Open datasets in perioperative medicine: a narrative review.

Artificial intelligence Big data Critical care Data science Machine learning Perioperative medicine

Journal

Anesthesia and pain medicine
ISSN: 2383-7977
Titre abrégé: Anesth Pain Med (Seoul)
Pays: Korea (South)
ID NLM: 101517708

Informations de publication

Date de publication:
Jul 2023
Historique:
received: 26 06 2023
accepted: 10 07 2023
medline: 11 9 2023
pubmed: 11 9 2023
entrez: 11 9 2023
Statut: ppublish

Résumé

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 the Medical Informatics Operating Room Vitals and Events Repository recently launched and the Informative Surgical Patient dataset for Innovative Research Environment expected to be released soon. For critically ill patients, the available resources include the Medical Information Mart for Intensive Care, the eICU Collaborative Research Database, the Amsterdam University Medical Centers Database, and the High time Resolution ICU Dataset, with the anticipated release of the Intensive Care Network with Million Patients' information for the AI Clinical decision support system Technology 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.

Identifiants

pubmed: 37691592
pii: apm.23076
doi: 10.17085/apm.23076
pmc: PMC10410546
doi:

Types de publication

Journal Article Review

Langues

eng

Pagination

213-219

Subventions

Organisme : National Research Foundation of Korea
ID : NRF-2020R1C1C1014905

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Auteurs

Leerang Lim (L)

Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Korea.

Hyung-Chul Lee (HC)

Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Korea.

Classifications MeSH