Machine learning for predicting emergency department visits in patients with type 2 diabetes: A real-world, multi-institutional study.
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| Title: | Machine learning for predicting emergency department visits in patients with type 2 diabetes: A real-world, multi-institutional study. |
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| Authors: | Kim S; Department of Family Medicine, Kyung Hee University College of Medicine, Seoul, Korea., Sang H; Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, Korea.; Department of Endocrinology and metabolism, Kyung Hee University College of Medicine, Seoul, Korea., Park J; Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, Korea.; Department of Regulatory Science, Kyung Hee University, Seoul, Korea., Woo S; Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, Korea., Cho EH; Department of Internal Medicine, Kangwon National University College of Medicine, Chuncheon, Republic of Korea., Kim CH; Department of Internal Medicine, Sejong General Hospital, Bucheon, Korea., Kim DJ; Department of Endocrinology and Metabolism, Ajou University School of Medicine, Suwon, Korea., Jeong CW; Smart Business Team in Information Management Office, Wonkwang University Hospital, Iksan, Korea., Park TS; Division of Endocrinology and Metabolism, Department of Internal Medicine, Research Institute of Clinical Medicine of Jeonbuk National University and Jeonbuk National University Hospital, Jeonju, Korea., Hwang YC; Division of Endocrinology and Metabolism, Department of Internal Medicine, Kyung Hee University Hospital at Gangdong and Kyung Hee University School of Medicine, Seoul, Korea., Lim H; Department of Medical Nutrition, Graduate School of East-West Medical Science, Kyung Hee University, Yongin, Korea., Kim Z; Department of Data Science, Evidnet, Seoul, Korea., Kang H; Department of Data Science, Evidnet, Seoul, Korea., Yon DK; Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, Korea.; Department of Regulatory Science, Kyung Hee University, Seoul, Korea.; Department of Precision Medicine, Kyung Hee University College of Medicine, Seoul, Korea.; Department of Pediatrics, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, Korea., Rhee SY; Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, Korea.; Department of Endocrinology and metabolism, Kyung Hee University College of Medicine, Seoul, Korea.; Department of Regulatory Science, Kyung Hee University, Seoul, Korea.; Department of Precision Medicine, Kyung Hee University College of Medicine, Seoul, Korea. |
| Source: | PloS one [PLoS One] 2026 Jul 09; Vol. 21 (7), pp. e0352342. Date of Electronic Publication: 2026 Jul 09 (Print Publication: 2026). |
| Publication Type: | Journal Article; Multicenter Study |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0352342 |
