ARTIFICIAL INTELLIGENCE (AI) APPROACHES IN PATIENTS WITH FEBRILE NEUTROPENIA

Aswathy Asokan A. N., Ajee K. L.*, Sachin Suresh Jadhav and K. T. Moly

ABSTRACT

Sepsis and septic shock are the most common complications in patients with febrile neutropenia. Early detection and treatment are key components that can improve patient outcomes. The latest digital health technology, such as remote patient monitoring devices integrated with AI-based models, showed promising results in sepsis identification. The goal of the study is to review the application of an artificial intelligence-based early warning scoring system using a contactless remote patient monitoring device for early identification of sepsis syndrome.

Highlights
• Patients with febrile neutropenia are at a high risk of sepsis related mortality.
• Clinical deterioration can be identified early with the use of an early warning score.
• Remote patient monitoring (RPM) technologies that provide continuous monitoring over extended periods of time may be more effective in identifying early changes in patients.
• Predictive Artificial Intelligence based algorithms helps in more accurate sepsis prediction
• A novel Early Warning Scoring system can be developed through integrating AI-based algorithms with remote patient monitoring devices.

Keywords: Artificial intelligence, Febrile neutropenia, Early Warning Scoring, Sepsis, Remote patient monitoring devices.