Citations 2
AI-Enabled Fusion of Electrocardiograph and Demographics for Prediction of Acute Kidney Injury Onset
Damodaran, Vasundhara , Sanchez, Jose , Gupta, Tushar , Bikkina, Phaneendra , Mikkola, Esko, Haidar, Abdul-Muhsin, Mikkola, Esko, Banerjee, Imon , Sanyal, Arindam
2024 IEEE Biomedical Circuits and Systems Conference (BioCAS) | December 23, 2024 | Volume: No volume available | Issue: No issue available | Pages: 1-5
AI-Enabled Fusion of Electrocardiograph and Demographics for Prediction of Acute Kidney Injury Onset
Damodaran, Vasundhara , Sanchez, Jose , Gupta, Tushar , Bikkina, Phaneendra , Mikkola, Esko, Haidar, Abdul-Muhsin, Sanyal, Arindam , Mikkola, Esko, Banerjee, Imon
No container title available | June 14, 2024 | Volume: No volume available | Issue: No issue available | Pages: No page available
10.36227/techrxiv.171838014.44843115/v1
Acute kidney injury (AKI) is a commonly encountered medical problem that is associated with poor health outcomes in AKI survivors, including increased mortality and re-admission to the hospital. Despite their high-risk status, only a small fraction (< 10%) of patients receive specialized nephrologist follow-up after AKI event. To address the gap in care for AKI patients, this work proposes an artificial intelligence (AI) based fusion technique that combines patient's single-lead electrocardiograph (ECG) and demographics to predict AKI recurrence 3-7 days before onset. The ECG data is analyzed with an on-chip reservoir-computer (RC) prototyped in 28nm CMOS process to create a compressed representation for predicting AKI onset from ECG. After fusion with demographics, the proposed technique is able to predict AKI recurrence 3-7 days before onset with 75.8% accuracy when evaluated on a retrospective patient dataset collected from Mayo Clinic Enterprise.