TY - GEN
T1 - Real-time sepsis prediction using fusion of on-chip analog classifier and electronic medical record
AU - Sadasivuni, Sudarsan
AU - Sahay, Monjoy
AU - Bhanushali, Sumukh Prashant
AU - Banerjee, Imon
AU - Sanyal, Arindam
N1 - Funding Information:
VI. ACKNOWLEDGEMENTS This material is based on research sponsored by Air Force Research Laboratory under agreement number FA8650-18-2-5402. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Air Force Research Laboratory or the U.S. Government.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - electrocardiogram
KW - late fusion
KW - machine learning
KW - on-chip analog classifier
KW - Sepsis prediction
UR - http://www.scopus.com/inward/record.url?scp=85142479839&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142479839&partnerID=8YFLogxK
U2 - 10.1109/ISCAS48785.2022.9937902
DO - 10.1109/ISCAS48785.2022.9937902
M3 - Conference contribution
AN - SCOPUS:85142479839
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
SP - 1635
EP - 1639
BT - IEEE International Symposium on Circuits and Systems, ISCAS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022
Y2 - 27 May 2022 through 1 June 2022
ER -