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.
