בשל "הגנת זכויות יוצרים", מובא להלן קישור למאמר בלבד. לקריאתו בטקסט מלא, אנא פנה לספרייה הרפואית הזמינה לך.

Studies reveal that the false alarm rate (FAR) demonstrated by intensive care unit (ICU) vital signs monitors ranges from 0.72 to 0.99. We applied machine learning (ML) to ICU multi-sensor information to imitate a medical specialist in diagnosing patient condition.

We hypothesized that applying this data-driven approach to medical monitors will help reduce the FAR even when data from sensors are missing. An expert-based rules algorithm identified and tagged in our dataset seven clinical alarm scenarios.