Physician documentation matters. Using natural language processing to predict mortality in sepsis

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.