Sep 28, 2021 · In this work, we present a flexible method for explaining, in human readable terms, the predictions made by decision trees used as decision support in liver ...

Abstract. In this work, we present a flexible method for explaining, in human readable terms, the predictions made by decision trees used as.

Jul 28, 2021 · We aimed to build a risk predictor of post-LT AKI via supervised machine learning and visualize the mechanism driving within to assist clinical decision-making.

Sep 11, 2024 · In this work, we present a flexible method for explaining, in human readable terms, the predictions made by decision trees used as decision ...

This systematic review highlights the promising role of machine learning (ML) models in improving prognostication for liver transplantation (LT).

This article proposes a small samples-oriented intrinsically explainable machine learning model, an Optimal Variational Bayesian Logistic Regression (OVBLR) ...

AI tools have the potential to enhance personalised clinical decision-making, especially in the context of liver transplant medicine.

Oct 12, 2022 · Our analysis demonstrates that XGBoost can be an ideal method to assess the mortality risk in liver transplantation.

Apr 5, 2022 · In this work, we present a flexible method for explaining predictions made by decision trees in human-readable terms and we apply it to obtain ...

Jan 1, 2024 · The probability of readmission for liver transplantation patients was studied. •. The impact of significant features on readmission prediction ...