Interpretable deep learning for anticipating length of stay in Wuhan: a comparative analysis before and during the COVID-19 pandemic

Purpose The COVID-19 pandemic has imposed unprecedented strain on healthcare systems worldwide, largely due to significant and unpredictable fluctuations in patients' length of stay (LOS) during hospitalization. Interpretable deep learning techniques have emerged as promising tools for accurately predicting LOS and thereby optimizing service levels and resource allocation. Design/methodology/approach In this study, we extracted electronic health record (EHR) data from a tertiary hospital in Wuhan for COVID-19 patients, who were stratified into three groups according to their LOS: short (<3 days), intermediate (3–5 days) and long (>5 days). We applied nine predictive algorithms, including three conventional machine learning methods – logistic regression (LR), support vector classification (SVC) and decision tree (DT) – and three deep learning models – AutoInt (AU), xDeepFM (XD) and FiBiNet (FB). To enhance model transparency and clinical applicability, we employed Shapley Additive Explanations (SHAP) to interpret model outputs and identify key variables influencing LOS. Findings Our results revealed several clinical and demographic variables significantly associated with LOS. Among all tested models, the FiBiNet (FB) model achieved the best predictive performance, with an average improvement of approximately 3% over other models. Compared with traditional machine learning methods, deep learning models demonstrated an average performance gain of 1.7%. Interpretable analysis further revealed that medical insurance status was a crucial determinant of LOS, particularly in differentiating between patients with household and non-household registration in Wuhan. Originality/value To the best of the authors’ knowledge, this study is among the first to integrate interpretable deep learning methods with EHR data to predict LOS for COVID-19 patients in Wuhan. By identifying medical insurance status and self-financing as key predictors, our findings offer actionable insights for hospital administrators seeking to optimize medical resource allocation. The results contribute to strengthening China's healthcare service system and support ongoing efforts in medical and healthcare reform.