Unstructured clinical notes within the 24 hours since admission predict short, mid & long-term mortality in adult ICU patients.

ABSTRACT: Mortality prediction for intensive care unit (ICU) patients is crucial for improving outcomes and efficient utilization of resources. Accessibility of electronic health records (EHR) has enabled data-driven predictive modeling using machine learning. However, very few studies rely solely on unstructured clinical notes from the EHR for mortality prediction. In this work, we propose a framework to predict short, mid, and long-term mortality in adult ICU patients using unstructured clinical notes from the MIMIC III database, natural language processing (NLP), and machine learning (ML) models. Depending on the statistical description of the patients' length of stay, we define the short-term as 48-hour and 4-day period, the mid-term as 7-day and 10-day period, and the long-term as 15-day and 30

SUBMITTER: Mahbub M 

PROVIDER: S-EPMC8735614 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature