Medicina intensiva
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Intensive care is an ideal environment for the use of Big Data Analysis (BDA) and Machine Learning (ML), due to the huge amount of information processed and stored in electronic format in relation to such care. These tools can improve our clinical research capabilities and clinical decision making in the future. The present study reviews the foundations of BDA and ML, and explores possible applications in our field from a clinical viewpoint. We also suggest potential strategies to optimize these new technologies and describe a new kind of hybrid healthcare-data science professional with a linking role between clinicians and data.
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Surgery represents one of the main therapeutic references in the world, affording greater survival and life expectancy for many patients. In general, the estimated postoperative mortality is low (around 1-4%). Thirteen percent of the surgical procedures have a high risk of complications, accounting for 80% of all postoperative deaths. ⋯ Institutional policies are required to ensure the detection of patients at risk in hospitalization wards, with early admission to the ICU of those patients in whom admission is indicated, adapting the treatment in the ICU and optimizing the criteria for discharge. The detection and prevention of post-ICU syndrome in patients and relatives, and the follow-up of ICU discharge and hospitalization in a multidisciplinary manner can reduce the sequelae among critical surgical patients, improving the outcomes and quality of life, and restoring the patient to society. In future publications of this series directed to the surgical patient, updates will be provided on the perioperative management of some of the most complex surgeries.