Real-time sepsis prediction using fusion of on-chip analog classifier and electronic medical record

Abstract

This work presents a fusion artificial intelligence (AI) framework that combines patient electronic medical record (EMR) and physiological sensor data to accurately predict early risk of sepsis 4 hours before onset. The fusion AI model has two components - an on-chip AI model that continuously analyzes patient electrocardiogram (ECG) data and a cloud AI model that combines EMR and prediction scores from on-chip AI model to predict fusion sepsis onset score. The on-chip AI model is designed using analog circuits for high energy efficiency that allows integration with resource constrained wearable device. The on-chip AI reduces by 4.5× compared to digital baseline, and by 4× compared to state-of-the-art bio-medical AI ICs. Combination of EMR and sensor physiological data improves prediction performance compared to EMR or physiological data alone, and the late fusion model has an accuracy of 92.2% in predicting sepsis 4 hours before onset. The key differentiation of this work over existing sepsis prediction literature is the use of single modality patient vital (ECG) and simple demographic information, instead of comprehensive laboratory test results and multiple vital signs.