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KMID : 1155520230180030213
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Anesthesia and Pain Medicine
2023 Volume.18 No. 3 p.213 ~ p.219
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Open datasets in perioperative medicine: a narrative review
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Lim Lee-Rang
Lee Hyung-Chul
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Abstract
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With the growing interest of researchers in machine learning and artificial intelligence (AI) based on large data, their roles in medical research have become increasingly prominent. Despite the proliferation of predictive models in perioperative medicine, external validation is lacking. Open datasets, defined as publicly available datasets for research, play a crucial role by providing high-quality data, facilitating collaboration, and allowing an objective evaluation of the developed models. Among the available datasets for surgical patients, VitalDB has been the most widely used, with MOVER recently launched and INSPIRE expected to be released soon. For critically ill patients, the available resources include MIMIC, eICU-CRD, AmsterdamUMCdb, and HiRID, with the anticipated release of the IMPACT dataset. This review presents a detailed comparison of each to enrich our understanding of these open datasets for data science and AI research in perioperative medicine.
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KEYWORD
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Artificial intelligence, Big data, Critical care, Data science, Machine learning, Perioperative medicine
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