Abstract
Machine learning and other artificial intelligence techniques have been extensively used in a large number of medical applications, in diagnosis, treatment selection, mining of electronic health records, genetics, and image processing, among several others. Machine learning techniques can be used to infer predictive models, from labeled data, in many areas of medicine, including stroke. In particular, in patients who suffered from stroke, machine learning can be used to perform or improve outcome prediction, lesion segmentation, and treatment assessment, among others. Machine learning algorithms can be based on a number of different approaches. In this chapter, we cover the symbolic, statistical, similarity-based, and connectionist approaches, which have different properties and trade-offs. Recently, a set of techniques generally known as deep learning have increased significantly the range of applicability of machine learning methods, which are now able to deal with several problems characterized by high-dimensional data, such as images and videos, outperforming even experts in tasks such as lesion segmentation and outcome prediction. We provide an overview of the different machine learning methods used in the stroke area and some of the applications proposed by researchers in the field.
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References
Domingos P. The master algorithm. London: Allen Lane; 2015.
Quinlan JR. Induction of decision trees. Mach Learn. 1986;1(1):81–106.
Quinlan JR. C4.5—programs for machine learning. San Mateo: Morgan Kaufmann; 1993.
Breiman L, Friedman J, Stone CJ, Olshen RA. Classification and regression trees. Boca Raton, FL: CRC Press; 1984.
Saraee MH, Keane J. Using T3, an improved decision tree classifier, for mining stroke-related medical data. Methods Inf Med. 2007;46(5):523–9.
Letham B, Rudin C, McCormick TH, Madigan D. Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model. Ann Appl Stat. 2015;9(3):1350–71.
Monteiro M, Fonseca AC, Freitas AT, Pinho E, Melo T, Francisco AP, Ferro JM, Oliveira AL. Using machine learning to improve the prediction of functional outcome in ischemic stroke patients. IEEE/ACM Trans Comput Biol Bioinform. 2018;15(6):1953–9.
Heo JN, Yoon JG, Park H, Kim YD, Nam HS, Heo JH. Machine learning-based model for prediction of outcomes in acute stroke. Stroke. 2019;50(5):1263–5.
Domingos P, Pazzani M. On the optimality of the simple Bayesian classifier under zero-one loss. Mach Learn. 1997;29(2–3):103–30.
Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–97.
Scholkopf B, Smola AJ. Learning with kernels: support vector machines, regularization, optimization, and beyond, Adaptive computation and machine learning series. Cambridge, MA: MIT Press; 2018.
Jeena RS, Kumar S. Stroke prediction using SVM. In: 2016 International conference on control, instrumentation, communication and computational technologies (ICCICCT 2016). Piscataway, NJ: IEEE; 2017. p. 600–2.
Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev. 1958;65(6):386–408.
Minsky M, Papert S. Perceptrons. Cambridge: MIT Press; 1969.
Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature. 1986;5(6088):533–6.
Fukushima K, Miyake S. Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition. In: Competition and cooperation in neural nets. Berlin: Springer; 1982. p. 267–85.
LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, et al. Backpropagation applied to handwritten ZIP code recognition. Neural Comput. 1989;1(4):541–51.
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, 2012. p. 1097–105.
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, Dec 2016. p. 770–8.
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision, 2015
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. In: Proceedings of the 30th IEEE conference on computer vision and pattern recognition (CVPR 2017), Jan 2017. p. 2261–9.
Drozdowska BA, Singh S, Quinn TJ. Thinking about the future: a review of prognostic scales used in acute stroke. Front Neurol. 2019;10:274.
Lin C-H, Hsu K-C, Johnson KR, Fann YC, Tsai C-H, Sun Y, et al. Evaluation of machine learning methods to stroke outcome prediction using a nationwide disease registry. Comput Methods Programs Biomed. 2020;190:105381.
Maier O, Menze BH, von der Gablentz J, Häni L, Heinrich MP, Liebrand M, et al. ISLES 2015—a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med Image Anal. 2017;35:250–69.
Winzeck S, Hakim A, McKinley R, Pinto JA, Alves V, Silva C, et al. ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI. Front Neurol. 2018;9:679.
Kamal H, Lopez V, Sheth SA. Machine learning in acute ischemic stroke neuroimaging. Front Neurol. 2018;9:7–12.
Sheth SA, Lopez-Rivera V, Barman A, Grotta JC, Yoo AJ, Lee S, et al. Machine learning-enabled automated determination of acute ischemic core from computed tomography angiography. Stroke. 2019;50(11):3093–100.
Nielsen A, Hansen MB, Tietze A, Mouridsen K. Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning. Stroke. 2018;49(6):1394–401.
Chauhan S, Vig L, De Grazia MDF, Corbetta M, Ahmad S, Zorzi M. A comparison of shallow and deep learning methods for predicting cognitive performance of stroke patients from MRI lesion images. Front Neuroinform. 2019;13:53.
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Oliveira, A.L. (2021). Artificial Intelligence Applications in Stroke. In: Fonseca, A.C., Ferro, J.M. (eds) Precision Medicine in Stroke. Springer, Cham. https://doi.org/10.1007/978-3-030-70761-3_10
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DOI: https://doi.org/10.1007/978-3-030-70761-3_10
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