Predictive models in emergency medicine and their missing data strategies: a systematic review.

ABSTRACT: In the field of emergency medicine (EM), the use of decision support tools based on artificial intelligence has increased markedly in recent years. In some cases, data are omitted deliberately and thus constitute "data not purposely collected" (DNPC). This accepted information bias can be managed in various ways: dropping patients with missing data, imputing with the mean, or using automatic techniques (e.g., machine learning) to handle or impute the data. Here, we systematically reviewed the methods used to handle missing data in EM research. A systematic review was performed after searching PubMed with the query "(emergency medicine OR emergency service) AND (artificial intelligence OR machine learning)". Seventy-two studies were included in the review. The trained models variously predi

SUBMITTER: Arnaud E 

PROVIDER: S-EPMC9950346 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature