Prediction of Postoperative Complications for Patients of End Stage Renal Disease
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 compl...
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| Format: | Article |
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MDPI AG
2021-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/2/544 |
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| author | Young-Seob Jeong Juhyun Kim Dahye Kim Jiyoung Woo Mun Gyu Kim Hun Woo Choi Ah Reum Kang Sun Young Park |
| author_facet | Young-Seob Jeong Juhyun Kim Dahye Kim Jiyoung Woo Mun Gyu Kim Hun Woo Choi Ah Reum Kang Sun Young Park |
| author_sort | Young-Seob Jeong |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T04:52:25Z |
| format | Article |
| id | doaj.art-de3f62a1d35745f69ef293d34236fd9b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:52:25Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-de3f62a1d35745f69ef293d34236fd9b2023-12-03T13:09:50ZengMDPI AGSensors1424-82202021-01-0121254410.3390/s21020544Prediction of Postoperative Complications for Patients of End Stage Renal DiseaseYoung-Seob Jeong0Juhyun Kim1Dahye Kim2Jiyoung Woo3Mun Gyu Kim4Hun Woo Choi5Ah Reum Kang6Sun Young Park7Department of Future Convergence Technology, Soonchunhyang University, Asan-si 31538, KoreaDepartment of Big Data Engineering, Soonchunhyang University, Asan-si 31538, KoreaDepartment of Future Convergence Technology, Soonchunhyang University, Asan-si 31538, KoreaDepartment of Future Convergence Technology, Soonchunhyang University, Asan-si 31538, KoreaDepartment of Anesthesiology and Pain Medicine, Soonchunhyang University Hospital Seoul, Seoul 04401, KoreaDepartment of Anesthesiology and Pain Medicine, Soonchunhyang University Hospital Seoul, Seoul 04401, KoreaSCH Convergence Science Institute, Soonchunhyang University, Asan-si 31538, KoreaDepartment of Anesthesiology and Pain Medicine, Soonchunhyang University Hospital Seoul, Seoul 04401, KoreaEnd 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.https://www.mdpi.com/1424-8220/21/2/544postoperative complicationmachine learning modelend stage renal diseasepostoperative complicationsfeature selection |
| spellingShingle | Young-Seob Jeong Juhyun Kim Dahye Kim Jiyoung Woo Mun Gyu Kim Hun Woo Choi Ah Reum Kang Sun Young Park Prediction of Postoperative Complications for Patients of End Stage Renal Disease Sensors postoperative complication machine learning model end stage renal disease postoperative complications feature selection |
| title | Prediction of Postoperative Complications for Patients of End Stage Renal Disease |
| title_full | Prediction of Postoperative Complications for Patients of End Stage Renal Disease |
| title_fullStr | Prediction of Postoperative Complications for Patients of End Stage Renal Disease |
| title_full_unstemmed | Prediction of Postoperative Complications for Patients of End Stage Renal Disease |
| title_short | Prediction of Postoperative Complications for Patients of End Stage Renal Disease |
| title_sort | prediction of postoperative complications for patients of end stage renal disease |
| topic | postoperative complication machine learning model end stage renal disease postoperative complications feature selection |
| url | https://www.mdpi.com/1424-8220/21/2/544 |
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