Research Output per year
Fingerprint Fingerprint is based on mining the text of the experts' scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.
Electrocardiography
Engineering & Materials Science
Pattern recognition
Engineering & Materials Science
Atrial Fibrillation
Medicine & Life Sciences
Neural networks
Engineering & Materials Science
Radial Basis Function Neural Network
Mathematics
Lead
Engineering & Materials Science
Least Squares
Mathematics
Factual Databases
Medicine & Life Sciences
Network
Recent external collaboration on country level. Dive into details by clicking on the dots.
Research Output 2006 2019
2
Citations
Physonline: An open source machine learning pipeline for real-time analysis of streaming physiological waveform
Sutton, J. R., Mahajan, R., Akbilgic, O. & Kamaleswaran, R., Jan 1 2019, In : IEEE Journal of Biomedical and Health Informatics. 23, 1, p. 59-65 7 p., 8353460.Research output: Contribution to journal › Article
Learning systems
Pipelines
Feature extraction
Pattern matching
Electrocardiography
Vancomycin-Associated Acute Kidney Injury in a Large Veteran Population
Gyamlani, G., Potukuchi, P. K., Thomas, F., Akbilgic, O., Soohoo, M., Streja, E., Naseer, A., Sumida, K., Molnar, M. Z., Kalantar-Zadeh, K. & Kovesdy, C., Jan 1 2019, (Accepted/In press) In : American Journal of Nephrology. p. 133-142 10 p.Research output: Contribution to journal › Article
Veterans
Vancomycin
Acute Kidney Injury
Population
Linezolid
A hybrid feature extraction method to detect Atrial Fibrillation from single lead ECG recording
Mahajan, R., Kamaleswaran, R. & Akbilgic, O., Apr 6 2018, 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018. Institute of Electrical and Electronics Engineers Inc., Vol. 2018-January. p. 116-119 4 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Electrocardiography
Atrial Fibrillation
Feature extraction
Entropy
Lead
A novel risk classification system for 30-day mortality in children undergoing surgery
Akbilgic, O., Langham, M., Walter, A. I., Jones, T., Huang, E. & Davis, R., Jan 1 2018, In : PloS one. 13, 1, e0191176.Research output: Contribution to journal › Article
Factual Databases
infant mortality
Child Mortality
postoperative complications
preschool children
3
Citations
Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the PICU
Kamaleswaran, R., Akbilgic, O., Hallman, M. A., West, A., Davis, R. & Shah, S., Oct 1 2018, In : Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies. 19, 10, p. e495-e503Research output: Contribution to journal › Article
Artificial Intelligence
Sepsis
Sensitivity and Specificity
Critical Illness
Logistic Models