Summary of Tee4ehr: Transformer Event Encoder For Better Representation Learning in Electronic Health Records, by Hojjat Karami et al.
TEE4EHR: Transformer Event Encoder for Better Representation Learning in Electronic Health Records
by Hojjat Karami, David Atienza, Anisoara Ionescu
First submitted to arxiv on: 9 Feb 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 This paper addresses a crucial challenge in developing machine learning models for electronic health records (EHRs): irregular sampling of time series data. The authors propose TEE4EHR, a transformer event encoder that incorporates point process loss to capture the patterns of laboratory tests in EHRs. They demonstrate the utility of their model in various benchmark datasets and conduct experiments on real-world EHR databases. The results show that TEE4EHR outperforms existing models for handling irregularly sampled time series data, improving representation learning and clinical prediction tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research solves a big problem with using electronic health records (EHRs) to develop machine learning models: missing data patterns. The authors create a new model called TEE4EHR that helps analyze EHR data more accurately. They test their model on real-world datasets and show it works better than other methods for predicting future events and making accurate predictions about patient outcomes. |
Keywords
* Artificial intelligence * Encoder * Machine learning * Representation learning * Time series * Transformer
