Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR

Ran Xu, Yue Yu, Chao Zhang, Mohammed K Ali, Joyce C Ho, Carl Yang
Proceedings of the 2nd Machine Learning for Health symposium, PMLR 193:259-278, 2022.

Abstract

Electronic Health Record modeling is crucial for digital medicine. However, existing models ignore higher-order interactions among medical codes and their causal relations towards downstream clinical predictions. To address such limitations, we propose a novel framework CACHE, to provide effective and insightful clinical predictions based on hypergraph representation learning and counterfactual and factual reasoning techniques. Experiments on two real EHR datasets show the superior performance of CACHE. Case studies with a domain expert illustrate a preferred capability of CACHE in generating clinically meaningful interpretations towards the correct predictions.

Cite this Paper

BibTeX
@InProceedings{pmlr-v193-xu22a, title = {Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR}, author = {Xu, Ran and Yu, Yue and Zhang, Chao and Ali, Mohammed K and Ho, Joyce C and Yang, Carl}, booktitle = {Proceedings of the 2nd Machine Learning for Health symposium}, pages = {259--278}, year = {2022}, editor = {Parziale, Antonio and Agrawal, Monica and Joshi, Shalmali and Chen, Irene Y. and Tang, Shengpu and Oala, Luis and Subbaswamy, Adarsh}, volume = {193}, series = {Proceedings of Machine Learning Research}, month = {28 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v193/xu22a/xu22a.pdf}, url = {https://proceedings.mlr.press/v193/xu22a.html}, abstract = {Electronic Health Record modeling is crucial for digital medicine. However, existing models ignore higher-order interactions among medical codes and their causal relations towards downstream clinical predictions. To address such limitations, we propose a novel framework CACHE, to provide effective and insightful clinical predictions based on hypergraph representation learning and counterfactual and factual reasoning techniques. Experiments on two real EHR datasets show the superior performance of CACHE. Case studies with a domain expert illustrate a preferred capability of CACHE in generating clinically meaningful interpretations towards the correct predictions.} }
Endnote
%0 Conference Paper %T Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR %A Ran Xu %A Yue Yu %A Chao Zhang %A Mohammed K Ali %A Joyce C Ho %A Carl Yang %B Proceedings of the 2nd Machine Learning for Health symposium %C Proceedings of Machine Learning Research %D 2022 %E Antonio Parziale %E Monica Agrawal %E Shalmali Joshi %E Irene Y. Chen %E Shengpu Tang %E Luis Oala %E Adarsh Subbaswamy %F pmlr-v193-xu22a %I PMLR %P 259--278 %U https://proceedings.mlr.press/v193/xu22a.html %V 193 %X Electronic Health Record modeling is crucial for digital medicine. However, existing models ignore higher-order interactions among medical codes and their causal relations towards downstream clinical predictions. To address such limitations, we propose a novel framework CACHE, to provide effective and insightful clinical predictions based on hypergraph representation learning and counterfactual and factual reasoning techniques. Experiments on two real EHR datasets show the superior performance of CACHE. Case studies with a domain expert illustrate a preferred capability of CACHE in generating clinically meaningful interpretations towards the correct predictions.
APA
Xu, R., Yu, Y., Zhang, C., Ali, M.K., Ho, J.C. & Yang, C.. (2022). Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR. Proceedings of the 2nd Machine Learning for Health symposium, in Proceedings of Machine Learning Research 193:259-278 Available from https://proceedings.mlr.press/v193/xu22a.html.

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