in the ICU

作者: F Ongenae , SV Hoecke , JJ De Waele , B Pickering , FA Gonzalez

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摘要: The possibilities of artificial intelligence (AI), and more specifically, machine learning (ML), are being researched across almost all domains of medicine, and the field of intensive care medicine is certainly no exception. Several factors contribute to the intensive care unit (ICU) being one of the focal points of AI/ML research. Intensive care was one of the earliest adaptors of the electronic health record (EHR)(Varun and Marik 2002), making data readily available. A vast amount of diverse data is generated for an individual patient during clinical care, resulting in 6.000–30.000 routinely collected healthcare data points per ICU patient per day. As this data is mostly structured and annotated, it is very suitable for training ML models. Although these datasets still need to be unbolted to be usable for (big) data research in many cases, several readyto-use publicly available datasets extracted from information in the EHR already exist to date (Sauer et al. 2022; Jin et al. 2023; Rodemund et al. 2023). At the same time, large nationwide data-sharing initiatives ar e being set up to unlock even more routinely collected healthcare data for clinicians, researchers and data scientists alike. As such, the data-rich ICU environment has and is developing the prerequisites to further enable (big) data research. The expectations for the clinical impact of AI have risen alongside the number of published studies. It is generally expected that AI will harbour benefits for patients as well as clinicians and the whole of society (Topol 2019). This is reflected in the variety of study aims for which AI has been investigated in published studies so far (van de Sande et al. 2021).

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