Generative artificial intelligence (AI), such as GPT-4, can help predict whether an emergency room patient needs to be admitted to the hospital even with only minimal training on a limited number of records, according to investigators at the Icahn School of Medicine at Mount Sinai. Details of the research were published in the May 21 online issue of the Journal of the American Medical Informatics Association in a paper titled "Evaluating the accuracy of a state-of-the-art large language model for prediction of admissions from the emergency room." In the retrospective study , the researchers analyzed records from seven Mount Sinai Health System hospitals, using both structured data, such as vital signs , and unstructured data, such as nurse triage notes, from more than 864,000 emergency room visits while excluding identifiable patient data.
Of these visits, 159,857 (18.5%) led to the patient being admitted to the hospital. The researchers compared GPT-4 against traditional machine-learning models such as Bio-Clinical-BERT for text and XGBoost for structured data in various scenarios, assessing its performance to predict hospital admissions independently and in combination with the traditional methods.
"We were motivated by the need to test whether generative AI, specifically large language models (LLMs) like GPT-4, could improve our ability to predict admissions in high-volume settings such as the Emergency Department," says co-senior author Eyal Klang, MD, Director of the Gen.