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.

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.

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.

Artificial metaplasticity neural network applied to credit scoring.

The assessment of the risk of default on credit is important for financial institutions. Different Artificial Neural Networks (ANN) have been suggested to tackle the credit scoring problem, however, the obtained error rates are often high. In the search for the best ANN algorithm for credit scoring, this paper contributes with the application of an ANN Training Algorithm inspired by the neurons' biological property of metaplasticity. This algorithm is especially efficient when few patterns of a class are available, or when information inherent to low probability events is crucial for a successful application, as weight updating is overemphasized in the less frequent activations than in the more frequent ones. Two well-known and readily available such as: Australia and German data sets has been used to test the algorithm. The results obtained by AMMLP shown have been superior to state-of-the-art classification algorithms in credit scoring.