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However, published preclinical findings should not be interpreted as evidence of safety, efficacy or suitability for human use

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Disclosure of interest: None E-Posters - DIGITAL TRANSFORMATION, AI AND ROBOTICS 07.00 - DIGITAL TRANSFORMATION, AI AND ROBOTICS - 07.01 - TECHNOLOGY INNOVATIONS: ROBOTS, VIRTUAL REALITY, ARTIFICIAL INTELLIGENCE AND MORE P2596 - ESOC25-1195 MACHINE LEARNING–BASED EXPLAINABLE AUTOMATED NONLINEARCOMPUTATION SCORING SYSTEM FOR HEALTH SCORE AND AN APPLICATIONFOR PREDICTION OF PERIOPERATIVE STROKE: RETROSPECTIVE STUDY Mi-Young Oh 1 , Hee-Soo Kim 2 , Seung-Bo Lee 2 , Seung Mi Lee 3 1 Sejong General Hospital, Bucheon-si, South Korea, 2 Keimyung University School of Medicine, Daegu, South Korea, 3 Seoul National University College of Medicine, Seoul, South Korea Background and Aims: Machine learning (ML) has the potential to enhance performance by capturing nonlinear interactions