ICU Patient Risk Level Monitoring System using Supervised Learning Approaches

Abstract

Present-day intensive care units, or ICUs, offer critically ill patients who are susceptible to several problems, including death and morbidity, round-the-clock monitoring. ICU environments produce a lot of data and call for a high staff-to-patient ratio. Making decisions and interpreting data in real time is a difficult task for clinicians. In intensive care units (ICUs), machine learning (ML) tools are advancing the early detection of high-risk events. because of more powerful computers and publicly accessible databases like the Medical Information Mart for Intensive Care (MIMIC). Techniques in ICU settings uses MIMIC data. The ICU patient risk level monitoring system plays a crucial role in improving patient safety, optimizing resource allocation, and enhancing clinical decision-making in intensive care settings. By continuously monitoring and analyzing patient data, it provides valuable insights that help healthcare providers intervene promptly and prevent adverse outcomes.