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An Electronic Health Record–Based Automated Self-Rescheduling Tool to Improve Patient Access: Retrospective Cohort Study

Smitha Ganeshan, Andrew W Liu, Anne Kroeger, Prerna Anand, Richard Seefeldt, Alexis Regner, Diana Vaughn, Anobel Y Odisho, Michelle Mourad

J Med Internet Res 2024;26:e52071

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Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping

To enable stable feature selection from a vast number of behavioral features, Chikersal et al [29] proposed an approach called “nested randomized logistic regression,” which we deployed in this study. This method decomposed the feature space for each sensor by grouping features from the same time slices and performed randomized logistic regression on each of these groups. The selected features from all groups (ie, all time slices) are then concatenated to give a new and much smaller set of features.

Prerna Chikersal, Shruthi Venkatesh, Karman Masown, Elizabeth Walker, Danyal Quraishi, Anind Dey, Mayank Goel, Zongqi Xia