From Big Data to Artificial Intelligence: Harnessing Data Routinely Collected in the Process of Care.

Critical Care Medicine www.ccmjournal.org 345 19. Dominici M, Le Blanc K, Mueller I, et al: Minimal criteria for defining multipotent mesenchymal stromal cells. The International Society for Cellular Therapy position statement. Cytotherapy 2006; 8:315–317 20. Galipeau J, Krampera M, Barrett J, et al: International Society for Cellular Therapy perspective on immune functional assays for mesenchymal stromal cells as potency release criterion for advanced phase clinical trials. Cytotherapy 2016; 18:151–159 21. Raicevic G, Najar M, Stamatopoulos B, et al: The source of human mesenchymal stromal cells influences their TLR profile as well as their functional properties. Cell Immunol 2011; 270:207–216 22. Ghanta S, Tsoyi K, Liu X, et al: Mesenchymal stromal cells deficient in autophagy proteins are susceptible to oxidative injury and mitochondrial dysfunction. Am J Respir Cell Mol Biol 2017; 56:300–309 23. Lee S, Choi E, Cha MJ, et al: Cell adhesion and long-term survival of transplanted mesenchymal stem cells: A prerequisite for cell therapy. Oxid Med Cell Longev 2015; 2015:632902 24. Kourembanas S: Exosomes: Vehicles of intercellular signaling, biomarkers, and vectors of cell therapy. Annu Rev Physiol 2015; 77:13–27

[1]  T. Frieden Evidence for Health Decision Making — Beyond Randomized, Controlled Trials: The Changing Face of Clinical Trials , 2017, The New England journal of medicine.

[2]  Fleur Fritz,et al.  Electronic health records to facilitate clinical research , 2016, Clinical Research in Cardiology.

[3]  Leo Anthony Celi,et al.  Preparing a New Generation of Clinicians for the Era of Big Data. , 2015, Harvard medical student review.

[4]  F. Della Corte,et al.  Effects of Propofol on Patient-Ventilator Synchrony and Interaction During Pressure Support Ventilation and Neurally Adjusted Ventilatory Assist* , 2014, Critical care medicine.

[5]  David J. Stone,et al.  State of the art review: the data revolution in critical care , 2015, Critical Care.

[6]  Jesse B. Hall,et al.  Impact of Ventilator Adjustment and Sedation–Analgesia Practices on Severe Asynchrony in Patients Ventilated in Assist-Control Mode* , 2013, Critical care medicine.

[7]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[8]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[9]  David J. Stone,et al.  "Big data" in the intensive care unit. Closing the data loop. , 2013, American journal of respiratory and critical care medicine.

[10]  Andrew James,et al.  Big Data in the Intensive Care Unit , 2017, AMIA.

[11]  Ziad Obermeyer,et al.  Lost in Thought - The Limits of the Human Mind and the Future of Medicine. , 2017, The New England journal of medicine.

[12]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[13]  Savino Spadaro,et al.  Effects of Sigh on Regional Lung Strain and Ventilation Heterogeneity in Acute Respiratory Failure Patients Undergoing Assisted Mechanical Ventilation* , 2015, Critical care medicine.

[14]  P. Pelosi,et al.  Variable tidal volumes improve lung protective ventilation strategies in experimental lung injury. , 2009, American journal of respiratory and critical care medicine.

[15]  David Albers,et al.  The Association Between Ventilator Dyssynchrony, Delivered Tidal Volume, and Sedation Using a Novel Automated Ventilator Dyssynchrony Detection Algorithm* , 2018, Critical care medicine.