Open datasets in perioperative medicine: a narrative review

Abstract

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.

Keywords: Artificial intelligence; Big data; Critical care; Data science; Machine learning; Perioperative medicine.

Fig. 1.

Schematic representation of data extraction and creation of surgical datasets: (A) VitalDB, (B) INSPIRE dataset, and (C) MOVER dataset. INSPIRE: the informative surgical patient dataset for innovative research environment, CPB: cardiopulmonary bypass, ECMO: extracorporeal membrane oxygenation, IABP: intra-aortic balloon pump, SIS: surgical information systems, EPIC: Epic Systems, MOVER: Medical Informatics Operating Room Vitals and Events Repository.