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Machine Learning in the Prediction of Costs for Liver Transplantation

Background and aim: Liver transplant is the most effective therapeutic option for patients with end-stage liver disease. The objective of this study is to develop a predictive model of costs after liver transplantation through machine learning using data obtained from the Nationwide Inpatient Sample Database.

Methods: We used the Nationwide Inpatient Sample (NIS) database, evaluating data from patients undergoing a liver transplantation procedure for the years 2011 (model training) and 2012 (model validation). Predictors of the total cost (using cost-to-charge ratios), total charges, and length of stay (LOS) were assessed using a combination of machine learning and tree regression models.

Results: A total of 2,274 individual patients met our inclusion criteria, 1,090 patients for the year 2011 and 1,184 for 2012. The most important variables predicting cost and LOS were consistent across all models and included the Charlson and Van Walraven comorbidity scores. The best performing model predicting total cost was Support Vector Machine with Linear Kernel with root mean square error (RMSE) values of 0.561 whereas for LOS was the Principal Component Analysis (RMSE=0.743). When evaluating predictors of total cost and LOS, Van Walraven score >26.5 constituted cost-drivers with an average total cost of 207,041 US dollars whereas scores ranging from 21.5-26.4 were associated with a mean increase in the LOS of 26 days.

Conclusion: Patient co-morbidities are major drivers of transplants costs, charges and LOS. Machine learning models allow for cost prediction of individual patients, thus allowing for better healthcare management and policy making.


Luciana Bertocco de Paiva Haddad, Luana Regina Baratelli Carelli Mendes, Liliana Ducatti, Vinicius Rocha-Santos, Wellington Andraus, Luiz Augusto Carneiro D'Albuquerque

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