# Machine learning-based algorithms for the prediction of 90-day survival in patients with liver failure receiving artificial liver therapy

## TL;DR

This study uses machine learning to predict 90-day survival in liver failure patients undergoing artificial liver therapy, aiming to improve individualized prognostic assessments.

## Contribution

The novel contribution is the development and comparison of machine learning models for predicting 90-day survival in liver failure patients using clinical data.

## Key findings

- Logistic regression achieved the highest predictive performance with an AUC of 0.884.
- Age, direct bilirubin, retinol, alpha-fetoprotein, and thrombin time were identified as independent predictors.
- Machine learning models showed promising performance for individualized prognostic assessment in liver failure patients.

## Abstract

Liver failure is associated with high short-term mortality, and the predictive value of clinical factors for patients undergoing artificial liver therapy is uncertain. We aim to develop prognostic models using several machine learning algorithms to predict 90-day survival in patients with liver failure undergoing artificial liver therapy.

We retrospectively enrolled hospitalized patients with liver failure who received artificial liver therapy in our center between December 2017 and December 2021. Prognostic characteristics were chosen by the least absolute shrinkage and selection operator (LASSO) regression and independent predictors by stepwise logistic regression analysis. Five machine learning algorithms—logistic regression (LR), random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and k-nearest neighbor (KNN)—were used to build and validate models to predict 90-day survival following Artificial liver support systems. The model performance was assessed by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.

A total of 197 patients were included in this study. LASSO regression, based on patient admission data, identified the top 15 prognostic features, and stepwise LR analysis determined that the age, direct bilirubin, retinol, alpha-fetoprotein, and thrombin time were independent predictors. Among the five machine learning models, LR achieved the highest predictive performance with an AUC of 0.884 and accuracy of 75.0%, followed by RF (AUC = 0.797), KNN (AUC = 0.788), XGBoost (AUC = 0.769), and SVM (AUC = 0.732). The predictive performance of LR models based on longitudinal data using patient characteristics from the day before treatment had an AUC of 0.869, and from the day after treatment, it had an AUC of 0.859.

Machine learning models showed promising performance in predicting 90-day survival in liver failure patients receiving artificial liver support therapy, potentially supporting individualized prognostic assessment.