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

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 p

SUBMITTER: Sadasivuni S 

PROVIDER: S-EPMC8983688 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature