Y Setoguchi, AA Ghaibeh, K Mitani, Y Abe, I Hashimoto and H Moriguchi,
The journal of medical investigation : JMI, 2016
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.
