Machine learning: Predicting hospital length of stay in patients admitted for lupus flares

Abstract

Background Although rare, severe systemic lupus erythematosus (SLE) flares requiring hospitalization account for most of the direct costs of SLE care. New machine learning (ML) methods may optimize lupus care by predicting which patients will have a prolonged hospital length of stay (LOS). Our study uses a machine learning approach to predict the LOS in patients admitted for lupus flares and assesses which features prolong LOS. Methods Our study sampled 5831 patients admitted for lupus flares from the National Inpatient Sample Database 2016–2018 and collected 90 demographics and comorbidity features. Four machine learning (ML) models were built (XGBoost, Linear Support Vector Machines, K Nearest Neighbors, and Logistic Regression) to predict LOS, and their performance was evaluated using multiple metrics, including accuracy, receiver operator area under the curve (ROC-AUC), precision-recall area under the curve (PR- AUC), and F1-score. Using the highest-performing model (XGBoost), we assessed the feature importance of our input features using Shapley value explanations (SHAP) to rank their impact on LOS. Results Our XGB model performed the best with a ROC-AUC of 0.87, PR-AUC of 0.61, an F1 score of 0.56, and an accuracy of 95%. The features with the most significant impact on the model were “the need for a central line,” “acute dialysis,” and “acute renal failure.” Other top features include those related to renal and infectious comorbidities. Conclusion Our results were consistent with the established literature and showed promise in ML over traditional methods of predictive analyses, even with rare rheumatic events such as lupus flare hospitalizations.

Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospective study

“…Extreme Gradient Boosting (XGB) Algorithm is a machine learning one which can improve the integration of multiple decision trees by gradient boosting method, which is characterized by high accuracy, difficulty of adaptation and good scalability. Chen et al ( 24) indicated that the XGB algorithm was a suitable ML model for predicting length of hospital stay in ischemic stroke patients with high accuracy, less over-fitting and scalability and Grovu et al (35) showed that the XGB model has higher accuracy and better performance in predicting the length of hospitalization compared with other algorithms. Morgan et al (36) indicted that the accuracy and stability of ML models was improved and presented less over-fitting as the sample size increased and a more stable ML model in training and testing sets was necessary to be selected in clinical practice.…”

Systemic lupus in the era of machine learning medicine

“…Most of these larger reports used EMRs and administrative databases to identify patients with SLE, recognising that these types of data may be limited Review by diagnostic misclassification. [17][18][19][20][21] Many reports experienced 'class imbalance', where the SLE group sample size was considerably smaller compared with healthy controls, potentially biasing ML in favour of the more prevalent class. To address this, some reports used generative adversarial networks 22 23 and Synthetic Minority Oversampling TEchnique (SMOTE) 20 24 to generate synthetic data.…”