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

• Published in: Expert systems with applications Vol. 41; no. 11; pp. 5238 - 5251
• Main Authors: García-Rudolph, Alejandro, Gibert, Karina
• Format: Journal Article Publication
• Language: English
• Published: Amsterdam Elsevier Ltd 01.09.2014; Elsevier
• Subjects: 62H Multivariate analysis; Algorithms; Anàlisi multivariable; Anàlisi multivariant; Applied sciences; Biological and medical sciences; Child clinical studies; Cognitive tasks; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Construction; Construction equipment; Criteria; Data mining; Data processing. List processing. Character string processing; Decision theory. Utility theory; Estadística aplicada; Estadística biosanitària; Estadística matemàtica; Exact sciences and technology; Machine learning; Matemàtiques i estadística; Medical sciences; Memory organisation. Data processing; Multivariate analysis; Operational research and scientific management; Operational research. Management science; Organic mental disorders. Neuropsychology; Patients; Psychology. Psychoanalysis. Psychiatry; Psychopathology. Psychiatry; Software; Statistics; Tasks; Àrees temàtiques de la UPC; Statistics; Algorithms; Machine learning; Proximal; Brain; Human operator; Cognitive disorder; Injury; Central nervous system; Cognition; Data driven modelling; Data mining; Modeling; Task analysis; Encephalon; Hierarchy; Formal specification; Graphical interface; Medical application; Monitoring; Repetition; Data analysis; Statistical analysis; Decision support system; Model driven architecture; Annotation; Cognitive theory; Surveillance; Learning (artificial intelligence); Data models; Rehabilitation; Hierarchical system
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2014.03.001

•A platform to search for data driven flow experiences in patients is introduced.•Traumatic Brain Injury patients rehabilitation plans based on empirical clinical data.•Zone of rehabilitation potential modeled using NeuroRehabilitation Range (NRR).•Analytical and visual tools are introduced and validated to search data driven NRR.•Obtained results lead to actual clinical NRR hypothesis reconsideration. 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.