Earticle

초록

영어

Despite technical advances in healthcare, the rates of hospital-acquired pressure injury (HAPI) are still high although many are potentially preventable. The purpose of this study was to determine whether tree-based prediction modeling is suitable for assessing the risk of HAPI in ICU patients. Retrospective cohort study has been carried out. A decision tree model was constructed with Age, Weight, eTube, diabetes, Braden score, Isolation, and Number of comorbid conditions as decision nodes. We used RStudio for model training and testing. Correct prediction rate of the final prediction model was 92.4 and the Area Under the ROC curve (AUC) was 0.699, which means there is about 70% chance that the model is able to distinguish between HAPI and non- HAPI. The results of this study has limited generalizability as the data were from a single academic institution. Our research finding shows that the data-driven tree-based prediction modeling may potentially support ICU sensitive risk assessment for HAPI prevention.

목차

Abstract
1. INTRODUCTION
2. METHODS
3. RESULTS
4. DISCUSSION AND CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Sookyung Hyun [ College of Nursing, Pusan National University, Yangsan-si, Korea ] Corresponding author
• Susan Moffatt-Bruce [ Department of Surgery, The Ohio State University, Columbus, Ohio, USA ]
• Cheryl Newton [ Central Quality and Education ]
• Brenda Hixon [ Health System Nursing Education, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA ]
• Pacharmon Kaewprag [ Department of Computer Engineering, Ramkhamhaeng University, Bangkok, Thailand ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Culture Technology(IJACT)

• 간기

  계간

• pISSN

  2288-7202

• eISSN

  2288-7318

• 수록기간

  2013~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 600 DDC 700