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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
- 2005.2
- 수록면
- 3 - 10 (8page)
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초록· 키워드
오류제보하기
Back ground : Nurses now occupy one third of all hospital human resources. Therefore, efficient management of nursing manpower is getting more important. While it is very clear that nursing workload requirement analysis and patient severity classification should be done first for the efficient allocation of nursing workforce, these processes have been conducted manually with ad hoc rule.
Purposes : This study was tried to make a predict model for patient classification according to nursing need. We tried to find the easier and faster method to classify nursing patients that can help efficient management of nursing manpower.
Methods : The nursing patient classifications data of the hospitalized cancer patients in one of the biggest cancer center in Korea during 2003.1.1- 2003.12.31 were assessed by trained nurses.
This study developed a prediction model and analyzing nursing needs by data mining techniques. Patients were classified by three different data mining techniques, (Logistic regression, Decision tree and Neural network) and the results were assessed.
Results : The data set was created using 165,073 records of 2,228 patients classification database.
Main explaining variables were as follows in 3 different data mining techniques.
1) Logistic regression : age, month and section.
2) Decision tree : section, month, age and tumor.
3) Neural network : section, diagnosis, age, sex, metastasis, hospital days and month.
Among these three techniques, neural network showed the best prediction power in ROC curve verification. As the result of the patient classification prediction model developed by neural network based on nurse needs, the prediction accuracy was 84.06%.
Conclusion : The patient classification prediction model was developed and tested in this study using real patients data. The result can be employed for more accurate calculation of required nursing staff and effective use of labor force.
Purposes : This study was tried to make a predict model for patient classification according to nursing need. We tried to find the easier and faster method to classify nursing patients that can help efficient management of nursing manpower.
Methods : The nursing patient classifications data of the hospitalized cancer patients in one of the biggest cancer center in Korea during 2003.1.1- 2003.12.31 were assessed by trained nurses.
This study developed a prediction model and analyzing nursing needs by data mining techniques. Patients were classified by three different data mining techniques, (Logistic regression, Decision tree and Neural network) and the results were assessed.
Results : The data set was created using 165,073 records of 2,228 patients classification database.
Main explaining variables were as follows in 3 different data mining techniques.
1) Logistic regression : age, month and section.
2) Decision tree : section, month, age and tumor.
3) Neural network : section, diagnosis, age, sex, metastasis, hospital days and month.
Among these three techniques, neural network showed the best prediction power in ROC curve verification. As the result of the patient classification prediction model developed by neural network based on nurse needs, the prediction accuracy was 84.06%.
Conclusion : The patient classification prediction model was developed and tested in this study using real patients data. The result can be employed for more accurate calculation of required nursing staff and effective use of labor force.
목차
Ⅰ. 서 론
Ⅱ. 이론적 배경
Ⅲ. 연구대상 및 방법
Ⅳ. 연구 결과
Ⅴ. 논 의
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UCI(KEPA) : I410-ECN-0101-2009-512-017818439