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Reporting serendipity in biomedical research literature : a mixed-methods analysis
(University of Missouri--Columbia, 2018)
As serendipity is an unexpected, anomalous, or inconsistent observation that culminates in a valuable, positive outcome (McCay-Peet & Toms, 2018, pp. 4–6), it can be inferred that effectively supporting serendipity will ...
Explainable cohort discoveries driven by exploratory data mining and efficient risk pattern detection
(University of Missouri--Columbia, 2022)
Finding small homogeneous subgroup cohorts in a large heterogeneous population is a critical process for hypothesis development within a broad range of applications, such as fraud detection, ad targeting, and geospatial ...
Domain-concept mining : an efficient on-demand data mining approach
(University of Missouri--Columbia, 2008)
Traditional brute-force association mining approaches, when applied to large datasets, are thorough but inefficient due to computational complexity. A low global minimum probability threshold can worsen this complexity by ...
Accelerating data-driven discover in type 1 diabetes: an informatics-based approach
(University of Missouri--Columbia, 2022)
Type 1 diabetes (T1D) is a lifelong chronic disease characterized by the absolute or near-absolute loss of insulin. For affected individuals, management of T1D is an unremitting challenge that involves constant blood glucose ...