Prediction of Postoperative Complications for Patients of End Stage Renal Diseaseopen access
- Authors
- Jeong, Young-Seob; Kim, Juhyun; Kim, Dahye; Woo, Jiyoung; Kim, Mun Gyu; Choi, Hun Woo; Kang, Ah Reum; Park, Sun Young
- Issue Date
- Jan-2021
- Publisher
- Multidisciplinary Digital Publishing Institute (MDPI)
- Keywords
- postoperative complication; machine learning model; end stage renal disease; postoperative complications; feature selection
- Citation
- Sensors, v.21, no.2, pp 1 - 15
- Pages
- 15
- Journal Title
- Sensors
- Volume
- 21
- Number
- 2
- Start Page
- 1
- End Page
- 15
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/20022
- DOI
- 10.3390/s21020544
- ISSN
- 1424-8220
1424-3210
- Abstract
- End stage renal disease (ESRD) is the last stage of chronic kidney disease that requires dialysis or a kidney transplant to survive. Many studies reported a higher risk of mortality in ESRD patients compared with patients without ESRD. In this paper, we develop a model to predict postoperative complications, major cardiac event, for patients who underwent any type of surgery. We compare several widely-used machine learning models through experiments with our collected data yellow of size 3220, and achieved F1 score of 0.797 with the random forest model. Based on experimental results, we found that features related to operation (e.g., anesthesia time, operation time, crystal, and colloid) have the biggest impact on model performance, and also found the best combination of features. We believe that this study will allow physicians to provide more appropriate therapy to the ESRD patients by providing information on potential postoperative complications.
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- Appears in
Collections - College of Medicine > Department of Anesthesiology > 1. Journal Articles
- SCH Media Labs > Department of Big Data Engineering > 1. Journal Articles
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