Fusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset

• Type of Material: Article
• Authors: Sudarsan Sadasivuni, University at Buffalo; Monjoy Saha, Emory University; Neal Bhatia, Emory University; Imon Banerjee, Emory University; Arindam Sanyal, University at Buffalo
• Language: English
• Date: 2022-04-05
• Publisher: NATURE PORTFOLIO
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2022
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41598-022-09712-w
• Title of Journal or Parent Work: SCIENTIFIC REPORTS
• Volume: 12
• Issue: 1
• Start Page: 5711
• End Page: 5711
• Grant/Funding Information: This material is based on research sponsored by Air Force Research Laboratory under agreement number FA8650-18-2-5402.
• Keywords: Science & Technology - Other Topics; Multidisciplinary Sciences; PROCESSOR; CMOS; Science & Technology
• Research Categories: Engineering, Electronics and Electrical; Health Sciences, Radiology

Abstract

The objective of this work is to develop a fusion artificial intelligence (AI) model that combines patient electronic medical record (EMR) and physiological sensor data to accurately predict early risk of sepsis. 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 sepsis prediction with high energy efficiency for integration with resource constrained wearable device. 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 93% in predicting sepsis 4 h 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. Such simple configuration and high accuracy makes our solution favorable for real-time, at-home use for self-monitoring.