Abstract: To prioritize treatment, allocate resources efficiently, and reduce negative consequences, identifying clinically unstable patients as early as possible is crucial. Consistent indicators of failure and death include cardiorespiratory instability and sepsis; hence, they are often used as the foundation for aggregate-weighted, vital sign-based early warning systems. Recently, it has been shown that aggregate-weighted models fall short, although machine learning models have shown promise due to their capacity to include trends and capture parameter correlations. Our primary objective in conducting this research would be to discover, examine, and evaluate the available literature on machine learning-based system for early warning that utilize vital signs to identify the likelihood of decline of one's physical condition in critically ill inpatients and outpatients. Several electronic databases were searched using the phrases "vital signs," "clinical deterioration," and "machine learning" to locate research papers. These studies all describe a machine learning model that uses demographic and vital sign data to predict emergency and outpatient outcomes. Throughout the data extraction procedure, the SRMA, TRMPMIPD, and Cochrane Collaboration procedures were observed.

Key words: ambulatory care, remote patient monitoring, risk prediction, machine learning, early warning systems, remote patient monitoring, vital signs