Prediction of Outcome in the Vegetative State by Machine Learning Algorithms: A Model for Clinicians?

Loris Pignolo, Vincenzo i Lagan
DOI: 10.4236/jsea.2011.46044                

Abstract

Purpose of this study was to compare different Machine Learning classifiers (C4.5, Support Vector Machine, Naive Bayes, K-NN) in the early prediction of outcome of the subjects in vegetative state due to traumatic brain injury. Accuracy proved acceptable for all compared methods (AUC > 0.8), but sensitivity and specificity varied considerably and only some classifiers (in particular, Support Vector Machine) appear applicable models in the clinical routine. A combined use of classifiers is advisable.

Keywords

Artificial Intelligence, Vegetative State, Clinical Outcome, Prognosis

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	B. Jennett and F. Plum, “Persistent Vegetative State after Brain Damage: A Syndrome in Search of a Name,” Lancet, Vol. 1, 1972, pp. 734-619. doi:10.1016/S0140-6736(72)90242-5
[2]	G. Dolce and L. Sazbon, “The Posttraumatic Vegetative state,” Stuttgart, Thiene, 2002.
[3]	S. Laureys, “The Neural Correlate of (un) Awareness: Lessons from the Vegetative State,” Trends in Cognitive Sciences, Vol. 9, No. 12, 2005, pp. 556-559. doi:10.1016/j.tics.2005.10.010
[4]	B. Jennett, “The Vegetative State,” Cambridge University Press, Cambridge, 2002. doi:10.1017/CBO9780511545535
[5]	Multi-Society Task Force on PVS, “Statement on Medical Aspects of the Persistent Vegetative State,” The New England Journal of Medicine, Vol. 330, No. 21, 1994, pp. 1499-1508. doi:10.1056/NEJM199405263302107
[6]	A. Zeman, “Consciousness,” Brain, Vol. 124, 2001, pp. 1263-1289.
[7]	R. Braakman, W. B. Jennett and J. M. Minderhoud, “Prognosis of the Post Traumatic Vegetative State,” Acta Neurologica Scandinavica, Vol. 95, 1988, pp. 49-52. doi:10.1007/BF01793082
[8]	E. Schmutzard, A. Kampfl, G. Franz, B. Pfausler, H. P. Haring, H. Ulmer, S. Felber, S. Golaszewski and F. Aichner, “Prediction of Recovery from Post Traumatic Vegetative State with Cerebral Magnetic-Resonance Imaging,” Lancet, Vol. 351, No. 9118, 1998, pp. 1663-1671.
[9]	A. Rovlias and S. Kotsou, “Classification and Regression Tree for Prediction of Outcome after Severe Head Injury Using Simple Clinical and Laboratory Variables,” Journal of Neurotrauma, Vol. 21, 2004, pp. 886-893. doi:10.1089/0897715041526249
[10]	G. Dolce, M. Quintieri, S. Serra, V. Lagani and L. Pignolo, “Clinical Signs and Early Prognosis: A Decisional Tree, Data Mining Study,” Brain Injury, Vol. 22, No. 7-8, 2008, pp. 617-623. doi:10.1080/02699050802132503
[11]	L. Pignolo, M. Quintieri and W. G. Sannita, “The Glasgow Outcome Scale in Vegetative State: A Possible Source of Bias,” Brain Injury, Vol. 23, No. 1, 2009, pp. 1-2. doi:10.1080/02699050802595873
[12]	K. Andrews (Chairman), “International Working Party Report on the Vegetative State,” Royal Hospital for Neuro-Disability, 1996. http://www.comarecovery.org/s.htm
[13]	G. Teasdale and B. Jennet, “Assessment of Coma and Impaired Consciousness: A Practical Scale,” Lancet, Vol. 2, 1974, pp. 81-84. doi:10.1016/S0140-6736(74)91639-0
[14]	B. Jennet and M. Bond, “Assessment Outcome after Severe Brain Damage: A Practical Scale,” Lancet, Vol. 1, 1976, pp. 480-484.
[15]	G. Shakhnarovish, T. Darrell and P. Indyk, “Nearest-Neighbor Methods in Learning and Vision,” The MIT Press, Cambridge, 2005.
[16]	P. Domingos and M. Pazzani, “On the Optimality of the Simple Bayesian Classifier under Zero-One Loss,” Machine Learning, Vol. 29, 1997, pp. 103-137. doi:10.1023/A:1007413511361
[17]	J. R. Quinlan, “C4.5: Programs for Machine Learning,” Morgan Kaufmann Publishers, Waltham, 1993.
[18]	L. Breiman, J. H. Friedman and R. A. Olshen, “Classification and Regression Trees,” Wadsworth International, Belmont, CA, 1984.
[19]	N. Cristianini and J. Shawe-Taylor, “An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods,” Cambridge University Press, Cambridge, 2000.
[20]	R. C. Holte, “Very Simple Classification Rules Perform Well on Most Commonly Used Datasets,” Machine Learning, Vol. 11, No. 1, 1993, pp. 63-90. doi:10.1023/A:1022631118932
[21]	F. Moser, G. E. Rong and E. Martin, “Joint Cluster Analysis of Attribute and Relationship Data without Apriori Specification of the Number of Clusters,” In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, California, 12-15 August 2007.
[22]	M. Stone, “Cross-Validatory Choice and Assessment of Statistical Predictions,” Journal of the Royal Statistical Society, Vol. 36, No. 2, 1974, pp. 111-147.
[23]	R. Picard and D. Cook, “Cross-Validation of Regression Models,” Journal of the American Statistical Association, Vol. 79, No. 387, 1984, pp. 575-583. doi:10.2307/2288403
[24]	M. L. Thompson and W. Zucchini, “On the Statistical Analysis of ROC Curves,” Statistics in Medicine, Vol. 8, No. 10, 1989, pp. 1277-1290. doi:10.1002/sim.4780081011
[25]	M. S, Pepe, “A Regression Modelling Framework for ROC Curves in Medical Diagnostic Testing,” Biometrika, Vol. 84, 1998, pp. 595-608.
[26]	R. W. Thatcher, R. A. Walker, C. J. Biver, D. M. North and R. Curtin, “Sensitivity and Specificity of an EEG Normative Database: Validation and Clinical Correlation,” Journal of Neurotherapy, Vol. 7, No. 3-4, 2003, pp. 87-121. doi:10.1300/J184v07n03_05
[27]	E. Frank, “Machine Learning with WEKA,” University of Waikato, New Zealand, 1999. http://www.cs.columbia.edu/~jaxin/nlpmeetings/2004-04-22-lokesh.ppt http://ww w.cs.waikato.ac.nz/ml/weka
[28]	J. H. Van Bemmel and M. A. Munsen, “Handbook of Medical Informatics,” Springer-Verlag, Berlin, 1997.
[29]	M. J. Osborne and A. Rubenstein, “A Course in Game Theory,” MIT Press, Cambridge, 1994.