Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients.

Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients

Objective The main purpose of this research is the novel use of artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining tool for prediction the outcome of patients with acquired brain injury (ABI) after cognitive rehabilitation. The final goal aims at increasing knowledge in the field of rehabilitation theory based on cognitive affectation. Methods and materials The data set used in this study contains records belonging to 123 ABI patients with moderate to severe cognitive affectation (according to Glasgow Coma Scale) that underwent rehabilitation at Institut Guttmann Neurorehabilitation Hospital (IG) using the tele-rehabilitation platform PREVIRNEC©. The variables included in the analysis comprise the neuropsychological initial evaluation of the patient (cognitive affectation profile), the results of the rehabilitation tasks performed by the patient in PREVIRNEC© and the outcome of the patient after a 3–5 months treatment. To achieve the treatment outcome prediction, we apply and compare three different data mining techniques: the AMMLP model, a backpropagation neural network (BPNN) and a C4.5 decision tree. Results The prediction performance of the models was measured by ten-fold cross validation and several architectures were tested. The results obtained by the AMMLP model are clearly superior, with an average predictive performance of 91.56%. BPNN and C4.5 models have a prediction average accuracy of 80.18% and 89.91% respectively. The best single AMMLP model provided a specificity of 92.38%, a sensitivity of 91.76% and a prediction accuracy of 92.07%. Conclusions The proposed prediction model presented in this study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients. The ability to predict treatment outcomes may provide new insights toward improving effectiveness and creating personalized therapeutic interventions based on clinical evidence.

Clustering techniques for patients suffering acquired brain injury inneuro personal trainer

The study of the effectiveness of the cognitive rehabilitation processes and the identification of cognitive profiles, in order to define comparable populations, is a controversial area, but concurrently it is strongly needed in order to improve therapies. There is limited evidence about cognitive rehabilitation efficacy. Many of the trials conclude that in spite of an apparent clinical good response, differences do not show statistical significance. The common feature in all these trials is heterogeneity among populations. In this situation, observational studies on very well controlled cohort of studies, together with innovative methods in knowledge extraction, could provide methodological insights for the design of more accurate comparative trials. Some correlation studies between neuropsychological tests and patients capacities have been carried out -1---2- and also correlation between tests and morphological changes in the brain -3-. The procedures efficacy depends on three main factors: the affectation profile, the scheduled tasks and the execution results. The relationship between them makes up the cognitive rehabilitation as a discipline, but its structure is not properly defined. In this work we present a clustering method used in Neuro Personal Trainer (NPT) to group patients into cognitive profiles using data mining techniques. The system uses these clusters to personalize treatments, using the patients assigned cluster to select which tasks are more suitable for its concrete needs, by comparing the results obtained in the past by patients with the same profile.

Intelligent Therapy Assistant (ITA) for cognitive rehabilitation in patients with Acquired Brain Injury

This paper presents the design, development and first evaluation of an algorithm, named Intelligent Therapy Assistant (ITA), which automatically selects, configures and schedules rehabilitation tasks for patients with cognitive impairments after an episode of Acquired Brain Injury. The ITA is integrated in "Guttmann, Neuro Personal Trainer" (GNPT), a cognitive tele-rehabilitation platform that provides neuropsychological services.

Physical activity cost in children following an acquired brain injury - a comparative study

Background: Alterations in energy expenditure during activity post head injury has not been investigated due primarily to the difficulty of measurement. Objective: The aim of this study was to compare energy expenditure during activity and body composition of children following acquired brain injury (ABI) with data from a group of normal. controls. Design: Energy expenditure was measured using the Cosmed K4b(2) in a group of 15 children with ABI and a group of 67 normal children during rest and when walking and running. Mean number of steps taken per 3 min run was also recorded and body composition was measured. Results: The energy expended during walking was not significantly different between both groups. A significant difference was found between the two groups in the energy expended during running and also for the number of steps taken as children with ABI took significantly less steps than the normal controls during a 3 min run. Conclusions: Children with ABI exert more energy per activity than healthy controls when controlled for velocity or distance. However, they expend less energy to walk and run when they are free to choose their own desirable, comfortable pace than normal controls. (C) 2003 Elsevier Ltd. All rights reserved.

Interventions for improving self-awareness following acquired brain injury

Accurate self-awareness in clients who have had an acquired brain injury (ABI) has been associated with positive outcomes. However, providing intervention that improves clients' self-awareness is a challenging task for occupational therapists. The present paper provides an overview of the literature regarding models to guide intervention, intervention considerations, descriptions of interventions, and research evidence for interventions. Professionals can draw upon cognitive rehabilitation models and specific models of self-awareness. Facilitatory interventions, such as education, feedback, behaviour therapy and psychotherapy have been recommended to a greater extent than compensatory interventions. The development of interventions for improving self-awareness is at an early stage, and research on the effectiveness of interventions is limited. Future research is required into the effectiveness of interventions to improve clients' self-awareness before structured intervention guidelines can be developed.

The effect of a perceptual cognitive task on exercise performance: the dual-task condition after brain injury

Objective: To examine the effect of additional cognitive demand on cycling performance in individuals with acquired brain injury (ABI). Design: Prospective observational study. Setting: Rivermead Rehabilitation Centre. Participants: Ten individuals with ABI ( 7 men, 3 women) ( traumatic brain injury 7, tumour 1, stroke 2) and 10 healthy controls ( 6 men, 4 women). Intervention: Individuals were asked to maintain a set cadence during a three-stage incremental cycling test in both single-task ( no additional task) and dual-task ( whilst performing an additional cognitive task) conditions. Results: The ABI group showed a slight slowing in cadence in stages 1 and 3 of the graded exercise test from the single-to the dual-task condition, although this was not significant ( p less than or equal to 0.05). The control group showed no slowing of cadence at any incremental stage. When directly comparing the ABI with the control group, the change in cadence observed in dual-task conditions was only significantly different in stage 3 ( p less than or equal to 0.05). Conclusions: Clinicians should be aware of the possibility that giving additional cognitive tasks ( such as monitoring exercise intensity) while individuals with acquired brain injury are performing exercises may detrimentally affect performance. The effect may be more marked when the individuals are performing exercise at higher intensities.

Improving brain injury cognitive rehabilitation by personalized telerehabilitation services: Guttmann neuropersonal trainer

Cognitive rehabilitation aims to remediate or alleviate the cognitive deficits appearing after an episode of acquired brain injury (ABI). The purpose of this work is to describe the telerehabilitation platform called Guttmann Neuropersonal Trainer (GNPT) which provides new strategies for cognitive rehabilitation, improving efficiency and access to treatments, and to increase knowledge generation from the process. A cognitive rehabilitation process has been modeled to design and develop the system, which allows neuropsychologists to configure and schedule rehabilitation sessions, consisting of set of personalized computerized cognitive exercises grounded on neuroscience and plasticity principles. It provides remote continuous monitoring of patient's performance, by an asynchronous communication strategy. An automatic knowledge extraction method has been used to implement a decision support system, improving treatment customization. GNPT has been implemented in 27 rehabilitation centers and in 83 patients' homes, facilitating the access to the treatment. In total, 1660 patients have been treated. Usability and cost analysis methodologies have been applied to measure the efficiency in real clinical environments. The usability evaluation reveals a system usability score higher than 70 for all target users. The cost efficiency study results show a relation of 1-20 compared to face-to-face rehabilitation. GNPT enables brain-damaged patients to continue and further extend rehabilitation beyond the hospital, improving the efficiency of the rehabilitation process. It allows customized therapeutic plans, providing information to further development of clinical practice guidelines.