Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: machine learning model development and evaluation

Abstract

Abstract Background Predicting the length of stay in advance will not only benefit the hospitals both clinically and financially but enable healthcare providers to better decision-making for improved quality of care. More importantly, understanding the length of stay of severe patients who require general anesthesia is key to enhancing health outcomes. Objective Here, we aim to discover how machine learning can support resource allocation management and decision-making resulting from the length of stay prediction. Methods A retrospective cohort study was conducted from January 2018 to October 2020. A total cohort of 240,000 patients’ medical records was collected. The data were collected exclusively for preoperative variables to accurately analyze the predictive factors impacting the postoperative length of stay. The main outcome of this study is an analysis of the length of stay (in days) after surgery until discharge. The prediction was performed with ridge regression, random forest, XGBoost, and multi-layer perceptron neural network models. Results The XGBoost resulted in the best performance with an average error within 3 days. Moreover, we explain each feature’s contribution over the XGBoost model and further display distinct predictors affecting the overall prediction outcome at the patient level. The risk factors that most importantly contributed to the stay after surgery were as follows: a direct bilirubin laboratory test, department change, calcium chloride medication, gender, and diagnosis with the removal of other organs. Our results suggest that healthcare providers take into account the risk factors such as the laboratory blood test, distributing patients, and the medication prescribed prior to the surgery. Conclusion We successfully predicted the length of stay after surgery and provide explainable models with supporting analyses. In summary, we demonstrate the interpretation with the XGBoost model presenting insights on preoperative features and defining higher risk predictors to the length of stay outcome. Our development in explainable models supports the current in-depth knowledge for the future length of stay prediction on electronic medical records that aids the decision-making and facilitation of the operation department.

Keywords

• Machine learning
• Healthcare management
• Predictive analysis
• Decision support system