Gmez-Ochoa SA, Pitton M, Valente LG, Sosa Vesga CD, Largo J, Quiroga-Centeno AC et al (2022) Efficacy of phage therapy in preclinical models of bacterial infection: a systematic review and meta-analysis
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)
Another is the development of more efficient delivery systems, particularly for systemic therapies, to ensure adequate bioavailability
Indones J Obstet Gynecol 5(4):203207
