Beyond the Bedside: Leveraging ICD Data and Machine Learning for Enhanced Febrile Neutropenia Risk Stratification

Presentation Time: 05:30 PM - 07:00 PM

Abstract Keywords: Machine Learning, Data Mining, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Clinical Informatics

This study refines risk stratification in febrile neutropenia by comparing a simplified MASCC score with a refined MASCC score that incorporates ICD‐derived flags for hypotension, sepsis, respiratory failure, COPD, dehydration, and cancer. In parallel, machine learning (ML) models were built using basic demographics and then extended with ICD‐features. The refined MASCC score improved discrimination (AUC 0.775 vs. 0.651) while ML models with ICD features achieved an AUC of ~0.87, suggesting enhanced detection of high‐risk patients.

Speaker(s):
Benedict Amalraj, MD
Louisiana State University Shreveport

Author(s):
Benedict Amalraj, MD - Louisiana State University Shreveport; Mariana Marrero Castillo, MD - Louisiana State University Shreveport;