This study introduces a novel approach to mine risk factors for short-term death after liver transplantation (LT). The method outputs intelligible survival models by combining Cox's regression with a genetic programming technique known as multi-objective symbolic regression (MOSR). We consider 485 Electronic Health Records (EHRs) of patients who underwent LT, containing information on hospitalization and preoperative conditions, with a focus on infections and colonizations by multi-resistant Gram-negative bacteria. We evaluate MOSR outcomes against several performance metrics and demonstrate that they are well-calibrated, predictive, safe, and parsimonious. Finally, we select the most promising post-LT early survival risk score based on information criteria, performance, and out-of-distribution safety. Validating this technique at a multicenter level could improve service pipeline logistics through a trustworthy machine-learning method.

Death After Liver Transplantation: Mining Interpretable Risk Factors for Survival Prediction / Guidetti, V.; Dolci, G.; Franceschini, E.; Bacca, E.; Burastero, G. J.; Ferrari, D.; Serra, V.; Di Benedetto, F.; Mussini, C.; Mandreoli, F.. - (2023), pp. -10. (Intervento presentato al convegno 10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023 tenutosi a grc nel 2023) [10.1109/DSAA60987.2023.10302622].

Death After Liver Transplantation: Mining Interpretable Risk Factors for Survival Prediction

Guidetti V.;Dolci G.;Franceschini E.;Bacca E.;Serra V.;Di Benedetto F.;Mussini C.;Mandreoli F.
2023

Abstract

This study introduces a novel approach to mine risk factors for short-term death after liver transplantation (LT). The method outputs intelligible survival models by combining Cox's regression with a genetic programming technique known as multi-objective symbolic regression (MOSR). We consider 485 Electronic Health Records (EHRs) of patients who underwent LT, containing information on hospitalization and preoperative conditions, with a focus on infections and colonizations by multi-resistant Gram-negative bacteria. We evaluate MOSR outcomes against several performance metrics and demonstrate that they are well-calibrated, predictive, safe, and parsimonious. Finally, we select the most promising post-LT early survival risk score based on information criteria, performance, and out-of-distribution safety. Validating this technique at a multicenter level could improve service pipeline logistics through a trustworthy machine-learning method.
2023
10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023
grc
2023
10
Guidetti, V.; Dolci, G.; Franceschini, E.; Bacca, E.; Burastero, G. J.; Ferrari, D.; Serra, V.; Di Benedetto, F.; Mussini, C.; Mandreoli, F.
Death After Liver Transplantation: Mining Interpretable Risk Factors for Survival Prediction / Guidetti, V.; Dolci, G.; Franceschini, E.; Bacca, E.; Burastero, G. J.; Ferrari, D.; Serra, V.; Di Benedetto, F.; Mussini, C.; Mandreoli, F.. - (2023), pp. -10. (Intervento presentato al convegno 10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023 tenutosi a grc nel 2023) [10.1109/DSAA60987.2023.10302622].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1332832
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