Gyumin Kim ¹ Sung Woo Lee ² Su Jin Kim ² Kap Su Han ² Sijin Lee ² Juhyun Song ^2* Hyo Kyung Lee ^1*

• ¹ School of Industrial and Management Engineering, Korea University, Seoul, Republic of Korea
• ² Department of Emergency Medicine, Korea University Anam Hospital, Seoul, Republic of Korea

As delayed treatment of septic shock can lead to an irreversible health state, timely identification of septic shock holds immense value. While numerous approaches have been proposed to build early warning systems, these approaches primarily focus on predicting the future risk of septic shock, irrespective of its precise onset timing. Such early prediction systems without consideration of timeliness fall short in assisting clinicians in taking proactive measures. To address this limitation, we establish a timely warning system for septic shock with data-task engineering, a novel technique regarding the control of data samples and prediction targets.Leveraging machine learning techniques and the real-world electronic medical records from the MIMIC-IV (Medical Information Mart for Intensive Care) database, our system, TEW3S (Timely Early Warning System for Septic Shock), successfully predicted 94% of all shock events with one true alarm for every four false alarms and a maximum lead time of eight hours. This approach emphasizes the often-overlooked importance of prediction timeliness and may provide a practical avenue to develop a timely warning system for acute deterioration in hospital settings, ultimately improving patient outcomes.