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Extending Association Rule Summarization Techniques to Assess Risk of Diabetes Mellitus., , , , , and . IEEE Trans. Knowl. Data Eng., 27 (1): 130-141 (2015)Type 2 Diabetes Mellitus Trajectories and Associated Risks., , , , , , and . Big Data, 4 (1): 25-30 (2016)Reduction of unnecessary thyroid biopsies using deep learning., , , , , , , , and . Medical Imaging: Image Processing, volume 10949 of SPIE Proceedings, page 109490W. SPIE, (2019)Frequent Causal Pattern Mining: A Computationally Efficient Framework For Estimating Bias-Corrected Effects., , , , , and . IEEE BigData, page 1981-1990. IEEE, (2019)Divisive Hierarchical Clustering towards Identifying Clinically Significant Pre-Diabetes Subpopulations., , , , , and . AMIA, AMIA, (2014)A Computational Method for Learning Disease Trajectories From Partially Observable EHR Data., , , , , , and . IEEE J. Biomed. Health Informatics, 25 (7): 2476-2486 (2021)Evaluating the Impact of Data Representation on EHR-Based Analytic Tasks., , , , , , and . MedInfo, volume 264 of Studies in Health Technology and Informatics, page 288-292. IOS Press, (2019)Towards more Accessible Precision Medicine: Building a more Transferable Machine Learning Model to Support Prognostic Decisions for Micro- and Macrovascular Complications of Type 2 Diabetes Mellitus., , , , and . J. Medical Systems, 43 (7): 185:1-185:12 (2019)A novel method for Causal Structure Discovery from EHR data, a demonstration on type-2 diabetes mellitus., , , , , and . CoRR, (2020)Estimating Disease Onset Time by Modeling Lab Result Trajectories via Bayes Networks., , , , , , and . ICHI, page 374-379. IEEE Computer Society, (2017)