Stroke patients tend to suffer from immobility, which increases the possibility of post-stroke complications. Urinary tract infections (UTIs) are one of the complications as an independent predictor of poor prognosis of stroke patients. However, the incidence of new UTIs onsets during hospitalization was rare in most datasets with a prevalence of 4%. This imbalanced data distribution sets obstacles to establishing an accurate prediction model. Our study aimed to develop an effective prediction model to identify UTIs risk in immobile stroke patients, and (2) to compare its prediction performance with traditional machine learning models. We tackled this problem by building a Siamese Network leveraging commonly used clinical features to identifying patients with UTIs risk. Model derivation and validation were based on a nationwide dataset including 3982 Chinese patients. Results showed that the Siamese Network performed better than traditional machine learning models in imbalanced datasets (Sensitivity: 0.810; AUC: 0.828).
Keywords: Immobility; Risk Prediction; Siamese Network; Stroke; Urinary Tract Infections.