A perspective on individualized treatment effects estimation from time-series health data

This work provides a comprehensive overview of ITE estimation for time-series EHR data, offering insights into the current state of the field and identifying future research directions. By addressing the limitations and challenges, we hope to encourage further exploration and innovation in this exciting and under-studied area of personalized medicine.

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Publication Details

Journal
Journal of the American Medical Informatics Association
Published
2024-12-28
DOI
https://doi.org/10.1093/jamia/ocae323
Citations
2
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
1.42

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article

A perspective on individualized treatment effects estimation from time-series health data

Ghadeer Ghosheh, Tingting Zhu, Moritz Gögl
2 citations
Journal of the American Medical Informatics Association
Advanced Causal Inference Techniques
1.42
article

A perspective on individualized treatment effects estimation from time-series health data

Ghadeer Ghosheh, Tingting Zhu, Moritz Gögl
article en
2 citations

Abstract

This work provides a comprehensive overview of ITE estimation for time-series EHR data, offering insights into the current state of the field and identifying future research directions. By addressing the limitations and challenges, we hope to encourage further exploration and innovation in this exciting and under-studied area of personalized medicine.

Journal of the American Medical Informatics AssociationVol. 33(1)
University of Oxford (GB)
UK Research and Innovation
Industry, innovation and infrastructure
Openalex Percentile: Top 15%
Advanced Causal Inference Techniques
1.42
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