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Traditional time-series classification methods often operate as black boxes, providing a class prediction without indicating which time point or time interval within the series led to that prediction. To improve temporal interpretability, MILLET (Multiple Instance Learning with Temporal Explanation), which combines an...
PurposeTo examine the association between sleep duration and metabolic syndrome (MetS) prevalence in U.S. adults.DesignCross-sectional analysis of National Health and Nutrition Examination Survey (NHANES) data.SettingUnited States.Sample14 982 adults (≥18 years) from 7 NHANES cycles (2005-2020) with complete data on sl...
A systematic MCDM approach is developed using newly defined operators to address complex decision problems with multiple and hesitant criteria to increase the flexibility of information aggregation but also improve decision-making accuracy and robustness.
It is suggested that serum 25(OH)D may serve as a metabolic biomarker associated with insulin resistance, while prospective and interventional studies are needed to clarify temporality and causality.
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