A Web-Based Decision Support System for Predicting Readmission of Pneumonia Patients after Discharge

Abstract

Patient readmission may be attributed to unsuccessful treatments, new diseases, or worsening comorbidities, which manifest degraded healthcare quality and increased cost of a hospital. Clinical decision support systems (CDSSs) provide useful information and expert knowledge to improve diagnostic performance, treatment outcomes, and healthcare quality in clinical settings. The objectives of this study is to design a CDSS based on the data retrieved from the National Health Insurance Research Database (NHIRD) for assisting physicians in the identification of high-risk patients of 30-day readmissions. A wrapper method integrating genetic algorithm (GA) and support vector machine (SVM) was used to designing the CDSSs by selecting salient features and designing the classifiers based on 2 under-sampling methods and 3 objective functions. The accuracy, sensitivity, specificity, and area under ROC curve (AUC) of the CDSSs achieve 69.33-71.44%, 66.27-69.41%, 69.32-72.24%, and 0.7518-0.7601, respectively, which outperform the models designed based on data retrieved from electronic medical records. An online web service system is provided to assist physicians for detecting potential pneumonia patients who have higher probability of all-cause readmission after being discharged from hospitals, thereby giving them necessary interventions to reduce morbidities and mortalities and to reduce healthcare cost.