Generating accurate alarms is a critical function of patient monitoring in hospitals, particularly in intensive care units. However, reliably generating clinically relevant alarms while keeping the overall number of alarms at a manageable level remains a significant challenge. Frequent false alarms reduce trust in the patient monitoring system and contribute to a phenomenon known as alarm fatigue, which can delay responses to important alarms and compromise patient safety. The objective of this project is to study how artificial intelligence can be utilized to develop personalized alarm systems that reduce false alarms in patient monitoring.