AIMS: To develop machine-learning algorithms for predicting the risk of a hospitalization or emergency department (ED) visit for opioid use disorder (OUD) (i.e. OUD acute events) in Pennsylvania Medicaid enrollees in the Opioid Use Disorder Centers o...
PURPOSE: To use machine learning to predict new-onset shock for at-risk intensive care unit (ICU) patients based on discrete vital sign data from the electronic health record.
BACKGROUND: Delirium is common in hospitalized patients and is correlated with increased morbidity and mortality. Despite this, delirium is underdiagnosed, and many institutions do not have sufficient resources to consistently apply effective screeni...
OBJECTIVE: To develop a machine learning (ML)-based admission screening model for hospital-acquired (HA) influenza using routinely available data to support early clinical intervention.
BACKGROUND: Several studies explored factors related to adverse clinical outcomes among COVID-19 patients but lacked analysis of the impact of the temporal data shifts on the strength of association between different predictors and adverse outcomes. ...
BACKGROUND: Remdesivir (RDV) was the first antiviral approved for mild-to-moderate COVID-19 and for those patients at risk for progression to severe disease after clinical trials supported its association with improved outcomes. Real-world evidence (...
BACKGROUND: Serious pulmonary pathologies of infectious, viral, or bacterial origin are accompanied by inflammation and an increase in oxidative stress (OS). In these situations, biological measurements of OS are technically difficult to obtain, and ...
BACKGROUND: Dependent older people or those losing their autonomy are at risk of emergency hospitalization. Digital systems that monitor health remotely could be useful in reducing these visits by detecting worsening health conditions earlier. Howeve...
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