A Study of Predicting the Likelihood of Falls in Hospitalized Elderly Patients Using Data Mining Classification Techniques
碩士 === 元智大學 === 資訊管理學系 === 97 === Reduce the risk of patient harm resulting from falls is a sub goal of the Joint Commission on Accreditation of Healthcare Organizations of 2007. So prevent the elderly from falling is one of the important public health issue of countries all over the world. The sub-...
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ndltd-TW-097YZU053960492016-05-04T04:17:08Z http://ndltd.ncl.edu.tw/handle/88073625473462960335 A Study of Predicting the Likelihood of Falls in Hospitalized Elderly Patients Using Data Mining Classification Techniques 運用資料探勘中分類技術於預測住院老人跌倒之研究 Yu-Chen Chen 陳榆臻 碩士 元智大學 資訊管理學系 97 Reduce the risk of patient harm resulting from falls is a sub goal of the Joint Commission on Accreditation of Healthcare Organizations of 2007. So prevent the elderly from falling is one of the important public health issue of countries all over the world. The sub-project coordinated fall risk factors of past literature and collected 602 fall and non-fall admission data includes age, admission type, diagnosis, muscle power, admission and fall(discharge) fall risk assessment, Barthel indexs from researching hospital of north Taiwan. The research proposed to use data mining to construct inpatient fall risk forecast model and find significant patterns and prediction factors of fall and non-fall. In addition, the research according to different surgery first, and then look over that influences its surgery''s important dangerous factor. It can provide hospital to examine patients more efficiency. Finally, the accuracy rate of training data were up to 70% ,and validation date were also achieved 70%. Therefore, the model can achieve not just accuracy, but general. 詹前隆 2009 學位論文 ; thesis 134 zh-TW |
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碩士 === 元智大學 === 資訊管理學系 === 97 === Reduce the risk of patient harm resulting from falls is a sub goal of the Joint Commission on Accreditation of Healthcare Organizations of 2007. So prevent the elderly from falling is one of the important public health issue of countries all over the world. The sub-project coordinated fall risk factors of past literature and collected 602 fall and non-fall admission data includes age, admission type, diagnosis, muscle power, admission and fall(discharge) fall risk assessment, Barthel indexs from researching hospital of north Taiwan. The research proposed to use data mining to construct inpatient fall risk forecast model and find significant patterns and prediction factors of fall and non-fall. In addition, the research according to different surgery first, and then look over that influences its surgery''s important dangerous factor. It can provide hospital to examine patients more efficiency. Finally, the accuracy rate of training data were up to 70% ,and validation date were also achieved 70%. Therefore, the model can achieve not just accuracy, but general.
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author2 |
詹前隆 |
author_facet |
詹前隆 Yu-Chen Chen 陳榆臻 |
author |
Yu-Chen Chen 陳榆臻 |
spellingShingle |
Yu-Chen Chen 陳榆臻 A Study of Predicting the Likelihood of Falls in Hospitalized Elderly Patients Using Data Mining Classification Techniques |
author_sort |
Yu-Chen Chen |
title |
A Study of Predicting the Likelihood of Falls in Hospitalized Elderly Patients Using Data Mining Classification Techniques |
title_short |
A Study of Predicting the Likelihood of Falls in Hospitalized Elderly Patients Using Data Mining Classification Techniques |
title_full |
A Study of Predicting the Likelihood of Falls in Hospitalized Elderly Patients Using Data Mining Classification Techniques |
title_fullStr |
A Study of Predicting the Likelihood of Falls in Hospitalized Elderly Patients Using Data Mining Classification Techniques |
title_full_unstemmed |
A Study of Predicting the Likelihood of Falls in Hospitalized Elderly Patients Using Data Mining Classification Techniques |
title_sort |
study of predicting the likelihood of falls in hospitalized elderly patients using data mining classification techniques |
publishDate |
2009 |
url |
http://ndltd.ncl.edu.tw/handle/88073625473462960335 |
work_keys_str_mv |
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