A team of researchers at Washington University (WashU) in St. Louis has developed a foundation AI model that uses clinical notes from surgical patients to predict complications like pneumonia, blood clots, and infections.
The new model, details of which are published in npj Digital Medicine, could help reduce the rate postoperative complications that affect roughly 10% of patients, which can lead to longer intensive care unit stays, higher mortality rates, and higher costs.
The reseachers use large language models (LLMs) to analyze the unstructured data found in a patient's clinical notes.
Using LLMs, the new model outperformed traditional machine learning methods in forecasting postoperative complications. For example, for every 100 patients who experienced a complication, the new model correctly identified 39 more at-risk patients than previous models.
An important capability of the model is its ability to identify risk of multiple complications. As complications often share underlying risk factors, a unified model can leverage these correlations to make more accurate predictions across various surgical outcomes.
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