Abstract
Healthcare has seen a great evolution in current era in terms of new computer technologies. Intensive medical data is generated that opens up research in healthcare analytics. Coping with this intensive data along with making it meaningful to deliver knowledge and be able to make decisions are the most important tasks. To deduce the authenticity of the data on basis of precision, correction, associations and true meaning is important to validate the understanding of correct semantics. In case of medical diagnosis to form accurate understanding of associations while removing ambiguity and forming a correct picture of the case is of utmost importance. To come up with the right metrics for the diagnostic solution we have explored the known criteria to validate healthcare analytics techniques involved in formation of diagnosis that results in betterment and safety of patients under observations and heading towards possible treatments. In this work, we have proposed a thematic taxonomy for the comparison of existing healthcare analytics techniques with emphasis on diabetes and its underlying diseases. This analysis lead us to propose a data model for hybrid distributed simulation model for future Context Aware SmartHealth cloud platform for diagnostics. This platform is designed to inherit smartness of unsupervised learning which in turn would keep updating itself under supervised learning by qualified experts. Finally, the accuracy would be determined using HUM approach with biomarkers or a better accuracy model than AUC. The recommended action plan is also presented.
Highlights
Going through some early histories of medical informatics [1] we get to know of the major research that was carried out in 1977 in Bosnia and Herzegovina
Based on our study of previous healthcare analytics techniques been utilized for prediction, diagnosis, treatments, and prognosis it is determined that researchers have focused over best approaches with respect to the maximum accuracy level achieved
We are working towards proposing a diagnostic system keeping in view for its commercial universal use by clinicians for various chronic diseases like; diabetes mellitus and its underline diseases liver cirrhosis
Summary
Going through some early histories of medical informatics [1] we get to know of the major research that was carried out in 1977 in Bosnia and Herzegovina. Biggest progress was seen in pharmaceutical industry where 43 pharmacies in Sarajevo centrally connected for receipt collection and analysis It took three years of testing and Izet Masic defended it in his Master‘s thesis [1] but the later planned activities got interrupted during and after war (1992-1995) and due to lack of funds. After the war (1992-1995), the B&H went through a very tough time with lack of electricity, gas, water supply and food During these circumstances Society for Medical Informatics carried out eight scientific and professional events where 500 papers were presented and published in the proceedings. It was found that artificial neural networks were the most used technique while other analytical tools were used and those included fuzzy expert systems, hybrid intelligent systems, and evolutionary computation to support healthcare workers in their duties, and assisting in tasks of manipulation with data and knowledge. The challenge lies in development of automated medical diagnostics to reap its long term benefits to human kind is enough of motivation to dig deep into this domain
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