TY - GEN
T1 - A 43.6 TOPS/W AI Classifier with Sensor Fusion for Sepsis Onset Prediction
AU - Sadasivuni, Sudarsan
AU - Bhanushali, Sumukh Prashant
AU - Banerjee, Imon
AU - Sanyal, Arindam
N1 - Funding Information:
ACKNOWLEDGMENT This work is supported in part by National Science Foundation grant CCF-1948331 and Air Force Research Laboratory under agreement number FA8650-18-2-5402.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This work presents an artificial intelligence (AI) framework for real-time, personalized sepsis prediction four hours before onset through fusion of electrocardiogram (ECG) and patient electronic medical record. An on-chip classifier combines analog reservoir-computer and artificial neural network to perform in-sensor classification at 43.6 TOPS/W (normalized efficiency of 528 TOPS/W) which reduces energy by 155× compared to conventional sensors and 4× compared to state-of-the-art bio-medical AI circuits. The proposed AI framework predicts sepsis onset with state-of-the-art 92.9% accuracy on patient data from MIMIC-III. The proposed framework is noninvasive and does not require lab tests which makes it suitable for at-home monitoring.
AB - This work presents an artificial intelligence (AI) framework for real-time, personalized sepsis prediction four hours before onset through fusion of electrocardiogram (ECG) and patient electronic medical record. An on-chip classifier combines analog reservoir-computer and artificial neural network to perform in-sensor classification at 43.6 TOPS/W (normalized efficiency of 528 TOPS/W) which reduces energy by 155× compared to conventional sensors and 4× compared to state-of-the-art bio-medical AI circuits. The proposed AI framework predicts sepsis onset with state-of-the-art 92.9% accuracy on patient data from MIMIC-III. The proposed framework is noninvasive and does not require lab tests which makes it suitable for at-home monitoring.
KW - artificial intelligence
KW - artificial neural network
KW - data fusion
KW - in-memory computing
KW - reservoir-computer
KW - sepsis
UR - http://www.scopus.com/inward/record.url?scp=85142924049&partnerID=8YFLogxK
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U2 - 10.1109/BioCAS54905.2022.9948610
DO - 10.1109/BioCAS54905.2022.9948610
M3 - Conference contribution
AN - SCOPUS:85142924049
T3 - BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings
SP - 569
EP - 572
BT - BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022
Y2 - 13 October 2022 through 15 October 2022
ER -