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Lisboa, PJG, Jayabalan, M, Ortega-Martorell, S, Olier, I, Medved, D and Nilsson, J (2022) Enhanced survival prediction using explainable artificial intelligence in heart transplantation. Scientific Reports, 12 (1). ISSN 2045-2322
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Explainable_risk_Prediction_heart_transplant_2022.pdf - Published Version Available under License Creative Commons Attribution. Download (3MB) | Preview |
Abstract
The most limiting factor in heart transplantation is the lack of donor organs. With enhanced prediction of outcome, it may be possible to increase the life-years from the organs that become available. Applications of machine learning to tabular data, typical of clinical decision support, pose the practical question of interpretation, which has technical and potential ethical implications. In particular, there is an issue of principle about the predictability of complex data and whether this is inherent in the data or strongly dependent on the choice of machine learning model, leading to the so-called accuracy-interpretability trade-off. We model 1-year mortality in heart transplantation data with a self-explaining neural network, which is benchmarked against a deep learning model on the same development data, in an external validation study with two data sets: (1) UNOS transplants in 2017-2018 (n = 4750) for which the self-explaining and deep learning models are comparable in their AUROC 0.628 [0.602,0.654] cf. 0.635 [0.609,0.662] and (2) Scandinavian transplants during 1997-2018 (n = 2293), showing good calibration with AUROCs of 0.626 [0.588,0.665] and 0.634 [0.570, 0.698], respectively, with and without missing data (n = 982). This shows that for tabular data, predictive models can be transparent and capture important nonlinearities, retaining full predictive performance.
| Item Type: | Article |
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| Uncontrolled Keywords: | Artificial Intelligence; Retrospective Studies; Machine Learning; Neural Networks, Computer; Heart Transplantation |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software R Medicine > R Medicine (General) |
| Divisions: | Computer Science & Mathematics |
| Publisher: | Nature Publishing Group |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 25 Nov 2022 12:22 |
| Last Modified: | 25 Nov 2022 12:30 |
| DOI or ID number: | 10.1038/s41598-022-23817-2 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/18222 |
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