Deployment of Artificial Intelligence in Neuro Critical Care

1. Ganapathy K, Abdul SS, Nursetyo AA. Artificial intelligence in neurosciences: A clinician's perspective. Neurol India. 2018;66(4):934-9. doi: 10.4103/0028-3886.236971

2. Suarez JI. Big Data/AI in Neurocritical Care: Maybe/Summary. Neurocrit Care. 2022;37:166-9. doi: 10.1007/s12028-021-01422-x

3. Al-Mufti F, Dodson V, Lee J, Wajswol E, Gandhi C, Scurlock C, et al. Artificial intelligence in neurocritical care. J Neurol Sci. 2019 Sep 15;404:1-4. doi: 10.1016/j.jns.2019.06.024

4. Miller GA. The magical number seven plus or minus two: some limits on our capacity for processing information. Psychological review. 1956;63:81-97.

5. Moss L, Corsar D, Shaw M, Piper I, Hawthorne C. Demystifying the Black Box: The Importance of Interpretability of Predictive Models in Neurocritical Care. Neurocrit Care. 2022;37:185–91. doi: https://doi.org/10.1007/s12028-022-01504-4

6. Ganapathy K. Artificial Intelligence and Healthcare Regulatory and Legal Concerns. TMT. 2021;6. https://doi.org/10.30953/tmt.v6.252

7. Dang J, Lal A, Flurin L, James A, Gajic O, Rabinstein AA. Predictive modeling in neurocritical care using causal artificial intelligence. World J Crit Care Med 2021;10:112-9. doi: 10.5492/wjccm.v10.i4.112

8. Alkhachroum A, Kromm J, De Georgia MA. Big data and predictive analytics in neurocritical care. Curr Neurol Neurosci Rep. 2022:19-32. doi: 10.1007/s11910-022-01167-w

9. Foreman B. Neurocritical Care: Bench to Bedside (Eds. Claude Hemphill, Michael James) Integrating and Using Big Data in Neurocritical Care. Neurotherapeutics. 2020;17:593-605. doi: 10.1007/s13311-020-00846-1

10. Munjal NK, Clark RSB, Simon DW, Kochanek PM, Horvat CM. Interoperable and explainable machine learning models to predict morbidity and mortality in acute neurological injury in the pediatric intensive care unit: secondary analysis of the TOPICC study. Front Pediatr. 2023;11:1177470. doi: 10.3389/fped.2023.1177470

11. Noh SH, Cho PG, Kim KN, Kim SH, Shin DA. Artificial Intelligence for Neurosurgery: Current State and Future Directions. J Korean Neurosurg Soc. 2023;66:113-20. doi: 10.3340/jkns.2022.0130

12. Wu M, Jiang X, Du K, Xu Y, Zhang W. Ensemble machine learning algorithm for predicting acute kidney injury in patients admitted to the neurointensive care unit following brain surgery. Sci Rep. 2023;13:6705. doi: 10.1038/s41598-023-33930-5

13. Nachtigall I, Tafelski S, Deja M, Halle E, Grebe MC, Tamarkin A, et al. Long-term effect of computer-assisted decision support for antibiotic treatment in critically ill patients: a prospective 'before/after' cohort study. BMJ open. 2014;4:e005370. doi: 10.1136/bmjopen-2014-005370

14. Ganapathy K. Brain death revisited. Neurol India. 2018;66:308-15. doi: 10.4103/0028-3886.227287

15. Haranath SP, Ganapathy K, Kesavarapu SR, Kuragayala SD. eNeuroIntensive Care in India: The Need of the Hour. Neurol India. 2021;69:245-51. doi: 10.4103/0028-3886.314591

16. Liu Q, Cui X, Abbod MF, Huang S-J, Han Y-Y, Shieh J-S. Brain death prediction based on ensembled artificial neural networks in neurosurgical intensive care unit J Taiwan Institute of Chemical Engineers. 2011;42:97–107. doi: https://doi.org/10.1016/j.jtice.2010.05.006

17. Luo L, Kou R, Feng Y, Xiang J, Zhu W. Cost-Effective Machine Learning Based Clinical Pre-Test Probability Strategy for DVT Diagnosis in Neurological Intensive Care Unit. Clin Appl Thromb Hemost. 2021;27:10760296211008650. doi: 10.1177/10760296211008650

18. Oh J, Cho D, Park J, Na SH, Kim J, Heo J, et al. Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning. Physiological measurement. 2018;39:035004. doi: 10.1088/1361-6579/aaab07

19. Gong KD, Lu R, Bergamaschi TS, Sanyal A, Guo J, Kim HB, et al. Predicting Intensive Care Delirium with Machine Learning: Model Development and External Validation. Anesthesiology. 2023;138:299-311. doi: 10.1097/ALN.0000000000004478

20. Wang ML, Kuo YT, Kuo LC, Liang HP, Cheng YW, Yeh YC, et al. Early prediction of delirium upon intensive care unit admission: Model development, validation, and deployment. J Clin Anesth. 2023;88:111121. doi: 10.1016/j.jclinane.2023.111121

21. Mataczynski C, Kazimierska A, Uryga A, Burzynska M, Rusiecki A, Kasprowicz M. End-to-End Automatic Morphological Classification of Intracranial Pressure Pulse Waveforms Using Deep Learning. IEEE JBHI. 2022;26:494-504. doi: 10.1109/JBHI.2021.3088629

22. Rush B, Celi LA, Stone DJ. Applying machine learning to continuously monitored physiological data. Journal of clinical monitoring and computing. 2019;33:887-93. doi: 10.1007/s10877-018-0219-z

23. X. Lu, J. Zhu, J. Gui, Q. Li. Prediction of All-cause Mortality with Sepsis-associated Encephalopathy in the ICU Based on Interpretable Machine Learning. 2022 IEEE International Conference on Mechatronics and Automation (ICMA), Guilin, Guangxi, China. 2022:298-302. doi: 10.1109/ICMA54519.2022.9856126

24. Quachtran B, Hamilton R, Scalzo F. Detection of Intracranial Hypertension using Deep Learning. Proc IAPR Int Conf Pattern Recogn. 2016;2016:2491-6. doi: 10.1109/ICPR.2016.7900010

25. Bihorac A, Ozrazgat-Baslanti T, Ebadi A, Motaei A, Madkour M, Pardalos PM, et al. MySurgeryRisk: Development and Validation of a Machine-learning Risk Algorithm for Major Complications and Death After Surgery. Ann Surg 2019;269:652-62. doi: 10.1097/SLA.0000000000002706

26. Yu R, Wang S, Xu J, Wang Q, He X, Li J, et al. Machine Learning Approaches-Driven for Mortality Prediction for Patients Undergoing Craniotomy in ICU. Brain Inj. 2021;35:1658-64. doi: 10.1080/02699052.2021.2008491

27. Park KH, Sun S, Lim YH, Park HR, Lee JM, Park K, et al. Clinical outcome prediction from analysis of microelectrode recordings using deep learning in subthalamic deep brain stimulation for Parkinson`s disease. PLoS One 2021;16:e0244133. Doi: 10.1371/journal.pone.0244133

28. Ren Y, Loftus TJ, Datta S, Ruppert MM, Guan Z, Miao S, et al. Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Predict Postoperative Complications and Report on a Mobile Platform. JAMA Netw Open. 2022;5:e2211973. doi: 10.1001/jamanetworkopen.2022.11973

29. van Niftrik CHB, van der Wouden F, Staartjes VE, Fierstra J, Stienen MN, Akeret K, et al. Machine Learning Algorithm Identifies Patients at High Risk for Early Complications After Intracranial Tumor Surgery: Registry-Based Cohort Study. Neurosurgery. 2019;85:E756-E764. doi: 10.1093/neuros/nyz145