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

03/28/2022
by   Yuqing Wang, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/24/2020

Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction

Intensive Care Unit Electronic Health Records (ICU EHRs) store multimoda...
research
02/25/2019

Forecasting intracranial hypertension using multi-scale waveform metrics

Objective: Intracranial hypertension is an important risk factor of seco...
research
07/23/2021

Improving Early Sepsis Prediction with Multi Modal Learning

Sepsis is a life-threatening disease with high morbidity, mortality and ...
research
02/15/2019

Critical Transitions in Intensive Care Units: A Sepsis Case Study

Progression of complex human diseases is associated with transitions acr...
research
08/19/2017

An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection

Sepsis is a poorly understood and potentially life-threatening complicat...
research
01/26/2023

Improving Text-based Early Prediction by Distillation from Privileged Time-Series Text

Modeling text-based time-series to make prediction about a future event ...
research
01/14/2019

A Self-Correcting Deep Learning Approach to Predict Acute Conditions in Critical Care

In critical care, intensivists are required to continuously monitor high...

Please sign up or login with your details

Forgot password? Click here to reset