Improving Early Sepsis Prediction with Multi Modal Learning

TL;DR

This paper demonstrates that combining structured clinical data with medical text using advanced NLP models significantly improves early sepsis prediction accuracy, outperforming existing clinical criteria and challenge-winning models.

Contribution

The study introduces a multi-modal model integrating structured data and clinical notes with state-of-the-art NLP techniques for enhanced sepsis prediction.

Findings

6.07 point improvement in utility score

2.89% increase in AUROC score

Outperforms qSOFA and challenge-winning models

Abstract

Sepsis is a life-threatening disease with high morbidity, mortality and healthcare costs. The early prediction and administration of antibiotics and intravenous fluids is considered crucial for the treatment of sepsis and can save potentially millions of lives and billions in health care costs. Professional clinical care practitioners have proposed clinical criterion which aid in early detection of sepsis; however, performance of these criterion is often limited. Clinical text provides essential information to estimate the severity of the sepsis in addition to structured clinical data. In this study, we explore how clinical text can complement structured data towards early sepsis prediction task. In this paper, we propose multi modal model which incorporates both structured data in the form of patient measurements as well as textual notes on the patient. We employ state-of-the-art NLP models such as BERT and a highly specialized NLP model in Amazon Comprehend Medical to represent the text. On the MIMIC-III dataset containing records of ICU admissions, we show that by using these notes, one achieves an improvement of 6.07 points in a standard utility score for Sepsis prediction and 2.89% in AUROC score. Our methods significantly outperforms a clinical criteria suggested by experts, qSOFA, as well as the winning model of the PhysioNet Computing in Cardiology Challenge for predicting Sepsis.