A data mining approach to identify cognitive NeuroRehabilitation Range in Traumatic Brain Injury patients

Abstract Cognitive rehabilitation (CR) treatment consists of hierarchically organized tasks that require repetitive use of impaired cognitive functions in a progressively more demanding sequence. Active monitoring of the progress of the subjects is therefore required, and the difficulty of the tasks must be progressively increased, always pushing the subjects to reach a goal just beyond what they can attain. There is an important lack of well-established criteria by which to identify the right tasks to propose to the patient. In this paper, the NeuroRehabilitation Range (NRR) is introduced as a means of identifying formal operational models. These are to provide the therapist with dynamic decision support information for assigning the most appropriate CR plan to each patient. Data mining techniques are used to build data-driven models for NRR. The Sectorized and Annotated Plane (SAP) is proposed as a visual tool by which to identify NRR, and two data-driven methods to build the SAP are introduced and compared. Application to a specific representative cognitive task is presented. The results obtained suggest that the current clinical hypothesis about NRR might be reconsidered. Prior knowledge in the area is taken into account to introduce the number of task executions and task performance into NRR models and a new model is proposed which outperforms the current clinical hypothesis. The NRR is introduced as a key concept to provide an operational model identifying when a patient is experiencing activities in his or her Zone of Proximal Development and, consequently, experiencing maximum improvement. For the first time, data collected through a CR platform has been used to find a model for the NRR.

[1]  Eric I. Knudsen,et al.  Incremental training increases the plasticity of the auditory space map in adult barn owls , 2002, Nature.

[2]  Allen W. Brown,et al.  Clinical elements that predict outcome after traumatic brain injury: a prospective multicenter recursive partitioning (decision-tree) analysis. , 2005, Journal of neurotrauma.

[3]  Vincent Corruble,et al.  Predicting recovery in patients suffering from traumatic brain injury by using admission variables and physiological data: a comparison between decision tree analysis and logistic regression. , 2002, Journal of neurosurgery.

[4]  M. D. Calero,et al.  Cognitive plasticity as a modulating variable on the effects of memory training in elderly persons. , 2007, Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists.

[5]  J. Arcos,et al.  Cognitive Prognosis of Acquired Brain Injury Patients Using Machine Learning Techniques , 2013 .

[6]  Enrique J. Gómez,et al.  Data mining applied to the cognitive rehabilitation of patients with acquired brain injury , 2013, Expert Syst. Appl..

[7]  S. Assouline,et al.  Proceedings from the 1993 Henry B. and Jocelyn Wallace National Research Symposium on Talent Development , 1994 .

[8]  Joseph T. DiPiro,et al.  Concepts in Clinical Pharmacokinetics , 1996 .

[9]  Constantine Stephanidis,et al.  Universal Access in Human-Computer Interaction. Ambient Interaction, 4th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2007 Held as Part of HCI International 2007 Beijing, China, July 22-27, 2007 Proceedings, Part II , 2007, HCI.

[10]  A. Rovlias,et al.  Classification and regression tree for prediction of outcome after severe head injury using simple clinical and laboratory variables. , 2004, Journal of neurotrauma.

[11]  Lee Humphreys,et al.  Digital Media: Transformations in Human Communication , 2006 .

[12]  Rebecca Smith,et al.  A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries , 2009, BMC Medical Informatics Decis. Mak..

[13]  Jacqui Crosbie,et al.  Adaptive Virtual Reality Games for Rehabilitation of Motor Disorders , 2007, HCI.

[14]  Josh Bongard,et al.  C L I N I C a L a R T I C L E , 2022 .

[15]  Richard Goldstein,et al.  The Accuracy of Artificial Neural Networks in Predicting Long‐term Outcome After Traumatic Brain Injury , 2006, The Journal of head trauma rehabilitation.

[16]  J. Kleim,et al.  Principles of experience-dependent neural plasticity: implications for rehabilitation after brain damage. , 2008, Journal of speech, language, and hearing research : JSLHR.

[17]  D. Fensterheim,et al.  Tele-Rehabilitation using the Rutgers Master II glove following Carpal Tunnel Release surgery , 2006, 2006 International Workshop on Virtual Rehabilitation.

[18]  Nicole Fruehauf.  Flow The Psychology Of Optimal Experience , 2016 .

[19]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[20]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[21]  Daniel B. Horn,et al.  The roles of task difficulty and prior videogame experience on performance and motivation in instructional videogames , 2008, Comput. Hum. Behav..

[22]  K. Cicerone,et al.  Cognitive Assessment in the Neuropsychological Rehabilitation of Head-Injured Adults , 1986 .

[23]  Joanne Azulay,et al.  Evidence-based cognitive rehabilitation: updated review of the literature from 2003 through 2008. , 2011, Archives of physical medicine and rehabilitation.

[24]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[25]  Edward E. Rigdon,et al.  Play, Flow, and the Online Search Experience , 2004 .

[26]  L. Merabet,et al.  The plastic human brain cortex. , 2005, Annual review of neuroscience.

[27]  V. Jagaroo.  Neuroinformatics for Neuropsychology , 2009 .

[28]  Michael Stonebraker,et al.  The Morgan Kaufmann Series in Data Management Systems , 1999 .

[29]  C S Green,et al.  Action-Video-Game Experience Alters the Spatial Resolution of Vision , 2007, Psychological science.

[30]  Ping Zhang,et al.  Flow in Computer-Mediated Environments: Promises and Challenges , 2005, Commun. Assoc. Inf. Syst..

[31]  M. Ríos-Lago,et al.  Efectividad de la rehabilitación neuropsicológica en el daño cerebral adquirido (I): atención, velocidad de procesamiento, memoria y lenguaje , 2010 .

[32]  Ian H. Witten,et al.  Data mining - practical machine learning tools and techniques, Second Edition , 2005, The Morgan Kaufmann series in data management systems.

[33]  John Whyte,et al.  It’s More Than a Black Box; It’s a Russian Doll: Defining Rehabilitation Treatments , 2003, American journal of physical medicine & rehabilitation.

[34]  B. Ang,et al.  Hybrid outcome prediction model for severe traumatic brain injury. , 2007, Journal of Neurotrauma.

[35]  Pedro Miguel Moreira,et al.  Serious games for rehabilitation: A survey and a classification towards a taxonomy , 2010, 5th Iberian Conference on Information Systems and Technologies.

[36]  C. Mateer,et al.  Cognitive Rehabilitation: An Integrative Neuropsychological Approach , 2001 .

[37]  Alejandro García-Rudolph,et al.  Information and communication technology in learning development and rehabilitation , 2009, International Journal of Integrated Care.

[38]  R. Bullock,et al.  Moderate and severe traumatic brain injury in adults , 2008, The Lancet Neurology.

[39]  O. Muldoon,et al.  Acquired brain injury: combining social psychological and neuropsychological perspectives , 2014, Health psychology review.

[40]  M. Faust,et al.  Effectiveness of cognitive rehabilitation following acquired brain injury: a meta-analytic re-examination of Cicerone et al.'s (2000, 2005) systematic reviews. , 2009, Neuropsychology.

[41]  J. Giacino,et al.  Evidence-based cognitive rehabilitation: updated review of the literature from 1998 through 2002. , 2005, Archives of physical medicine and rehabilitation.