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

Are some brain injury patients improving more than ohers?

Predicting the evolution of individuals is a rather new mining task with applications in medicine. Medical researchers are interested in the progress of a disease and in the evolution of individuals subjected to treatment. We investigate the evolution of patients on the basis of medical tests before and during treatment after brain trauma: we want to understand how similar patients can become to healthy participants. We face two challenges. First, we have less information on healthy participants than on the patients. Second, the values of the medical tests for patients, even after treatment started, remain well-separated from those of healthy people; this is typical for neurodegenerative diseases, but also for further brain impairments. Our approach encompasses methods for modelling patient evolution and for predicting the health improvement of different patient subpopulations, dealing with the above challenges. We test our approach on a cohort of patients treated after brain trauma and a corresponding cohort of controls

On the Biological Plausibility of Artificial Metaplasticity

The training algorithm studied in this paper is inspired by the biological metaplasticity property of neurons. Tested on different multidisciplinary applications, it achieves a more efficient training and improves Artificial Neural Network Performance. The algorithm has been recently proposed for Artificial Neural Networks in general, although for the purpose of discussing its biological plausibility, a Multilayer Perceptron has been used. During the training phase, the artificial metaplasticity multilayer perceptron could be considered a new probabilistic version of the presynaptic rule, as during the training phase the algorithm assigns higher values for updating the weights in the less probable activations than in the ones with higher probabilit

Toward more successful biomedical informatics education programs and ecosystems in the Arab world

Biomedical & Health Informatics (BMHI) is relatively new in Arab States. However, several programs/ tracks are running, with high promises of expansion. Programs are evaluated by national authorities, not by a specialized body/association. This does not always mean that the program is of an international standard. One of the possible ways of ensuring the quality of these programs is to be evaluated by international agencies. The International Medical Informatics Association (IMIA) has the expertise in the evaluation BMHI education programs. Accredited programs staffs will have the opportunities for Internationalization and to be engaged with other top-notch organizations, which will have great impacts on the overall implementations of the BMHI in the Arab World. The goal of this document is to show to Arab Universities (pilot: Egypt) how to apply for IMIA Accreditation for their programs. © 2015 The authors and IOS Press. All rights reserved

IMIA dynamic accreditation procedure: Suggestions, simplicity and efficiency

The International Medical Informatics Association (IMIA) is the world body for biomedical and health informatics (BMHI). IMIA accreditation program allows the health and medical informatics programs around the world to reach to an international level. Staffs (professors, students, education programmes directors, others) that work on the accredited BMHI programs will have the opportunity to be engaged with organizations that possess a world-class research and education profile from other countries, which will have great impacts on their field at their institutions, within their country providing the high quality overall health services. IMIA accreditation procedure is usually a long process and slightly complicated. The goal of this paper is to illustrate and to simplify the IMIA accreditation process to increase the success of the applicants. Toward more dynamic IMIA accreditation procedure, the paper presents 4 steps: translation, IMIA-Accreditation Step by Step Guideline, Questions and Answers, and finally the (new) Labelling System. © 2015 The authors and IOS Press. All rights reserved