Early Deterioration Warning for Hospitalized Patients by Mining Clinical Data

Abstract

Data mining on medical data has great potential to improve the treatment quality of hospitals and increase the survival rate of patients. Every year, 4-17% of patients undergo cardiopulmonary or respiratory arrest while in hospitals. Clinical study has found early detection and intervention to be essential for preventing clinical deterioration in patients at general hospital units. This paper proposes an early warning system (EWS) designed to identify the signs of clinical deterioration and provide early warning for serious clinical events. The EWS is designed to provide reliable early alarms for patients at the general hospital wards (GHWs). The main task of EWS is a challenging classification problem on high-dimensional stream data with irregular, multi-scale data gaps, measurement errors, outliers, and class imbalance. This paper proposes a novel data mining framework for analyzing such medical data streams. The authors assess the feasibility of the proposed EWS approach through retrospective study that includes data from 41,503 visits at a major hospital. Finally, the system is applied in a clinical trial at a major hospital and obtains promising results. This project is an example of multidisciplinary cyber-physical systems involving researchers in clinical science, data mining, and nursing staff.