지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2013.1
- 수록면
- 121 - 129 (9page)
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초록· 키워드
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Objectives: Predicting the length of stay (LOS) of patients in a hospital is important in providing them with better services and higher satisfaction, as well as helping the hospital management plan and managing hospital resources as meticulously as possible. We propose applying data mining techniques to extract useful knowledge and draw an accurate model to predict the LOS of heart patients. Methods: Data were collected from patients with coronary artery disease (CAD). The patient records of 4,948 patients who had suffered CAD were included in the analysis. The techniques used are classification with three algorithms, namely, decision tree, support vector machines (SVM), and artificial neural network (ANN). LOS is the target variable, and 36 input variables are used for prediction. A confusion matrix was obtained to calculate sensitivity, specificity, and accuracy. Results: The overall accuracy of SVM was 96.4% in the training set. Most single patients (64.3%) had an LOS ≤5 days, whereas 41.2% of married patients had an LOS >10 days. Moreover, the study showed that comorbidity states, such as lung disorders and hemorrhage with drug consumption have an impact on long LOS. The presence of comorbidities, an ejection fraction <2, being a current smoker, and having social security type insurance in coronary artery patients led to longer LOS than other subjects. Conclusions: All three algorithms are able to predict LOS with various degrees of accuracy. The findings demonstrated that the SVM was the best fit. There was a significant tendency for LOS to be longer in patients with lung or respiratory disorders and high blood pressure.
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참고문헌 (25)
참고문헌 신청
Arab M / 2010 / Analysis of factors affecting length of stay in public hospitals in Lorestan Province, Iran / Hakim Res J 12 (4) : 27 ~ 32
Blais MA / 2003 / Predicting length of stay on an acute care medical psychiatric inpatient service / Adm Policy Ment Health 31 (1) : 15 ~ 29
Chang KC / 2002 / Prediction of length of stay of first-ever ischemic stroke / Stroke 33 (11) : 2670 ~ 2674
Gomez V / 2009 / Using data mining to describe long hospital stays / Paradigma 3 (1) : 1 ~ 10
Isken MW / 2002 / Data mining to support simulation modeling of patient flow in hospitals / J Med Syst 26 (2) : 179 ~ 197
Blais MA / 2003 / Predicting length of stay on an acute care medical psychiatric inpatient service / Adm Policy Ment Health 31 (1) : 15 ~ 29
Chang KC / 2002 / Prediction of length of stay of first-ever ischemic stroke / Stroke 33 (11) : 2670 ~ 2674
Gomez V / 2009 / Using data mining to describe long hospital stays / Paradigma 3 (1) : 1 ~ 10
Isken MW / 2002 / Data mining to support simulation modeling of patient flow in hospitals / J Med Syst 26 (2) : 179 ~ 197
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