Predicting ICU Stay Duration for Hemodialysis Patients with Poisoning: A Study Comparing Deep Learning with Machine Learning Models

Abstract

Objective: This study compares the effectiveness of machine learning and deep learning models in predicting hemodialysis patients’ length of stay in the intensive care unit (ICU). Methods: This retrospective cohort study used data from 980 poisoned patients undergoing hemodialysis. A variety of eight well-known machine learning [support vector machine, extreme gradient boosting, random forest (RF), decision tree] and deep learning (deep neural network, feedforward neural network, long short-term memory, convolutional neural network) models were employed. Results: Feature importance analyses using Shapley Additive exPlanation and local interpretable model-agnostic explanation methodologies identified Glasgow Coma Scale (GCS <8), intubation, acute kidney injury, PO2, blood urea nitrogen, metabolic acidosis, and number of hemodialysis sessions as key predictors of ICU stay duration in poisoned hemodialysis patients, with intubation score, GCS score, and ICU admission type being the most significant predictors. Overall, the RF model displayed exceptional performance across various metrics. Conclusion: Our findings emphasize the importance of neurological status, respiratory function, and renal injury in predicting ICU duration, offering valuable insights for clinical decision-making and resource allocation in this high-risk population.