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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.
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
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  Data: Machine learning for predicting emergency department visits in patients with type 2 diabetes: A real-world, multi-institutional study.
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  Data: <searchLink fieldCode="AU" term="%22Kim+S%22">Kim S</searchLink>; Department of Family Medicine, Kyung Hee University College of Medicine, Seoul, Korea.<br /><searchLink fieldCode="AU" term="%22Sang+H%22">Sang H</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Park+J%22">Park J</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Woo+S%22">Woo S</searchLink>; Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, Korea.<br /><searchLink fieldCode="AU" term="%22Cho+EH%22">Cho EH</searchLink>; Department of Internal Medicine, Kangwon National University College of Medicine, Chuncheon, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Kim+CH%22">Kim CH</searchLink>; Department of Internal Medicine, Sejong General Hospital, Bucheon, Korea.<br /><searchLink fieldCode="AU" term="%22Kim+DJ%22">Kim DJ</searchLink>; Department of Endocrinology and Metabolism, Ajou University School of Medicine, Suwon, Korea.<br /><searchLink fieldCode="AU" term="%22Jeong+CW%22">Jeong CW</searchLink>; Smart Business Team in Information Management Office, Wonkwang University Hospital, Iksan, Korea.<br /><searchLink fieldCode="AU" term="%22Park+TS%22">Park TS</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Hwang+YC%22">Hwang YC</searchLink>; Division of Endocrinology and Metabolism, Department of Internal Medicine, Kyung Hee University Hospital at Gangdong and Kyung Hee University School of Medicine, Seoul, Korea.<br /><searchLink fieldCode="AU" term="%22Lim+H%22">Lim H</searchLink>; Department of Medical Nutrition, Graduate School of East-West Medical Science, Kyung Hee University, Yongin, Korea.<br /><searchLink fieldCode="AU" term="%22Kim+Z%22">Kim Z</searchLink>; Department of Data Science, Evidnet, Seoul, Korea.<br /><searchLink fieldCode="AU" term="%22Kang+H%22">Kang H</searchLink>; Department of Data Science, Evidnet, Seoul, Korea.<br /><searchLink fieldCode="AU" term="%22Yon+DK%22">Yon DK</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Rhee+SY%22">Rhee SY</searchLink>; 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.
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  Data: <searchLink fieldCode="JN" term="%22101285081%22">PloS one</searchLink> [PLoS One] 2026 Jul 09; Vol. 21 (7), pp. e0352342. <i>Date of Electronic Publication: </i>2026 Jul 09 (<i>Print Publication: </i>2026).
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