6 citations · 10 across the 4 of their papers we have counts for
4 papers
Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis
Tom Velez, Tony Wang, Ioannis Koutroulis +5
Background: While machine learning (ML) models are rapidly emerging as promising screening tools in critical care medicine, the identification of homogeneous subphenotypes within p…
Using Latent Class Analysis to Identify ARDS Sub-phenotypes for Enhanced Machine Learning Predictive Performance
Tony Wang, Tim Tschampel, Emilia Apostolova +1
In this work, we utilize Machine Learning for early recognition of patients at high risk of acute respiratory distress syndrome (ARDS), which is critical for successful prevention…
Toward Automated Early Sepsis Alerting: Identifying Infection Patients from Nursing Notes
Emilia Apostolova, Tom Velez
Severe sepsis and septic shock are conditions that affect millions of patients and have close to 50% mortality rate. Early identification of at-risk patients significantly improves…
Semantically Enhanced Dynamic Bayesian Network for Detecting Sepsis Mortality Risk in ICU Patients with Infection
Tony Wang, Tom Velez, Emilia Apostolova +3
Although timely sepsis diagnosis and prompt interventions in Intensive Care Unit (ICU) patients are associated with reduced mortality, early clinical recognition is frequently impe…