1. Predicting Urgent Dialysis at Ambulance Transport to the Emergency Department Using Machine Learning Methods.
- Author
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MAJOUNI, Sheida, TENNANKORE, Karthik, and ABIDI, Syed Sibte Raza
- Subjects
HOSPITALS ,SUPPORT vector machines ,HOSPITAL emergency services ,AMBULANCES ,MACHINE learning ,TRANSPORTATION of patients ,EMERGENCY medical technicians ,CONFERENCES & conventions ,COMPARATIVE studies ,PEARSON correlation (Statistics) ,HEMODIALYSIS ,PREDICTION models ,SENSITIVITY & specificity (Statistics) ,RECEIVER operating characteristic curves ,LOGISTIC regression analysis ,ARTIFICIAL neural networks ,LONGITUDINAL method - Abstract
Hemodialysis patients frequently require ambulance transport to the hospital for dialysis. Some patients require urgent dialysis (UD) within 24 hours of transport to hospital to avoid morbidity and mortality. UD is not available in all hospitals; therefore, predicting patients who need UD prior to hospital transport can help paramedics with destination planning. In this paper, we developed machine learning models for paramedics to predict whether a patient needs UD based on patient characteristics available at the time of ambulance transport. This paper presented a study based on ambulance data collected in Halifax, Canada. Given that relatively few patients need UD, a class imbalance problem is addressed by up-sampling methods and prediction models are developed using multiple machine learning methods. The achieved prediction scores are F1-score=0.76, sensitivity=0.76, and specificity=0.97, confirming that models can predict UD with limited patient characteristics. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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