Best of both worlds: Combining predictive power with interpretable and explainable results for patient pathway prediction

Zilker S, Weinzierl S, Zschech P, Kraus M, Matzner M (2023)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2023

Publisher: AISeL

Pages Range: 1-16

Conference Proceedings Title: Proceedings of the 31st European Conference on Information Systems

Event location: Kristiansand

Abstract

Proactively analyzing patient pathways can help healthcare providers to anticipate treatment-related risks, detect undesired outcomes, and allocate resources quickly. For this purpose, modern methods from the field of predictive business process monitoring can be applied to create data-driven models that capture patterns from past behavior to provide predictions about running process instances. Recent methods increasingly focus on deep neural networks (DNN) due to their superior prediction performances and their independence from process knowledge. However, DNNs generally have the disadvantage of showing black-box characteristics, which hampers dissemination in critical environments such as healthcare. To this end, we propose the design of HIXPred, a novel artifact combining predictive power with explainable results for patient pathway predictions. We instantiate HIXPred and apply it to a real-life healthcare use case for evaluation and demonstration purposes and conduct interviews with medical experts. Our results confirm high predictive performance while ensuring sufficient interpretability and explainability to provide comprehensible decision support.

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How to cite

APA:

Zilker, S., Weinzierl, S., Zschech, P., Kraus, M., & Matzner, M. (2023). Best of both worlds: Combining predictive power with interpretable and explainable results for patient pathway prediction. In Proceedings of the 31st European Conference on Information Systems (pp. 1-16). Kristiansand, NO: AISeL.

MLA:

Zilker, Sandra, et al. "Best of both worlds: Combining predictive power with interpretable and explainable results for patient pathway prediction." Proceedings of the European Conference on Information Systems, Kristiansand AISeL, 2023. 1-16.

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