K-MIMIC: a nationwide Korean multi-institutional Multimodal intensive care dataset.
Authors
Affiliations (37)
Affiliations (37)
- Department of Transdisciplinary Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Healthcare AI Research Institute, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Critical Care Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
- Interdisciplinary Program of Medical Informatics, Seoul National University, Seoul, Republic of Korea.
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
- Department of Psychiatry, College of Medicine, The Catholic University of Korea, Seoul St. Mary's Hospital, Seoul, Republic of Korea.
- Department of Medical Sciences, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
- CMC Institute for Basic Medical Science, The Catholic Medical Center of the Catholic University of Korea, Seoul, Republic of Korea.
- Big Data Science Team, ezCaretech Co., Ltd., Seoul, Republic of Korea.
- Department of Cardiovascular Thoracic Surgery, Chungnam National University Hospital, Daejeon, Republic of Korea.
- Center for Critical Care Medicine, Chungnam National University Hospital, Daejeon, Republic of Korea.
- Department of Neurology, Keimyung University Dongsan Hospital, Keimyung University School of Medicine, Daegu, Republic of Korea.
- Department of Medical Informatics, Keimyung University School of Medicine, Daegu, Republic of Korea.
- Department of Otorhinolaryngology-Head & Neck Surgery, College of Medicine, Konyang University, Daejeon, Republic of Korea.
- Department of Biomedical Informatics, College of Medicine, Konyang University, Daejeon, Republic of Korea.
- Department of Pulmonology, Kangwon National University Hospital, Chuncheon, Republic of Korea.
- Department of Internal Medicine, Yonsei University Wonju Severance Christian Hospital Yonsei University Wonju College of Medicine, Wonju, Republic of Korea.
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Internal Medicine, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Republic of Korea.
- Department of Internal Medicine, School of Medicine, Pusan National University, Yangsan, Republic of Korea.
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Chungbuk National University Hospital, Cheongju, Republic of Korea.
- Department of Internal Medicine, Chungbuk National University College of Medicine, Cheongju, Republic of Korea.
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Dongguk University Ilsan Hospital, Goyang, Republic of Korea.
- Department of Internal Medicine, Graduate School, Dongguk University, Seoul, Republic of Korea.
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Veterans Health Service Medical Center, Seoul, Republic of Korea.
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
- Center for Data Science, Biomedical Research Institute, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea.
- Department of Intensive Care Medicine & Neurology, Dong-A University Hospital, Busan, Republic of Korea.
- Division of Respiratory, Allergy and Critical Care Medicine, Chonbuk National University Hospital, Jeonju, Republic of Korea.
- Department of Internal Medicine, Chonbuk National University Medical School, Jeonju, Republic of Korea.
- Division of Cardiothoracic Surgery, Bundang Jesaeng Hospital, Seongnam, Republic of Korea.
- Department of Critical Care Medicine, Seongnam Citizens Medical Center, Seongnam, Republic of Korea.
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea.
Abstract
Recent advancements in critical care have highlighted the need for comprehensive, multimodal datasets to support clinical decision-making and advancing artificial intelligence (AI) research. However, such datasets are scarce in Asia. We developed the Korean Multi-Institutional Multimodal Intensive Care (K-MIMIC) dataset by integrating structured electronic medical records (EMRs), high-resolution bio-signals, and medical imaging from multiple hospitals in Korea. This retrospective multicenter study collected intensive care unit (ICU) data from 278,274 patients admitted to 71 ICUs across 10 hospitals between 2001 and 2023. The data modalities included structured EMRs, physiological waveforms, and imaging studies. Data extraction followed standardized protocols and de-identification procedures in compliance with the Korean Health Data Utilization Guidelines. Multimodal linkage was achieved at the patient level to enable temporal trajectory analysis. The K-MIMIC dataset contains 287,274 ICU admissions from 241,805 unique patients, including 22,588 bio-signal files and 496,999 imaging studies, primarily chest X-rays aligned with EMRs. Nearly 47% of ICU admissions originated in the emergency department (ED). Elderly patients (65-90 years old) constituted the largest age group. Fifteen thousand, five hundred forty-eight patients had EMR data linked with both bio-signals and imaging, enabling full multimodal analyses. The K-MIMIC is the first large-scale, multicenter, multimodal ICU dataset in Asia to provide a robust resource for critical care research, including AI-based prediction, monitoring, and longitudinal outcome studies. The dataset demonstrates the feasibility of secure and standardized ICU data integration across diverse institutions.