Modern Learning from Big Data in Critical Care: Primum Non Nocere.

ABSTRACT: Large and complex data sets are increasingly available for research in critical care. To analyze these data, researchers use techniques commonly referred to as statistical learning or machine learning (ML). The latter is known for large successes in the field of diagnostics, for example, by identification of radiological anomalies. In other research areas, such as clustering and prediction studies, there is more discussion regarding the benefit and efficiency of ML techniques compared with statistical learning. In this viewpoint, we aim to explain commonly used statistical learning and ML techniques and provide guidance for responsible use in the case of clustering and prediction questions in critical care. Clustering studies have been increasingly popular in critical care research, aiming

SUBMITTER: Gravesteijn BY 

PROVIDER: S-EPMC9071245 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature