Summary of A Perspective on Individualized Treatment Effects Estimation From Time-series Health Data, by Ghadeer O. Ghosheh et al.
A Perspective on Individualized Treatment Effects Estimation from Time-series Health Data
by Ghadeer O. Ghosheh, Moritz Gögl, Tingting Zhu
First submitted to arxiv on: 7 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 In this paper, researchers aim to address the rising burden of diseases globally by developing methodologies for individualized treatment effects (ITE) estimation using time-series electronic health records (EHRs). Current machine-learning-driven ITE models focus on tabular data, leaving a gap in understanding and reviewing methodologies proposed for EHRs. The authors provide an overview of recent literature, discussing theoretical assumptions, treatment settings, and computational frameworks. They also highlight challenges and future research directions for ITEs in time-series settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve the big problem of unequal healthcare treatment by studying how different people respond to medicine. Right now, doctors use a one-size-fits-all approach that doesn’t always work best for each patient. To make personalized medicine a reality, researchers need to develop new methods using medical records from patients over time. This paper looks at what’s been done so far and where we might go next in this exciting area of research. |
Keywords
* Artificial intelligence * Machine learning * Time series
