PURPOSE: Identifying factors predicting the spontaneous passage of distal ureteral stones and evaluating the effectiveness of artificial intelligence in prediction.
This study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification tools to identify high-risk patients for early intervention. While several scoring systems, often bas...
BACKGROUND: Unscheduled return visits (URVs) to emergency departments (EDs) are used to assess the quality of care in EDs. Machine learning (ML) models can incorporate a wide range of complex predictors to identify high-risk patients and reduce error...
BACKGROUND: Deep learning models have had a lot of success in various fields. However, on structured data they have struggled. Here we apply four state-of-the-art supervised deep learning models using the attention mechanism and compare against logis...
Driven by advancements in data-driven methods, recent developments in proactive crash prediction models have primarily focused on implementing machine learning and artificial intelligence. However, from a causal perspective, statistical models are pr...
OBJECTIVE: Preterm birth remains the predominant cause of perinatal mortality throughout the United States and the world, with well-documented racial and socioeconomic disparities. To develop and validate a predictive algorithm for all-cause preterm ...
Satellite remote sensing is widely being used by the researchers and geospatial scientists due to its free data access for land observation and agricultural activities monitoring. The world is suffering from food shortages due to the dramatic increas...