Abstract
Lung transplantation is a critical procedure performed in end-stage pulmonary patients. The number of lung transplantations performed in the USA in the last decade has been rising, but the survival rate is still lower than that of other solid organ transplantations. First, this study aims to employ machine learning models to predict patient survival after lung transplantation. Additionally, the aim is to generate counterfactual explanations based on these predictions to help clinicians and patients understand the changes needed to increase the probability of survival after the transplantation and better comply with normative requirements. We use data derived from the UNOS database, particularly the lung transplantations performed in the USA between 2019 and 2021. We formulate the problem and define two data representations, with the first being a representation that describes only the lung recipients and the second the recipients and donors. We propose an explainable ML workflow for predicting patient survival after lung transplantation. We evaluate the workflow based on various performance metrics, using five classification models and two counterfactual generation methods. Finally, we demonstrate the potential of explainable ML for resource allocation, predicting patient mortality, and generating explainable predictions for lung transplantation.
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Acknowledgements
This work was supported in part by the Health Resources and Services Administration contract 234-2005-370011C, the Digital Futures EXTREMUM project on “Explainable and Ethical Machine Learning for Knowledge Discovery from Medical Data Sources”, as well as by the Horizon2020 ASME project on “Using Artificial Intelligence for Predicting the Treatment Outcome of Melanoma Patients”.
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Rugolon, F., Bampa, M., Papapetrou, P. (2023). A Workflow for Generating Patient Counterfactuals in Lung Transplant Recipients. In: Koprinska, I., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science, vol 1753. Springer, Cham. https://doi.org/10.1007/978-3-031-23633-4_20
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