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Sepsis is a life-threatening disease with high morbidity, mortality andhealthcare costs. The early prediction and administration of antibiotics andintravenous fluids is considered crucial for the treatment of sepsis and cansave potentially millions of lives and billions in health care costs.Professional clinical care practitioners have proposed clinical criterion whichaid in early detection of sepsis; however, performance of these criterion isoften limited. Clinical text provides essential information to estimate theseverity of the sepsis in addition to structured clinical data. In this study,we explore how clinical text can complement structured data towards earlysepsis prediction task. In this paper, we propose multi modal model whichincorporates both structured data in the form of patient measurements as wellas textual notes on the patient. We employ state-of-the-art NLP models such asBERT and a highly specialized NLP model in Amazon Comprehend Medical torepresent the text. On the MIMIC-III dataset containing records of ICUadmissions, we show that by using these notes, one achieves an improvement of6.07 points in a standard utility score for Sepsis prediction and 2.89% inAUROC score. Our methods significantly outperforms a clinical criteriasuggested by experts, qSOFA, as well as the winning model of the PhysioNetComputing in Cardiology Challenge for predicting Sepsis.

Improving Early Sepsis Prediction with Multi Modal Learning