Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsis

TL;DR

This paper introduces a multimodal Transformer model that combines physiological time series data and clinical notes to predict sepsis early in ICU patients, demonstrating superior performance on large datasets.

Contribution

The study presents a novel multimodal Transformer approach for early sepsis prediction using combined data modalities within the first 36 hours of ICU admission.

Findings

Outperforms six baseline models on all metrics

Effective use of multimodal data improves prediction accuracy

Ablation analysis highlights the importance of each data modality

Abstract

Sepsis is a leading cause of death in the Intensive Care Units (ICU). Early detection of sepsis is critical for patient survival. In this paper, we propose a multimodal Transformer model for early sepsis prediction, using the physiological time series data and clinical notes for each patient within $36$ hours of ICU admission. Specifically, we aim to predict sepsis using only the first 12, 18, 24, 30 and 36 hours of laboratory measurements, vital signs, patient demographics, and clinical notes. We evaluate our model on two large critical care datasets: MIMIC-III and eICU-CRD. The proposed method is compared with six baselines. In addition, ablation analysis and case studies are conducted to study the influence of each individual component of the model and the contribution of each data modality for early sepsis prediction. Experimental results demonstrate the effectiveness of our method, which outperforms competitive baselines on all metrics.