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The Learning Health Community is an emergent global multistakeholder grassroots incipient movement bonded together by a set of consensus developed at the 2012 Learning Health System (LHS) Summit. The Learning Health Community's Second LHS Summit was convened on December 8 to 9, 2016 building upon LHS efforts taking shape in order to achieve consensus on actions that, if taken, will advance LHSs and the LHS vision from what remain appealing concepts to a working reality for improving the health of individuals and populations globally. An iterative half-year collaborative revision process following the Second LHS Summit led to the development of the .
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http://dx.doi.org/10.1002/lrh2.10055 | DOI Listing |
Driven by eutrophication and global warming, the occurrence and frequency of harmful cyanobacteria blooms (CyanoHABs) are increasing worldwide, posing a serious threat to human health and biodiversity. Early warning enables precautional control measures of CyanoHABs within water bodies and in water works, and it becomes operational with high frequency in situ data (HFISD) of water quality and forecasting models by machine learning (ML). However, the acceptance of early warning systems by end-users relies significantly on the interpretability and generalizability of underlying models, and their operability.
View Article and Find Full Text PDFAm J Emerg Med
September 2025
University of Toronto, Rotman School of Management, Canada.
Study Objective: Accurately predicting which Emergency Department (ED) patients are at high risk of leaving without being seen (LWBS) could enable targeted interventions aimed at reducing LWBS rates. Machine Learning (ML) models that dynamically update these risk predictions as patients experience more time waiting were developed and validated, in order to improve the prediction accuracy and correctly identify more patients who LWBS.
Methods: The study was deemed quality improvement by the institutional review board, and collected all patient visits to the ED of a large academic medical campus over 24 months.
JMIR Res Protoc
September 2025
University of Nevada, Las Vegas, Las Vegas, NV, United States.
Background: In-hospital cardiac arrest (IHCA) remains a public health conundrum with high morbidity and mortality rates. While early identification of high-risk patients could enable preventive interventions and improve survival, evidence on the effectiveness of current prediction methods remains inconclusive. Limited research exists on patients' prearrest pathophysiological status and predictive and prognostic factors of IHCA, highlighting the need for a comprehensive synthesis of predictive methodologies.
View Article and Find Full Text PDFJ Exp Anal Behav
September 2025
Fralin Biomedical Research Institute at VTC, Roanoke, VA, United States of America.
Reward delays are often associated with reduced probability of reward, although standard assessments of delay discounting do not specify degree of reward certainty. Thus, the extent to which estimates of delay discounting are influenced by uncontrolled variance in perceived reward certainty remains unclear. Here we examine 370 participants who were randomly assigned to complete a delay discounting task when reward certainty was either unspecified (n=184) or specified as 100% (n = 186) in the task trials and task instructions.
View Article and Find Full Text PDF