UC Irvine

BACKGROUND/OBJECTIVE: Sepsis remains without good outcome prediction. Technological advances, specifically, natural language processing (NLP), has an opportunity to approach sepsis mortality prediction in a novel way. METHODS: Using the MIMIC III dataset, patients diagnosed with sepsis from 2008 to 2013 had physician progress notes analyzed using NLP. Researchers utilized concepts from analysis to build a model to predict for in-hospital-mortality, using notes in the first 24 hours of a patient admission. This model was retrospectively validated on septic admissions to University of California Irvine Medical Center (UCIMC) from 2013 to 2018 and compared to SOFA and qSOFA. RESULTS: An 80-concept model was developed and validated on 7117 admissions to UCIMC. For severe sepsis, an Area Under Curve or AUC of 0.687 (95% CI 0.618-0.748) was demonstrated which was greater than SOFA at 0.571 (0.497-0.643). Additionally, for simple sepsis the model demonstrated an AUC of 0.696 (0.649-0.738) which was greater than qSOFA at 0.590 (0.545-0.638). CONCLUSIONS: Physician clinical judgement extracted from notes using NLP has greater performance in predicting mortality and survival in sepsis compared to structured data used in SOFA and qSOFA.