Decision fusion in healthcare and medicine : a narrative review

Objective: To provide an overview of the decision fusion (DF) technique and describe the applications of the technique in healthcare and medicine at prevention, diagnosis, treatment and administrative levels. Background: The rapid development of technology over the past 20 years has led to an explosion in data growth in various industries, like healthcare. Big data analysis within the healthcare systems is essential for arriving to a value-based decision over a period of time. Diversity and uncertainty in big data analytics have made it impossible to analyze data by using conventional data mining techniques and thus alternative solutions are required. DF is a form of data fusion techniques that could increase the accuracy of diagnosis and facilitate interpretation, summarization and sharing of information. Methods: We conducted a review of articles published between January 1980 and December 2020 from various databases such as Google Scholar, IEEE, PubMed, Science Direct, Scopus and web of science using the keywords decision fusion (DF), information fusion, healthcare, medicine and big data. A total of 141 articles were included in this narrative review. Conclusions: Given the importance of big data analysis in reducing costs and improving the quality of healthcare; along with the potential role of DF in big data analysis, it is recommended to know the full potential of this technique including the advantages, challenges and applications of the technique before its use. Future studies should focus on describing the methodology and types of data used for its applications within the healthcare sector

Distance Measures in Bioinformatics

Many bioinformatics applications rely on the computation of similarities between objects. Distance and similarity measures applied to vectors of characteristics are essential to problems such as classification, clustering and information retrieval. This study explores the usefulness of distance and similarity measures in several bioinformatics applications. These applications are in two categories. (1) Estimation of the adverse reaction severity of unknown pharmaceutical treatments, based on the severity of known treatments, in order to provide guidance for testing of the unknown treatments in clinical trials. (2) Classification of cancer tissue types and estimation of cancer stages, based on high-dimensional microarray data, in order to support clinical decisions making. To address the first category, we studied several clustering and classification approaches for binary severity estimation of Cytokine Release Syndrome (CRS). We developed a Severity Estimation using Distance Metric Learning (SE-DML) approach to get graded severity estimation. With binary estimation we were able to identify treatments that caused the most severe response and then built prediction models for CRS. Using the SE-DML approach, we evaluated four known data sets and showed that SE-DML outperformed other widely used methods on these data sets. For the second category, we presented Kernelized Information-Theoretic Metric Learning (KITML) algorithms that optimize distance metrics and effectively handle high-dimensional data. This learned metric by KITML is used to improve the performance of $k$-nearest neighbor classification for cancer tissue microarray data. We evaluated our approach on fourteen (14) cancer microarray data sets and compared our results with other state-of-the-art approaches. We achieved the best overall performance for the classification task. In addition we tested the KITML algorithm in estimating the severity stages of cancer samples, with accurate results.Ph.D., Electrical Engineering -- Drexel University, 201