Enhancing Activity Recognition After Stroke: Generative Adversarial Networks for Kinematic Data Augmentation.

The generalizability of machine learning (ML) models for wearable monitoring in stroke rehabilitation is often constrained by the limited scale and heterogeneity of available data. Data augmentation addresses this challenge by adding computationally derived data to real data to enrich the variability represented in the training set. Traditional augmentation methods, such as rotation, permutation, and time-warping, have shown some benefits in improving classifier performance, but often fail to produce realistic training examples.

A Quality Initiative to Prioritize Enteral Feeding in Bronchiolitis.

Introduction: Recent studies have identified enteral feeding as a safe alternative to intravenous fluid hydration for inpatients with bronchiolitis receiving respiratory support. Specifically, it can improve vital signs, shorten time on high-flow nasal cannula, and is associated with reduced length of stay. We aimed to increase the percentage of patients receiving enteral feeding on admission with mild-to-moderate bronchiolitis, including those on high-flow nasal cannula, from 83% to 95% within 6 months.

Measuring Healthcare Experiences Among People With Intellectual Disability: A Rapid Evidence Synthesis of Tools and Methods.

Objectives: Patient-reported experience measures (PREMs) collect essential data for service and system-wide quality improvement and performance monitoring toward value-based care. However, the experiences of people with intellectual disability, who have high healthcare utilization couple with poorer outcomes, are often omitted from system-wide PREMs and service-wide PREMs data. The use of PREMs instruments for data collection among people with intellectual disability has not been explored.