Artificial Intelligence and Big Data Science in Neurocritical Care

  • Shraddha Mainali
    Corresponding author. Virginia Commonwealth University, 1101 East Marshall Street, Sanger 6-004, Richmond, VA 23298.
    Department of Neurology, Virginia Commonwealth University, Richmond, 1101 East Marshall Street, Sanger-6-04, Richmond, VA 23298, USA
    Search for articles by this author
  • Soojin Park
    Department of Neurology, Department of Biomedical Informatics, Columbia University, 8-Milstein-300 center, 177 Fort, Washington Avenue, New York, NY 10032, USA
    Search for articles by this author
Published:October 09, 2022DOI:


      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribers receive full online access to your subscription and archive of back issues up to and including 2002.

      Content published before 2002 is available via pay-per-view purchase only.


      Subscribe to Critical Care Clinics
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Flechet M.
        • Grandas F.G.
        • Meyfroidt G.
        Informatics in neurocritical care: new ideas for Big Data.
        Curr Opin Crit Care. 2016; 22: 87-93
        • Mainali S.
        • Darsie M.E.
        • Smetana K.S.
        Machine learning in action: stroke diagnosis and outcome prediction.
        Front Neurol. 2021; 12: 734345
        • Jordan M.I.
        • Mitchell T.M.
        Machine learning: trends, perspectives, and prospects.
        Science. 2015; 349: 255-260
        • Halford G.S.
        • Baker R.
        • McCredden J.E.
        • et al.
        How many variables can humans process?.
        Psychol Sci. 2005; 16: 70-76
        • Rebitzer J.B.
        • Rege M.
        • Shepard C.
        Influence, information overload, and information technology in health care.
        Emerald Group Publishing Limited, Bingley, United Kingdom.2008
        • Montgomery V.L.
        Effect of fatigue, workload, and environment on patient safety in the pediatric intensive care unit.
        Pediatr Crit Care Med. 2007; 8: S11-S16
        • Winters B.
        • Custer J.
        • Galvagno S.M.
        • et al.
        Diagnostic errors in the intensive care unit: a systematic review of autopsy studies.
        BMJ Qual Saf. 2012; 21: 894-902
        • Medic G.
        • Kosaner Kließ M.
        • Atallah L.
        • Weichert J.
        • Panda S.
        • Postma M.
        • El-Kerdi A.
        Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review.
        F1000Res. 2019 Oct 8; 8 (PMID: 31824670; PMCID: PMC6894361): 1728
        • Donald R.
        • Howells T.
        • Piper I.
        • et al.
        Forewarning of hypotensive events using a Bayesian artificial neural network in neurocritical care.
        J Clin Monit Comput. 2019; 33: 39-51
        • Henry K.E.
        • Hager D.N.
        • Pronovost P.J.
        • et al.
        A targeted real-time early warning score (TREWScore) for septic shock.
        Sci Translational Med. 2015; 7 (299ra122-299ra122)
        • Güiza F.
        • Depreitere B.
        • Piper I.
        • et al.
        Early detection of increased intracranial pressure episodes in traumatic brain injury: external validation in an adult and in a pediatric cohort.
        Crit Care Med. 2017; 45: e316-e320
        • Potes C.
        • Conroy B.
        • Xu-Wilson M.
        • et al.
        A clinical prediction model to identify patients at high risk of hemodynamic instability in the pediatric intensive care unit.
        Crit Care. 2017; 21: 1-8
        • Zhao L.
        Event prediction in the big data era: a systematic survey.
        ACM Comput Surv (CSUR). 2021; 54: 1-37
      1. Clifton L, Clifton DA, Watkinson PJ, et al: Identification of patient deterioration in vital-sign data using one-class support vector machines. In: 2011 federated conference on computer science and information systems (FedCSIS): September 18-21, 2011: IEEE; 2011: 125-131.

        • Forkan A.R.M.
        • Khalil I.
        • Atiquzzaman M.
        • et al.
        A learning model for early discovery and real-time prediction of severe clinical events using vital signs as big data.
        Computer Networks. 2017; 113: 244-257
        • da Silva D.B.
        • Schmidt D.
        • da Costa C.A.
        • et al.
        DeepSigns: a predictive model based on deep learning for the early detection of patient health deterioration.
        Expert Syst Appl. 2021; 165: 113905
        • Knaus W.A.
        • Zimmerman J.E.
        • Wagner D.P.
        • et al.
        APACHE-acute physiology and chronic health evaluation: a physiologically based classification system.
        Crit Care Med. 1981; 9: 591-597
        • Marshall J.C.
        • Cook D.J.
        • Christou N.V.
        • et al.
        Multiple organ dysfunction score: a reliable descriptor of a complex clinical outcome.
        Crit Care Med. 1995; 23: 1638-1652
        • Vincent J.-L.
        • Moreno R.
        • Takala J.
        • et al.
        The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure.
        Springer-Verlag, 1996
        • Dijkland S.A.
        • Helmrich I.
        • Nieboer D.
        • et al.
        Outcome prediction after moderate and severe traumatic brain injury: external validation of two established prognostic models in 1742 European patients.
        J Neurotrauma. 2021; 38: 1377-1388
        • Bulgarelli L.
        • Deliberato R.O.
        • Johnson A.E.
        Prediction on critically ill patients: the role of “big data”.
        J Crit Care. 2020; 60: 64-68
      2. Johnson AE, Mark RG: Real-time mortality prediction in the Intensive Care Unit. In: AMIA Annual Symposium Proceedings: September 18-21, 2011: American Medical Informatics Association; 2017: 994.

      3. Xu Y, Biswal S, Deshpande SR, Maher KO, Sun J: Raim: Recurrent attentive and intensive model of multimodal patient monitoring data. In: Proceedings of the 24th ACM SIGKDD international conference on Knowledge Discovery & Data Mining: September 18-21, 2011; 2018: 2565-2573.

        • Castineira D.
        • Schlosser K.R.
        • Geva A.
        • et al.
        Adding continuous vital sign information to static clinical data improves the prediction of length of stay after intubation: a data-driven machine learning approach.
        Respir Care. 2020; 65: 1367-1377
        • Bhattacharyay S.
        • Rattray J.
        • Wang M.
        • et al.
        Decoding accelerometry for classification and prediction of critically ill patients with severe brain injury.
        Scientific Rep. 2021; 11: 1-17
        • Stevens R.D.
        • Sutter R.
        Prognosis in severe brain injury.
        Crit Care Med. 2013; 41: 1104-1123
        • Gill T.M.
        The central role of prognosis in clinical decision making.
        JAMA. 2012; 307: 199-200
        • Stapleton C.J.
        • Acharjee A.
        • Irvine H.J.
        • et al.
        High-throughput metabolite profiling: identification of plasma taurine as a potential biomarker of functional outcome after aneurysmal subarachnoid hemorrhage.
        J Neurosurg. 2019; 133: 1842-1849
        • Tjepkema-Cloostermans M.C.
        • da Silva Lourenço C.
        • Ruijter B.J.
        • et al.
        Outcome prediction in postanoxic coma with deep learning.
        Crit Care Med. 2019; 47: 1424-1432
        • Ghassemi M.M.
        • Amorim E.
        • Al Hanai T.
        • et al.
        Quantitative eeg trends predict recovery in hypoxic-ischemic encephalopathy.
        Crit Care Med. 2019; 47: 1416
        • MacEachern S.J.
        • Forkert N.D.
        Machine learning for precision medicine.
        Genome. 2021; 64: 416-425
        • Claassen J.
        • Doyle K.
        • Matory A.
        • et al.
        Detection of brain activation in unresponsive patients with acute brain injury.
        N Engl J Med. 2019; 380: 2497-2505
        • Murray N.M.
        • Unberath M.
        • Hager G.D.
        • et al.
        Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review.
        J Neurointerventional Surg. 2020; 12: 156-164
        • Ghassemi M.
        • Celi L.A.
        • Stone D.J.
        State of the art review: the data revolution in critical care.
        Crit Care (London, England). 2015; 19: 118