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This research realised the systems development life cycle of an intelligent decision support platform centred on machine learning models that reliably predict (predictive analytics) and recommend (recommender system) randomised evidence-based interventions. The outcomes of which are routinely evaluated to continuously evolve and optimise the effectiveness of interventions for subsequent application.
Computer Science has become a growingly significant aspect of my life; providing me with the confidence to continue my passion by studying towards a PhD. I studied Computer Science for my Bachelor's, achieving a First-Class Honours before pursuing a Master's in Business Information Technology, achieving not only a Distinction but also the Course Director's Prize for attaining best all-around performance. For the past five years, I have been working for Kingston University in a number of significant capacities, ranging from Systems & Data, Operations and Reward Analyst to Technical Research and Innovation Associate. I am an Associate Fellow of the Higher Education Academy as well as an Associate Member of the British Computer Society. Elsewhere, I have made efforts to broaden my knowledge, completing several industry-approved courses such as the Microsoft Professional Program Certificate in Data Science and Columbia University's Data Science for Executives.
Ahmed, B., Dannhauser, T. and Philip, N. (2018). A systematic review of reviews to identify key research opportunities within the field of eHealth implementation. Journal of Telemedicine and Telecare.
Ahmed, B., Dannhauser, T. and Philip, N. (2018). Intervention Evolution Engine - An Intelligent eHealth Service Delivery Platform. In: 23rd UK Academy for Information Systems International Conference. Oxford, UK.
Ahmed, B., Dannhauser, T. and Philip, N. (2018). A Lean Design Thinking Methodology (LDTM) for Machine Learning and Modern Data Projects. In: 10th Computer Science and Electronic Engineering Conference. Essex, UK.