Enhancing Target Trial Emulation with Natural Language Processing: A Case Study of Corticosteroids for Sepsis Patients in Critical Care
Presentation Time: 03:30 PM - 03:45 PM
Abstract Keywords: Bioinformatics, Critical Care, Natural Language Processing
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
Target trial emulation using large-scale real-world patient data (RWD) has attracted more attention from researchers to assess the effectiveness of drug treatments. Controlling confounding factors is important for accurately estimating treatment effects from RWD. Here, we propose a framework that leverages Natural Language Processing techniques to analyze clinical notes and integrates this information with structured data to enhance target trial emulation. Experiment results show the efficiency of our framework regarding balancing confounding variables.
Speaker(s):
Suraj Rajendran, PhD
Weill Cornell Medicine
Presentation Time: 03:30 PM - 03:45 PM
Abstract Keywords: Bioinformatics, Critical Care, Natural Language Processing
Primary Track: Applications
Programmatic Theme: Academic Informatics / LIEAF
Target trial emulation using large-scale real-world patient data (RWD) has attracted more attention from researchers to assess the effectiveness of drug treatments. Controlling confounding factors is important for accurately estimating treatment effects from RWD. Here, we propose a framework that leverages Natural Language Processing techniques to analyze clinical notes and integrates this information with structured data to enhance target trial emulation. Experiment results show the efficiency of our framework regarding balancing confounding variables.
Speaker(s):
Suraj Rajendran, PhD
Weill Cornell Medicine
Enhancing Target Trial Emulation with Natural Language Processing: A Case Study of Corticosteroids for Sepsis Patients in Critical Care
Category
Podium Abstract