Cedars-Sinai Health Sciences University investigators developed an AI-based model that can identify hospitalized patients at risk of low blood sugar up to 24 hours before the condition occurs.
The long short-term memory (LSTM) model, described in npj Digital Medicine, could help clinicians intervene earlier and prevent complications, including, in severe cases, seizures, coma and long-term heart arrhythmias.
The AI model developed by Cedars-Sinai investigators analyzes patterns in medications, lab results, meals and other data from patients' electronic health records.
It collects the information in four-hour intervals over a five-day period and uses it to predict whether a patient will develop hypoglycemia within the next 24 hours.
Researchers developed and tested the model using data from more than 143,000 adult hospital admissions across three Cedars-Sinai Health System hospitals between 2014 and 2025.
Researchers estimate the tool could help prevent about three to four cases of low blood sugar at a large hospital each day.
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