Modern ICUs continuously monitor vital signs like heart rate, blood pressure, and oxygen levels. However, current monitoring systems rely on static threshold-based alarms. These systems: •Trigger alerts for minor, isolated fluctuations •Do not analyze trends over time •Ignore patient-specific baselines •Operate reactively instead of predictively As a result, clinicians face 150–300 alarms per patient per day, leading to alarm fatigue and reduced responsiveness. The critical issue is not the lack of data — it is the lack of intelligent interpretation of multi-parameter trends. Cardiac arrest is rarely sudden. Clinical instability often begins 4–6 hours before collapse, but existing systems fail to detect these early deterioration patterns. Therefore, there is a need for an AI-driven predictive system that can analyze continuous ICU data, detect hidden deterioration trajectories, and alert clinicians before a critical event occurs.
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