Machine learning-driven prediction of hospital admissions using gradient boosting and GPT-2.

ABSTRACT:

Background

Accurately predicting hospital admissions from the emergency department (ED) is essential for improving patient care and resource allocation. This study aimed to predict hospital admissions by integrating both structured clinical data and unstructured text data using machine learning models.

Methods

Data were obtained from the 2021 National Hospital Ambulatory Medical Care Survey-Emergency Department (NHAMCS-ED), including adult patients aged 18 years and older. Structured data included demographics, visit characteristics, vital signs, and medical history, while unstructured data consisted of free-text chief complaints and injury descriptions. A Gradient Boosting Classifier (GBC) was applied to structured data, while a fine-tuned GPT-2 model processed the unstruct

SUBMITTER: Zhang X 

PROVIDER: S-EPMC11951900 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature