“We need better ways to identify the patients that have this complication, as well as the different clinical factors associated with it,” said corresponding author Vesela Kovacheva, MD, of the Department of Anesthesiology, Perioperative and Pain Medicine.
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“There are so many amazing large language models being developed right now, and this approach could be used with other conditions and diseases.”
The emergence of artificial intelligence tools in healthcare has been groundbreaking and has the potential to positively reshape the continuum of care. Mass General Brigham, as one of the nation’s top integrated academic health systems and largest innovation enterprises, is leading the way in conducting rigorous research on new and emerging technologies to inform the responsible incorporation of AI into care delivery, workforce support, and administrative processes.
Because conditions like postpartum hemorrhage include a large spectrum of patients, symptoms, and causes, the research team used the Flan-T5 model to analyze comprehensive information from electronic health records to help them better categorize subpopulations of patients.
They prompted the Flan-T5 model with lists of concepts known to be associated with postpartum hemorrhage and then asked it to extract them from the discharge summaries of a cohort of 131,284 patients who gave birth at Mass General Brigham hospitals between 1998-2015. This method achieved rapid and accurate results without the need for manual labeling.
“Ideally, we would like to be able to predict who will develop postpartum hemorrhage before they do so, and this is a tool that can help us get there,” said first author Emily Alsentzer, PhD, a research fellow in the Division.
Next, the team plans to continue to use this approach to look at other pregnancy complications and hopes their work will help address growing maternal health crises in the United States.
“This approach can be applied to many future studies,” said Kovacheva. “And it could be used to help guide real-time medical decision making, which is very exciting and valuable to me as a clinician.”
Reference :
- Zero-shot Interpretable Phenotyping of Postpartum Hemorrhage Using Large Language Models – (https://www.nature.com/articles/s41746-023-00957-x)
Source: Eurekalert
