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Disclosure of interest: NO Poster Session I - DIGITAL TRANSFORMATION, AI AND ROBOTICS 07.00 - DIGITAL TRANSFORMATION, AI AND ROBOTICS - 07.01 - TECHNOLOGY INNOVATIONS: ROBOTS, VIRTUAL REALITY, ARTIFICIAL INTELLIGENCE AND MORE P308 - ESOC25-442 CLINICIAN PERSPECTIVES ON MACHINE LEARNING TOOLS FOR OUTCOME PREDICTION AND DECISION-MAKING IN INTRACEREBRAL HAEMORRHAGE Alexandra Hurden 1 , Menglu Ouyang, Leibo Liu 1,2 , Xiaoying Chen 1 , Craig Anderson 1 The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, Australia, 2 Centre for Big Data Research in Health, Faculty of Medicine, University of New South Wales, Sydney, Australia Background and Aims: Machine learning (ML) tools hold promise in outcome prediction and assisting clinicians decision making for patients with acute intracerebral haemorrhage (ICH)

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The diet emphasizes fresh fruit and vegetable juice, high carbohydrate and potassium, no sodium or fat, and low animal protein, and is sometimes supplemented with exogenous digestive enzymes