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Counterfactual Explanations for Survival Prediction of Cardiovascular ICU Patients

Part of the Lecture Notes in Computer Science book series (LNAI,volume 12721)


In recent years, machine learning methods have been rapidly implemented in the medical domain. However, current state-of-the-art methods usually produce opaque, black-box models. To address the lack of model transparency, substantial attention has been given to develop interpretable machine learning methods. In the medical domain, counterfactuals can provide example-based explanations for predictions, and show practitioners the modifications required to change a prediction from an undesired to a desired state. In this paper, we propose a counterfactual explanation solution for predicting the survival of cardiovascular ICU patients, by representing their electronic health record as a sequence of medical events, and generating counterfactuals by adopting and employing a text style-transfer technique. Experimental results on the MIMIC-III dataset strongly suggest that text style-transfer methods can be effectively adapted for the problem of counterfactual explanations in healthcare applications and can achieve competitive performance in terms of counterfactual validity, BLEU-4 and local outlier metrics.


  • Counterfactual explanations
  • Survival prediction
  • Explainable models
  • Deep learning

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  • DOI: 10.1007/978-3-030-77211-6_38
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This work was supported in part the EXTREMUM collaborative project of the Digital Futures framework.

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Correspondence to Zhendong Wang .

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Wang, Z., Samsten, I., Papapetrou, P. (2021). Counterfactual Explanations for Survival Prediction of Cardiovascular ICU Patients. In: Tucker, A., Henriques Abreu, P., Cardoso, J., Pereira Rodrigues, P., Riaño, D. (eds) Artificial Intelligence in Medicine. AIME 2021. Lecture Notes in Computer Science(), vol 12721. Springer, Cham.

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