The increasing death rate over the past eight years due to stroke has prompted clinicians to
look for data-driven decision aids. Recently, deep-learning-based prediction models trained …

Background: Patients with sepsis have a high mortality rate. Rapid and effective risk
stratification indicators for sepsis-related death are urgently needed to explored. Blood urea …

We study the problem of off-policy policy evaluation (OPPE) in RL. In contrast to prior work,
we consider how to estimate both the individual policy value and average policy value …

Objective Clinical research data warehouses (RDWs) linked to genomic pipelines and open
data archives are being created to support innovative, complex data-driven discoveries. The …

Reinforcement Learning (RL) has recently been applied to sequential estimation and
prediction problems identifying and developing hypothetical treatment strategies for septic …

Background The optimal thresholds for the initiation of invasive ventilation in patients with
hypoxemic respiratory failure are unknown. Using the saturation-to-inspired oxygen ratio …

Patient-specific mortality prediction models are an essential component of Clinical Decision
Support Systems developed for caregivers in Intensive Care Units (ICUs), that enable timely …

Cuffless continuous blood pressure (BP) monitoring is of vital importance for personal health
management. Currently, there are extensive studies devoted to cuffless BP prediction based …

We aimed to investigate the association between the plasma anion gap (AG) and in-hospital
mortality among patients with acute ischemic stroke (AIS). In total, 1236 AIS patients were …

Since its introduction in 2006, differential privacy has emerged as a predominant statistical
tool for quantifying data privacy in academic works. Yet despite the plethora of research and …