Machine learning models for predicting the onset of chronic kidney disease after surgery in patients with renal cell carcinoma.
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| Title: | Machine learning models for predicting the onset of chronic kidney disease after surgery in patients with renal cell carcinoma. |
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| Authors: | Oh SW; Department of Medical Informatics, College of Medicine, The Catholic University of Korea, 06591, Seoul, Korea.; Department of Biomedicine & Health Sciences, The Catholic University of Korea, 06591, Seoul, Korea., Byun SS; Department of Urology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 13620, Seongnam, Korea., Kim JK; Department of Urology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 13620, Seongnam, Korea., Jeong CW; Department of Urology, Seoul National University College of Medicine, Seoul National University Hospital, 03080, Seoul, Korea., Kwak C; Department of Urology, Seoul National University College of Medicine, Seoul National University Hospital, 03080, Seoul, Korea., Hwang EC; Department of Urology, Chonnam National University Medical School, 61469, Gwangju, Korea., Kang SH; Department of Urology, Korea University School of Medicine, 02841, Seoul, Korea., Chung J; Department of Urology, National Cancer Center, 10408, Goyang, Korea., Kim YJ; Department of Urology, Chungbuk National University College of Medicine, 28644, Cheongju, Korea.; Department of Urology, College of Medicine, Chungbuk National University, 28644, Cheongju, Korea., Ha YS; Department of Urology, School of Medicine, Kyungpook National University Chilgok Hospital, Kyungpook National University, 41404, Daegu, Korea., Hong SH; Department of Urology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea. toomey@catholic.ac.kr. |
| Source: | BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2024 Mar 22; Vol. 24 (1), pp. 85. Date of Electronic Publication: 2024 Mar 22. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947 (Electronic) Linking ISSN: 14726947 NLM ISO Abbreviation: BMC Med Inform Decis Mak Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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