Background: Clinical decision support tools based on predictive analytics can provide actionable information and improve clinical outcomes for patients at risk of developing sepsis. Scoring systems such as Systemic Inflammatory Response Syndrome (SIRS) and National [...]
Background: Sepsis is one of the top causes of inpatient mortality and rapid detection presents numerous challenges. In March, 2016, an interdisciplinary team consisting of top clinicians, data scientists and machine learning experts at a [...]
Background: Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of hospital readmissions in America. Over one-third of patients admitted for COPD exacerbations are readmitted within 90 days, with readmissions costing $15 billion. Medicare [...]
Background: Sepsis is a leading cause of death among hospitalized patients. Early detection of sepsis has the potential to reduce mortality by facilitating timely evidence-based interventions. Past studies have used electronic health records (EHR) to [...]
UTILIZATION OF WEATHER AND CALENDER DATA IN PREDICTING DAILY NUMBER OF HOSPITALIZED PATIENTS-APPLICATION OF CONVENTIONAL REGRESSION ANALYSIS AND MACHINE LEARNING.
Background: The number of hospitalized patients varies significantly throughout the year. Fine prediction of the number of hospitalized patients is essential to provide adequate staffing and quality care. We aimed to identify the factors affecting [...]