investigation of multi-source cross-validation in clinical ECG classification
by Tuija Leinonen, David Wong, Antti Vasankari, Ali Wahab, Ramesh Nadarajah, Matti Kaisti, Antti Airola
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposed paper develops a novel approach to evaluating machine learning-based clinical prediction models on patient data from multiple sources. By adopting a source-level cross-validation design, the researchers demonstrate how traditional single-source training and evaluation methods can lead to overoptimistic estimates of model accuracy. The study highlights the importance of considering the generalizability of models across different medical settings, leveraging the increasing availability of multi-source datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to predict patient outcomes using a machine learning model trained on data from one hospital. Sounds good, right? But what happens when you try to use that same model at another hospital with patients who are slightly different? The results might not be as accurate as you thought! This paper explores how to make better predictions by training models on data from multiple hospitals and evaluating their performance across all those sources. |
Keywords
* Artificial intelligence * Machine learning
