Application of machine learning-based models to boost the predictive power of the SPAN index

References

• Ntaios G, Faouzi M, Ferrari J, et al. An integer-based score to predict functional outcome in acute ischemic stroke: the ASTRAL score. Neurology. 2012;78(24):1916–1922.
   PubMed Web of Science ®Google Scholar
• Strbian D, Meretoja A, Ahlhelm FJ, et al. Predicting outcome of IV thrombolysis-treated ischemic stroke patients: the DRAGON score. Neurology. 2012;78(6):427–432.
   PubMed Web of Science ®Google Scholar
• Flint AC, Cullen SP, Faigeles BS, et al. Predicting long-term outcome after endovascular stroke treatment: the totaled health risks in vascular events score. AJNR Am J Neuroradiol. 2010;31(7):1192–1196.
   PubMed Web of Science ®Google Scholar
• Saposnik G, Kapral MK, Liu Y, Stroke Outcomes Research Canada (SORCan) Working Group, et al. IScore: a risk score to predict death early after hospitalization for an acute ischemic stroke. Circulation. 2011;123(7):739–749.
   PubMed Web of Science ®Google Scholar
• Saposnik G, Guzik AK, Reeves M, et al. Stroke prognostication using age and NIH Stroke Scale: SPAN-100. Neurology. 2013;80(1):21–28.
   PubMed Web of Science ®Google Scholar
• Rabinstein A, Rundek T. Prediction of outcome after ischemic stroke: the value of clinical scores. Neurology. 2013;80(1):15–16.
   PubMed Web of Science ®Google Scholar
• Mobius C, et al. Re-evaluation of the stroke prognostication using age and NIH Stroke Scale index (SPAN-100 index) in IVT patients – the-SPAN 10065 index. BMC Neurol. 2018;18(1):129.
   PubMed Web of Science ®Google Scholar
• Krishnan P, Saposnik G, Ovbiagele B, et al. Contribution and additional impact of imaging to the SPAN-100 score. AJNR Am J Neuroradiol. 2015;36(4):646–652.
   PubMed Web of Science ®Google Scholar
• Almekhlafi MA, Davalos A, Bonafe A, STAR Registry Investigators, et al. Impact of age and baseline NIHSS scores on clinical outcomes in the mechanical thrombectomy using Solitaire FR in acute ischemic stroke study. AJNR Am J Neuroradiol. 2014;35(7):1337–1340.
   PubMed Web of Science ®Google Scholar
• Pan Y, Jing J, Zhang R, et al. Poor performance of stroke prognostication using age and National Institutes of Health Stroke Scale-100 to predict 3- and 12-month outcomes of ischemic stroke in China National Stroke Registry. J Stroke Cerebrovasc Dis. 2014;23(9):2335–2340.
   PubMed Web of Science ®Google Scholar
• Yu K-H, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719–731.
   PubMed Web of Science ®Google Scholar
• Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94–98.
   PubMedGoogle Scholar
• Hsieh F-I, Lien L-M, Chen S-T, Taiwan Stroke Registry Investigators, et al. Get With the Guidelines-Stroke performance indicators: surveillance of stroke care in the Taiwan Stroke Registry: Get With the Guidelines-Stroke in Taiwan. Circulation. 2010;122(11):1116–1123.
   PubMed Web of Science ®Google Scholar
• Furie KL, Jayaraman MV. 2018 Guidelines for the early management of patients with acute ischemic stroke. Stroke. 2018;49(3):509–510.
   PubMed Web of Science ®Google Scholar
• Jahan R, Saver JL, Schwamm LH, et al. Association between time to treatment with endovascular reperfusion therapy and outcomes in patients with acute ischemic stroke treated in clinical practice. JAMA. 2019;322(3):252–263.
   PubMed Web of Science ®Google Scholar
• Jang MU, Kang J, Kim BJ, et al. In-hospital and post-discharge recovery after acute ischemic stroke: a nationwide multicenter stroke registry-base study. J Korean Med Sci. 2019;34(36):e240.
   PubMed Web of Science ®Google Scholar
• Amato F, López A, Peña-Méndez EM, et al. Artificial neural networks in medical diagnosis. J Appl Biomed. 2013;11(2):47–58.
   Web of Science ®Google Scholar
• Friedman JH. Stochastic gradient boosting. Comput Stat Data Anal. 2002;38(4):367–378.
   Web of Science ®Google Scholar
• Chin JH, Vora N. The global burden of neurologic diseases. Neurology. 2014;83(4):349–351.
   PubMed Web of Science ®Google Scholar
• Duncan PW, Zorowitz R, Bates B, et al. Management of Adult Stroke Rehabilitation Care: a clinical practice guideline. Stroke. 2005;36(9):e100–e143.
   PubMed Web of Science ®Google Scholar
• Ovbiagele B, Nguyen-Huynh MN. Stroke epidemiology: advancing our understanding of disease mechanism and therapy. Neurotherapeutics. 2011;8(3):319–329.
   PubMed Web of Science ®Google Scholar
• Wardlaw JM, Murray V, Berge E, et al. Recombinant tissue plasminogen activator for acute ischaemic stroke: an updated systematic review and meta-analysis. Lancet. 2012;379(9834):2364–2372.
   PubMed Web of Science ®Google Scholar
• El Khoury R, Jung R, Nanda A, et al. Overview of key factors in improving access to acute stroke care. Neurology. 2012;79(13 Suppl 1):S26–S34.
   PubMed Web of Science ®Google Scholar
• Asuzu D, Nystrom K, Amin H, et al. Comparison of 8 scores for predicting symptomatic intracerebral hemorrhage after IV thrombolysis. Neurocrit Care. 2015;22(2):229–233.
   PubMed Web of Science ®Google Scholar
• Sung S-F, Chen SC-C, Lin H-J, et al. Comparison of risk-scoring systems in predicting symptomatic intracerebral hemorrhage after intravenous thrombolysis. Stroke. 2013;44(6):1561–1566.
   PubMed Web of Science ®Google Scholar
• Ovbiagele B, Reeves MJ, Nasiri M, et al. A simple risk index and thrombolytic treatment response in acute ischemic stroke. JAMA Neurol. 2014;71(7):848–854.
   PubMed Web of Science ®Google Scholar
• Ntaios G, Papavasileiou V, Michel P, et al. Predicting functional outcome and symptomatic intracranial hemorrhage in patients with acute ischemic stroke: a glimpse into the crystal ball? Stroke. 2015;46(3):899–908.
   PubMed Web of Science ®Google Scholar
• Bentsen L, Christensen L, Christensen A, et al. Outcome and risk factors presented in old patients above 80 years of age versus younger patients after ischemic stroke. J Stroke Cerebrovasc Dis. 2014;23(7):1944–1948.
   PubMed Web of Science ®Google Scholar
• Veerbeek JM, Kwakkel G, van Wegen EEH, et al. Early prediction of outcome of activities of daily living after stroke: a systematic review. Stroke. 2011;42(5):1482–1488.
   PubMed Web of Science ®Google Scholar
• Kasner SE. Clinical interpretation and use of stroke scales. Lancet Neurol. 2006;5(7):603–612.
   PubMed Web of Science ®Google Scholar
• Stoltzfus JC. Logistic regression: a brief primer. Acad Emerg Med. 2011;18(10):1099–1104.
   PubMed Web of Science ®Google Scholar
• Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal. 2000;22(5):717–727.
   PubMed Web of Science ®Google Scholar
• Chan KL, Leng X, Zhang W, et al. Early identification of high-risk TIA or minor stroke using artificial neural network. Front Neurol. 2019;10:171.
   PubMed Web of Science ®Google Scholar
• Sung SM, Kang YJ, Cho HJ, et al. Prediction of early neurological deterioration in acute minor ischemic stroke by machine learning algorithms. Clin Neurol Neurosurg. 2020;195:105892.
   PubMed Web of Science ®Google Scholar
• Li X, Bian D, Yu J, et al. Using machine learning models to improve stroke risk level classification methods of China national stroke screening. BMC Med Inform Decis Mak. 2019;19(1):261.
   PubMed Web of Science ®Google Scholar
• Chung C-C, Hong C-T, Huang Y-H, et al. Predicting major neurologic improvement and long-term outcome after thrombolysis using artificial neural networks. J Neurol Sci. 2020;410:116667.
   PubMed Web of Science ®Google Scholar
• Chung C-C, Chan L, Bamodu OA, et al. Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death. Sci Rep. 2020;10(1):20501.
   PubMed Web of Science ®Google Scholar