CC BY-NC-ND 4.0 · J Neuroanaesth Crit Care 2020; 7(01): 01-02
DOI: 10.1055/s-0040-1701955
Editorial

Artificial Intelligence in Neurointensive Care Unit: A Cautious Leap into Future

Parmod K. Bithal
1   Division of Neuroanesthesiology, Department of Anesthesiology and Perioperative Medicine, King Fahad Medical City, Riyadh, Saudi Arabia
› Author Affiliations

Artificial intelligence (AI) is the branch of computer science dealing with the simulation of intelligent behavior in computers.[1] Computers play a key role in almost every aspect of our daily life. In healthcare, computers are an excellent means of storage of patient-related data. The amount of data gleaned electronically from patients admitted in the intensive care units (ICUs) has been growing rapidly every day. Several equipment, such as pressure transducers, infusion pumps, electrocardiography (ECG), pulse oximeters, cardiac output monitors, fluids intake and output monitors, temperature, neurological examination, and mechanical ventilators, interface with computers and store electronic data. Similarly, a wealth of information is recorded from each patient in the ICU, including high-resolution physiological signals, various laboratory tests, and details of medical history in electronic health records (EHRs).[2] Computerized ICU systems interface, in turn, provide access to hospital database, including demographic, electronic patient records, order entry, laboratory, pharmacy, and radiological systems. To be of use, it is necessary that ICU bedside data must be extracted and organized to become information for clinical decisions.[3] AI can assist not only in administering repetitive patient assessment in real time, but also in integrating and interpreting these data source with EHR data, thus potentially enabling more timely and targeted interventions.[4] [5] Closed-loop AI systems can monitor parameters of patients; then, directly treat patients and induce changes in those very parameters that are undergoing monitoring. These systems can make direct real-time adjustments to patient care without any human input.[6] AI has proved effective in lowering cost, expanding access, and improving healthcare fields. The application of AI in medicine has been related to the development of AI programs, intended to help the clinician in the making of a diagnosis, adopting therapeutic decisions, and forecasting outcomes. It plays a pivotal role by forewarning impending complications, thereby resulting in a faster response by the clinician.[7] AI in an ICU setting could decrease clinicians’ as well as nurses’ workload, thereby allowing them to focus their attention on critical tasks. It could also augment human decision-making by offering low-cost, high-capacity intelligent data processing.



Publication History

Article published online:
25 March 2020

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  • References

  • 1 Ghahramani Z. Probabilistic machine learning and artificial intelligence. Nature 2015; 521 (7553) 452-459
  • 2 Jalali A, Bender D, Rehman M, Nadkarni V, Natraj C. Clinical systems for patient specific outcomes prediction in intensive care units. Paper presented at: 38th Annual International Conference of the IEEE in Engineering in Medicine and Biology Society (EMBC); Aug 17–20. 2016. Orlando, FL:
  • 3 Hanson III CW, Marshall BE. Artificial intelligence applications in the intensive care unit. Crit Care Med 2001; 29 (02) 427-435
  • 4 Vincent JL, Creteur J. Paradigm shifts in critical care medicine: the progress we have made. Crit Care 2015; 19 (Suppl. 03) S10
  • 5 Hirsch LJ. Continuous EEG monitoring in the intensive care unit: an overview. J Clin Neurophysiol 2004; 21 (05) 332-340
  • 6 Rinehart J, Liu N, Alexander B, Cannesson M. Review article: closed-loop systems in anesthesia: is there a potential for closed-loop fluid management and hemodynamic optimization?. Anesth Analg 2012; 114 (01) 130-143
  • 7 Vespa PM, Miller C, Hu X, Nenov V, Buxey F, Martin NA. Intensive care unit robotic telepresence facilitates rapid physician response to unstable patients and decreased cost in neurointensive care. Surg Neurol 2007; 67 (04) 331-337
  • 8 Wartenberg KE, Schmidt JM, Mayer SA. Multimodality monitoring in neurocritical care. Crit Care Clin 2007; 23 (03) 507-538
  • 9 Hu X, Xu P, Asgari S, Vespa P, Bergsneider M. Forecasting ICP elevation based on prescient changes of intracranial pressure waveform morphology. IEEE Trans Biomed Eng 2010; 57 (05) 1070-1078
  • 10 Huang SJ, Shieh JS, Fu M, Kao MC. Fuzzy logic control for intracranial pressure via continuous propofol sedation in a neurosurgical intensive care unit. Med Eng Phys 2006; 28 (07) 639-647
  • 11 Zhang F, Feng M, Pan SJ. et al. Artificial neural network based intracranial pressure mean forecast algorithm for medical decision support. Conference proceeding—Annual International Conference of the IEEE. Engineering in Medicine and Biological Society; Jan 1 2011; 7111-7114
  • 12 Donald R, Howells T, Piper I. et al. Brain IT Group. Forewarning of hypotensive events using a Bayesian artificial neural network in neurocritical care. J Clin Monit Comput 2019; 33 (01) 39-51
  • 13 Prasad N, Cheng L-F, Chivers C, Draugelis M, Engelhardt BE. A reinforcement learning approach to weaning of mechanical ventilation in intensive care units. ArXiv170406300 Cs. Available at: http://arxiv.org/abs/1704.06300. Accessed Oct 4, 2019
  • 14 Abbasi B, Goldenholz DM. Machine learning applications in epilepsy. Epilepsia 2019; 60 (10) 2037-2047
  • 15 Nydahl P, Bartoszek G, Binder A. et al. Prevalence for delirium in stroke patients: a prospective controlled study. Brain Behav 2017; 7 (08) e00748
  • 16 Davoudi A, Malhotra KR, Shickel B. et al. The intelligent ICU for autonomous patient monitoring using pervasive sensing and deep learning. Sci Rep 2019; 9 (01) 8020