Longitudinal patient stratification of electronic health records with flexible adjustment for clinical outcomes

Oliver Carr, Avelino Javer, Patrick Rockenschaub, Owen Parsons, Robert Durichen
Proceedings of Machine Learning for Health, PMLR 158:220-238, 2021.

Abstract

The increase in availability of longitudinal EHR data is leading to improved understanding of diseases and discovery of novel phenotypes. The majority of clustering algorithms focus only on patient trajectories, yet patients with similar trajectories may have different outcomes. Finding subgroups of patients with different trajectories and outcomes can guide future drug development and improve recruitment to clinical trials. We develop a recurrent neural network autoencoder to cluster EHR data using reconstruction, outcome, and clustering losses which can be weighted to find different types of patient clusters. We show our model is able to discover known clusters from both data biases and outcome differences, outperforming baseline models. We demonstrate the model performance on 29,229 diabetes patients, showing it finds clusters of patients with both different trajectories and different outcomes which can be utilized to aid clinical decision making.

Cite this Paper

BibTeX
@InProceedings{pmlr-v158-carr21a, title = {Longitudinal patient stratification of electronic health records with flexible adjustment for clinical outcomes}, author = {Carr, Oliver and Javer, Avelino and Rockenschaub, Patrick and Parsons, Owen and Durichen, Robert}, booktitle = {Proceedings of Machine Learning for Health}, pages = {220--238}, year = {2021}, editor = {Roy, Subhrajit and Pfohl, Stephen and Rocheteau, Emma and Tadesse, Girmaw Abebe and Oala, Luis and Falck, Fabian and Zhou, Yuyin and Shen, Liyue and Zamzmi, Ghada and Mugambi, Purity and Zirikly, Ayah and McDermott, Matthew B. A. and Alsentzer, Emily}, volume = {158}, series = {Proceedings of Machine Learning Research}, month = {04 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v158/carr21a/carr21a.pdf}, url = {https://proceedings.mlr.press/v158/carr21a.html}, abstract = {The increase in availability of longitudinal EHR data is leading to improved understanding of diseases and discovery of novel phenotypes. The majority of clustering algorithms focus only on patient trajectories, yet patients with similar trajectories may have different outcomes. Finding subgroups of patients with different trajectories and outcomes can guide future drug development and improve recruitment to clinical trials. We develop a recurrent neural network autoencoder to cluster EHR data using reconstruction, outcome, and clustering losses which can be weighted to find different types of patient clusters. We show our model is able to discover known clusters from both data biases and outcome differences, outperforming baseline models. We demonstrate the model performance on 29,229 diabetes patients, showing it finds clusters of patients with both different trajectories and different outcomes which can be utilized to aid clinical decision making.} }
Endnote
%0 Conference Paper %T Longitudinal patient stratification of electronic health records with flexible adjustment for clinical outcomes %A Oliver Carr %A Avelino Javer %A Patrick Rockenschaub %A Owen Parsons %A Robert Durichen %B Proceedings of Machine Learning for Health %C Proceedings of Machine Learning Research %D 2021 %E Subhrajit Roy %E Stephen Pfohl %E Emma Rocheteau %E Girmaw Abebe Tadesse %E Luis Oala %E Fabian Falck %E Yuyin Zhou %E Liyue Shen %E Ghada Zamzmi %E Purity Mugambi %E Ayah Zirikly %E Matthew B. A. McDermott %E Emily Alsentzer %F pmlr-v158-carr21a %I PMLR %P 220--238 %U https://proceedings.mlr.press/v158/carr21a.html %V 158 %X The increase in availability of longitudinal EHR data is leading to improved understanding of diseases and discovery of novel phenotypes. The majority of clustering algorithms focus only on patient trajectories, yet patients with similar trajectories may have different outcomes. Finding subgroups of patients with different trajectories and outcomes can guide future drug development and improve recruitment to clinical trials. We develop a recurrent neural network autoencoder to cluster EHR data using reconstruction, outcome, and clustering losses which can be weighted to find different types of patient clusters. We show our model is able to discover known clusters from both data biases and outcome differences, outperforming baseline models. We demonstrate the model performance on 29,229 diabetes patients, showing it finds clusters of patients with both different trajectories and different outcomes which can be utilized to aid clinical decision making.
APA
Carr, O., Javer, A., Rockenschaub, P., Parsons, O. & Durichen, R.. (2021). Longitudinal patient stratification of electronic health records with flexible adjustment for clinical outcomes. Proceedings of Machine Learning for Health, in Proceedings of Machine Learning Research 158:220-238 Available from https://proceedings.mlr.press/v158/carr21a.html.

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