An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest

In‐hospital cardiac arrest is a major burden to public health, which affects patient safety.1, Two types of TTS are used in RRSs. For the single‐parameter TTS (SPTTS), cardiac arrest is predicted if any single vital sign (eg, heart rate [HR], blood pressure) is out of the normal range.14 The aggregated weighted TTS calculates a weighted score for each vital sign and then finds patients with cardiac arrest based on the sum of these scores.15 The modified early warning score (MEWS) is one of the most widely used approaches among all aggregated weighted TTSs (Table 1)16; however, traditional TTSs including MEWS have limitations, with low sensitivity or high false‐alarm rates.14, 15, 17 Sensitivity and false‐alarm rate interact: Increased sensitivity creates higher false‐alarm rates and vice versa.