From paper to patient, Pacmed’s AI newsletter #1: handling electronic health records

Helping doctors and data scientists get a clear view on the latest developments in medical AI

# 1: A general overview

Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review
By: Cao Xiao, Edward Choi, Jimeng Sun
https://doi.org/10.1093/jamia/ocy068

#2: Producing synthetic data

Generating Multi-label Discrete Patient Records using Generative Adversarial Networks
By: Edward Choi, Siddharth Biswal, Bradley Malin, et al.
https://arxiv.org/abs/1703.06490

#3: Deep learning for scalable models

Scalable and accurate deep learning with electronic health records
By: Alvin Rajkomar, Eyal Oren, Kai Chen, et al.
https://www.nature.com/articles/s41746-018-0029-1

#4: Learning personalized data representations

Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record
By: Jinghe Zhang, Kamran Kowsari, James H. Harrison, et al.
https://doi.org/10.1109/ACCESS.2018.2875677

#5: Learning from suboptimal clinical decisions

The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care
By: Matthieu Komorowski, Leo A. Celi, Omar Badawi, et al.
https://www.nature.com/articles/s41591-018-0213-5

References

[1] Xiao C, Choi E, Sun J. Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. Journal of the American Medical Informatics Association. 2018 Jun 8.

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