Setoguchi Yoko, Ghaibeh A Ammar, Mitani Kazue, Abe Yoshiro, Hashimoto Ichiro, Moriguchi Hiroki
Department of Medical Informatics, Institute of Biomedical Sciences, Tokushima University Graduate School.
J Med Invest. 2016;63(3-4):248-55. doi: 10.2152/jmi.63.248.
To develop a prediction model for pressure ulcer cases that continue to occur at an acute care hospital with a low occurrence rate of pressure ulcers.
Analyzing data were collected from patients hospitalized at Tokushima University Hospital during 2012 using an alternating decision tree (ADT) data mining method.
The ADT-based analysis revealed transfer activity, operation time, and low body mass index (BMI) as important factors for predicting pressure ulcer development.
Among the factors identified, only "transfer activity" can be modified by nursing intervention. While shear force and friction are known to lead to pressure ulcers, transfer activity has not been identified as such. Our results suggest that transfer activities creating shear force and friction correlate with pressure ulcer development. The ADT algorithm was effective in determining prediction factors, especially for highly imbalanced data. Our three stumps ADT yielded accuracy, sensitivity, and specificity values of 72.1%±3.7%, 79.3%±18.1%, and 72.1%±3.8%, respectively.
Transfer activity, identified as an interventional factor, can be modified through nursing interventions to prevent pressure ulcer formation. The ADT method was effective in identifying factors within largely imbalanced data. J. Med. Invest. 63: 248-255, August, 2016.
针对一家压疮发生率较低的急症医院中持续出现的压疮病例，开发一种预测模型。
采用交替决策树（ADT）数据挖掘方法，对2012年在德岛大学医院住院的患者收集的分析数据进行分析。
基于ADT的分析显示，转运活动、手术时间和低体重指数（BMI）是预测压疮发生的重要因素。
在确定的因素中，只有“转运活动”可通过护理干预进行调整。虽然已知剪切力和摩擦力会导致压疮，但转运活动此前未被认定为导致压疮的因素。我们的结果表明，产生剪切力和摩擦力的转运活动与压疮的发生相关。ADT算法在确定预测因素方面有效，尤其是对于高度不平衡的数据。我们的三节点ADT的准确率、灵敏度和特异度值分别为72.1%±3.7%、79.3%±18.1%和72.1%±3.8%。
被确定为干预因素的转运活动可通过护理干预进行调整，以预防压疮形成。ADT方法在识别严重不平衡数据中的因素方面有效。《医学调查杂志》63: 248 - 255，2016年8月。
