Predicting Urgent Dialysis at Ambulance Transport to the Emergency Department Using Machine Learning Methods

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.

Applying Machine Learning to Arsenic Species and Metallomics Profiles of Toenails to Evaluate Associations of Environmental Arsenic with Incident Cancer Cases

Chronic exposure to environmental arsenic has been linked to a number of human diseases affecting multiple organ systems, including cancer. The greatest concern for chronic exposure to arsenic is contaminated groundwater used for drinking as it is the main contributor to the amount of arsenic present in the body. An estimated 40% of households in Nova Scotia (Canada) use water from private wells, and there is a concern that exposure to arsenic may be linked to/associated with cancer. In this preliminary study, we are aiming to gain insights into the association of environmental metal's pathogenicity and carcinogenicity with prostate cancer. We use toenails as a novel biomarker for capturing long-term exposure to arsenic, and have performed toxicological analysis to generate data about differential profiles of arsenic species and the metallome (entirety of metals) for both healthy and individuals with a history cancer. We have applied feature selection and machine learning algorithms to arsenic species and metallomics profiles of toenails to investigate the complex association between environmental arsenic (as a carcinogen) and prostate cancer. We present machine learning based models to ultimately predict the association of environmental arsenic exposure in cancer cases.