Early Deterioration Warning for Hospitalized Patients by Mining Clinical Data

Early Deterioration Warning for Hospitalized Patients by Mining Clinical Data

Yi Mao (Washington University in St. Louis, USA and Xidian University, China), Yixin Chen (Washington University in St. Louis, USA), Gregory Hackmann (Washington University in St. Louis, USA), Minmin Chen (Washington University in St. Louis, USA), Chenyang Lu (Washington University in St. Louis, USA), Marin Kollef (Washington University School of Medicine, USA) and Thomas C. Bailey (Washington University School of Medicine, USA)
Copyright: © 2011 |Pages: 20
DOI: 10.4018/jkdb.2011070101
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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.
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Medical data mining is one of key issues to get useful clinical knowledge from medical databases. These algorithms either rely on medical knowledge or general data mining techniques.

A number of scoring systems that already exist use medical knowledge for various medical conditions. For example, the effectiveness of Several Community-Acquire Pneumonia (SCAP) and Pneumonia Severity Index (PSI) in predicting outcomes in patients with pneumonia is evaluated in Yandiola et al. (2009). Similarly, outcomes in patients with renal failures may be predicted using the Acute Physiology Score (12 physiologic variables), Chronic Health Score (organ dysfunction), and APACHE score (Knaus, Draper, Wagner, & Zimmerman, 1985). However, these algorithms are best for specialized hospital units for specific visits. In contrast, the detection of clinical deterioration on general hospital units requires more general algorithms. For example, the Modified Early Warning Score (MEWS) (Ko et al., 2010) uses systolic blood pressure, pulse rate, temperature, respiratory rate, age and BMI to predict clinical deterioration. These physiological and demographic parameters may be collected at bedside, making MEWS suitable for a general hospital.

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