Alessandro Calamuneri, Luigi Donato, Concetta Scimone, Alessandra Costa, Rosalia D’Angelo, Antonina Sidoti
Abstract
Machine Learning (ML) is a field of Scientific Computing that emerged in the last decades to investigate complex phenomena based on huge amounts of observational data. In the context of Biomedicine, an increasing body of literature makes use of ML based approaches as an alternative to standard statistical inference. Moreover, a number of studies are now focusing on adopting ML to disclose new results that otherwise would have not been possible to achieve with classical inference. In this short review we outline fundamentals of ML, by further providing examples gathered from scientific literature to highlight the potentialities of ML for clinical purposes.
References
[1] Cruz JA, Wishart DS. Applications of Machine Learning in Cancer Prediction and Prognosis. Cancer Inform 2007;2:59–77.
[2] Magaudda A, Laganà A, Calamuneri A, Brizzi T, Scalera C, Beghi M, et al. Validation of a novel classification model of psychogenic nonepileptic seizures by video-EEG analysis and a machine learning approach. Epilepsy Behav 2016;60:197–201. doi:10.1016/j.yebeh.2016.03.031.
[3] Senders JT, Zaki MM, Karhade AV, Chang B, Gormley WB, Broekman ML, et al. An introduction and overview of machine learning in neurosurgical care. Acta Neurochir (Wien) 2017:1–10. doi:10.1007/s00701-017-3385-8.
[4] Quinlan JR. Induction of decision trees. Mach Learn 1986;1:81–106. doi:10.1007/BF00116251.
[5] Deo RC. Machine Learning in Medicine. Circulation 2015;132:1920–30. doi:10.1161/CIRCULATIONAHA.115.001593.
[6] Lakhani P, Prater AB, Hutson RK, Andriole KP, Dreyer KJ, Morey J, et al. Machine Learning in Radiology: Applications Beyond Image Interpretation. J Am Coll Radiol JACR 2017. doi:10.1016/j.jacr.2017.09.044.
[7] Davatzikos C, Ruparel K, Fan Y, Shen DG, Acharyya M, Loughead JW, et al. Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection. NeuroImage 2005;28:663–8. doi:10.1016/j.neuroimage.2005.08.009.
[8] Pereira F, Mitchell T, Botvinick M. Machine learning classifiers and fMRI: A tutorial overview. NeuroImage 2009;45:S199–209. doi:10.1016/j.neuroimage.2008.11.007.
[9] Richiardi J, Achard S, Bunke H, Van De Ville D. Machine Learning with Brain Graphs: Predictive Modeling Approaches for Functional Imaging in Systems Neuroscience. IEEE Signal Process Mag 2013;30:58–70. doi:10.1109/MSP.2012.2233865.
[10] Hussain L, Ahmed A, Saeed S, Rathore S, Awan IA, Shah SA, et al. Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies. Cancer Biomark Sect Dis Markers 2017. doi:10.3233/CBM-170643.
[11] Mustaqeem A, Anwar SM, Khan AR, Majid M. A statistical analysis based recommender model for heart disease patients. Int J Med Inf 2017;108:134–45. doi:10.1016/j.ijmedinf.2017.10.008.
[12] Lowe VJ, Wiste HJ, Senjem ML, Weigand SD, Therneau TM, Boeve BF, et al. Widespread brain tau and its association with ageing, Braak stage and Alzheimer’s dementia. Brain J Neurol 2017. doi:10.1093/brain/awx320.
[13] Lopes RR, Navarro J, Silva AJ. Emotions as Proximal Causes of Word of Mouth: A Nonlinear Approach. Nonlinear Dyn Psychol Life Sci 2018;22:103–25.
[14] Mitchell T (1997). Machine Learning. McGraw Hill. ISBN: 978-0070428072
[15] Hastie T, Tibshirani R, Friedman J (2002). The Elements of Statistical Learning.
Springer. ISBN: 978-0387848587
