A Study of Predicting the Likelihood of Falls in Hospitalized Elderly Patients Using Data Mining Classification Techniques

• Main Authors: Yu-Chen Chen, 陳榆臻
• Other Authors: 詹前隆
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/88073625473462960335

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.