SummaryThere has been a growing interest in Health Informatics applications, research, and education within the Middle East and North African Region over the past twenty years. People of this region share similar cultural and religious values, primarily speak the Arabic language, and have similar health care related issues, which are in dire need of being addressed. Health Informatics efforts, organizations, and initiatives within the region have been largely under-represented within, but not ignored by, the International Medical Informatics Association (IMIA). Attempts to create bonds and collaboration between the different organizations of the region have remained scattered, and often, resulted in failure despite the fact that the need for a united health informatics collaborative within the region has never been more crucial than today. During the 2017 MEDINFO, held in Hangzhou, China, a new organization, the Middle East and North African Health Informatics Association (MENAHIA) was conceived as a regional non-governmental organization to promote and facilitate health informatics uptake within the region endorsing health informatics research and educational initiatives of the 22 countries represented within the region. This paper provides an overview of the collaboration and efforts to date in forming MENAHIA and displays the variety of initiatives that are already occurring within the MENAHIA region, which MENAHIA will help, endorse, support, share, and improve within the international forum of health informatics.
As per WHO report, Tuberculosis remains one of the world's deadliest communicable diseases. In 2013, an estimated 9.0 million developed TB and 1.5 million died from the disease, 360,000 of which whom were HIV positive. Tuberculosis is still a major problem in advanced countries due to specific socioeconomic factors. From a global perspective, many laboratories use the same methods today that were in use long time ago for the detection of tuberculosis, because most of innovative current technologies for the detection of tuberculosis incurs high cost and cannot be afforded for all the countries. The detection of tuberculosis remains a challenge from the point of diagnosis and confirmation and there is a growing need of accurate diagnosis process. In this research, an ontology based classification of tuberculosis laboratory tests, environmental factors and other vital signs are studied along with support vector machine for the diagnosis of the tuberculosis disease. Through this method, we are able to measure of the weightage of the disease, the future onset of the disease and produce, an alert. Ontology based classification is widely used for knowledge based information grouping and structuring while SVM is used for accurate and fast machine learning algorithm. By combining Ontology and the training data based on various characteristic of the tuberculosis are passed onto linear SVM. The results we are able to achieve with this method are helpful for diagnosis support and future onset.
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