Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data.

ABSTRACT: Overcrowding of emergency departments is a global concern, leading to numerous negative consequences. This study aimed to develop a useful and inexpensive tool derived from electronic medical records that supports clinical decision-making and can be easily utilized by emergency department physicians. We presented machine learning models that predicted the likelihood of hospitalizations within 24 hours and estimated waiting times. Moreover, we revealed the enhanced performance of these machine learning models compared to existing models by incorporating unstructured text data. Among several evaluated models, the extreme gradient boosting model that incorporated text data yielded the best performance. This model achieved an area under the receiver operating characteristic curve score of 0.92

SUBMITTER: Seo H 

PROVIDER: S-EPMC10896961 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature