1. A. Shaban-Nejad, R. Kamaleswaran, E. Kyong Shin, and O. Akbilgic, “Chapter 6: Health Intelligence,” in Biomedical Information Technology 2e, 2019. (In print)
  2. R. Mahajan, R. Kamaleswaran and O. Akbilgic, “Effects of varying sampling frequency on the analysis of continuous ECG data streams” in Data Management and Analytics for Medicine and Healthcare, Springer 2018


  1. Kamaleswaran, R., Akbilgic, O., Hallman, M.A., West, A.N., Davis, R.L. and Shah, S.H., 2019. Artificial Intelligence: Progress Towards an Intelligent Clinical Support System. Pediatric Critical Care Medicine, 20(4), p.399.


  1. A.W. McMahon, C.O. William, J.S. Brown, B. Carleton, F. Doshi-Velez, I. Kohane, J.L. Goldman, M.A. Hoffman, R. Kamaleswaran, A.A. Mitchell, M. Sakiyama, S. Sekine, M.C.J.M Sturkenboom, M.A. Turner, R.M. Califf, “Big Data in the Assessment of Pediatric Drug Safety” Pediatrics, 2019
  2. A. Mohammed, Y. Cui, V. R. Mas, and R. Kamaleswaran, “Differential gene expression analysis reveals novel genes and pathways in pediatric septic shock patients,” Scientific Reports (Nature Publisher Group) 9 (2019): 1-7.
  3. F. van Wyk, A. Khojandi, and R. Kamaleswaran, “Improving Prediction Performance Using Hierarchical Analysis of Real-Time Data: A Sepsis Case Study” IEEE Journal of Biomedical and Health Informatics, 23(3), pp.978-986.
  4. F. Van Wyk, A. Khojandi, A. Mohammed, E. Begoli, R.L. Davis, and R. Kamaleswaran, “A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier” International Journal of Medical Informatics, 2019
  5. J. R. Sutton, R. Mahajan, O. Akbilgic, and R. Kamaleswaran, “PhysOnline: An Open Source Machine Learning Pipeline for Real-Time Analysis of Streaming Physiological Waveform,” IEEE J. Biomed. Heal. Informatics, vol. 23, no. 1, pp. 59–65, 2019.
  6. F. Van Wyk, A. Khojandi, B. Williams, D. MacMillan, R.L. Davis, D. Jacobson, R. Kamaleswaran, “A Cost-Benefit Analysis of Automated Physiological Data Acquisition Systems Using Data-Driven Modeling” Journal of Healthcare Informatics Research, pp.1-19. 2018
  7. R. Kamaleswaran, O. Akbilgic, M.A. Hallman, A.N. West, R.L. Davis, S.H. Shah. “Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the Pediatric Intensive Care Unit.” Pediatr Crit Care Med. 2018 19(10), e495–e503. doi:10.1097/PCC.0000000000001666
  8. R. Kamaleswaran, R. Mahajan and O. Akbilgic, “A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using varying length single lead electrocardiogram.” Physiol Meas. January 2018.
  9. R. Kamaleswaran and C. McGregor, “A Review of Visual Representations of Physiologic Data.,” JMIR Med. informatics, vol. 4, no. 4, p. e31, Nov. 2016.
  10. R. Kamaleswaran, C. Collins, A. James, and C. McGregor, “PhysioEx: Visual Analysis of Physiological Event Streams,” Comput. Graph. Forum, vol. 35, no. 3, pp. 331–340, Jun. 2016.
  11. R. Kamaleswaran, R. Wehbe, J. E. Pugh, L. Nacke, C. Mcgregor, and A. James, “Collaborative Multi-Touch Clinical Handover System for the Neonatal Intensive Care Unit,” Electron. J. Heal. Informatics, 2015.
  12. J. Sritharan, R. Kamaleswaran, K. McFarlan, M. Lemonde, C. George, and O. Sanchez, “Environmental Factors in an Ontario Community with Disparities in Colorectal Cancer Incidence,” Glob. J. Health Sci., vol. 6, no. 3, p. p175, 2014.


  1. S. Srinivasan, E. Begoli, G. Peterson, M. Muthiah, and R. Kamaleswaran, Markers in Unstructured Progress Notes Predict Imminent ICU Admission Using Machine Learning, SCCM 2020
  2. R. Swaminathan, A. Singh, A. Mohammed, and R. Kamaleswaran, Machine Learning Predicts Early Onset of Sepsis from Continuous Physiological Data of Critically Ill Adults. IEEE-NIH HI-POCT 2019
  3. K. Silvas, J. O'Neill, B. Monk, R. Schorr, R. Kamaleswaran 114 Emergency Department Factors Associated With Early Rapid Responses Activation After Admission. Annals of Emergency Medicine. 2019 Oct 1;74(4):S46.
  4. R. Kamaleswaran, C. Koo, R. Helmick, V. Mas, J. Eason, D. Maluf. Predicting Early Post-Operative Sepsis in Liver Transplantation Applying Artificial Intelligence. ILTS 2019, 25th Annual International Congress (Plenary Abstract Presentation)
  5. L.K. Chinthala, A. Mohammed, and R. Kamaleswaran. ICUWaveDB: A Big Data Approach to Capture and Processing Real-Time Streams in Critical Care. AMIA 2019 Clinical Informatics Conference. Atlanta, GA.
  6. R. Kamaleswaran, R. Mahajan, O. Akbilgic, N.I. Shafi, R.L. Davis. 46: Machine Learning Applied To Continuous Physiologic Data Predicts Fever In Critically Ill Children. Critical Care Medicine 47, no. 1 (2019): 23. Star Research Achievement Award (Top 64 of 1831 Abstracts)
  7. R. Mahajan, R. Kamaleswaran, O. Akbilgic, “A hybrid feature extraction method to detect Atrial Fibrillation from single lead ECG recording”. IEEE BHI 2018 doi:10.1109/BHI.2018.8333383
  8. F. Van Wyk, A. Khojandi, R. Kamaleswaran, O. Akbilgic, S. Nemati, R.L. Davis, “How Much Data Should We Collect? A Case Study in Sepsis Detection Using Deep Learning” IEEE-NIH HI-POCT 2017
  9. R. Kamaleswaran, O. Akbilgic, M. Hallman, A. West, R.L. Davis, S. Shah, “Physiomarker Variability For Early Prediction of Severe Sepsis in the Pediatric Intensive Care Unit” SCCM 2018 Critical Care Congress, San Antonio, TX
  10. R. Mahajan, R. Kamaleswaran and O. Akbilgic, “Cardiac Rhythm Classification from a Short Single Lead ECG Recording via Random Forests” Computing in Cardiology 2017 Rennes, France.
  11. R. Mahajan, R. Kamaleswaran and O. Akbilgic, “Effects of varying sampling frequency on the analysis of continuous ECG data streams” in the Third International Workshop on Data Management and Analytics for Medicine and Healthcare, September 2017.
  12. R. Mahajan, R. Kamaleswaran and O. Akbilgic, “Paroxysmal Atrial Fibrillation Screening at Different ECG Sampling Frequencies via Symbolic Pattern Recognition”, in Proc. Of IEEE Biomedical and Health Informatics (BHI), 2017
  13. R. Kamaleswaran, C. Collins, A. James, and C. McGregor, “CoRAD: Visual Analytics for Cohort Analysis,” in IEEE International Conference on Healthcare Informatics 2016 (ICHI 2016), 2016, pp. 517–526. doi: 10.1109/ICHI.2016.93
  14. R. Kamaleswaran, J. E. Pugh, A. Thommandram, A. James, and C. Mcgregor, “Visualizing Neonatal Spells: Temporal Visualization of High Frequency Cardiorespiratory Physiological Event Streams,” in Proc. of IEEE VIS 2014 Workshop on Visualization of Electronic Health Records, 2014.
  15. R. Kamaleswaran and C. McGregor, “A Real-Time Multi-dimensional Visualization Framework for Critical and Complex Environments,” in Computer-Based Medical Systems (CBMS), 2014 IEEE 27th International Symposium on, 2014, pp. 325–328.
  16. J. Sritharan, R. Kamaleswaran, K. McFarlan, M. Lemonde, C. George, and O. Sanchez, “Assessing the environmental factors in two Ontario communities with diverging colorectal cancer incidence rates.,” Cancer Res., vol. 73, no. 8 Supplement, p. 4819 LP-4819, Nov. 2014.
  17. R. Kamaleswaran, A. Thommandram, Q. Zhou, M. Eklund, Y. Cao, W. P. Wang, and C. McGregor, “Cloud framework for real-time synchronous physiological streams to support rural and remote Critical Care,” in Computer-Based Medical Systems (CBMS), 2013 IEEE 26th International Symposium on, 2013, pp. 473–476.
  18. C. Tomlinson, M. Rafii, R. Kamaleswaran, R. Ball, P. Pencharz. (2012). Fractional synthesis rate of creatine from arginine in healthy adult men. 2012 Research Meeting, FASEB J.
  19. R. Kamaleswaran, C. McGregor. (2012). CBPSP: Complex Business Processes For Stream Processing. 25th Canadian Conference on Electrical and Computer Engineering (CCECE)
  20. R. Kamaleswaran, C. McGregor, A. James. (2012). A novel framework for event stream processing of clinical practice guidelines. Biomedical and Health Informatics (BHI), 2012 IEEE-EMBS International Conference on
  21. R. Kamaleswaran, M. Eklund. (2011). A method for interactive hypothesis testing for clinical decision support systems using Ptolemy II. Electrical and Computer Engineering (CCECE), 2011 24th Canadian Conference
  22. J. Percival, C. McGregor, N. Percival, R. Kamaleswaran, S. Tuuha. (2010). A framework for nursing documentation enabling integration with HER and real-time patient monitoring. Computer-Based Medical Systems (CBMS), 2010 IEEE 23rd International Symposium on
  23. R. Kamaleswaran, C. McGregor, J. M. Eklund, J. Mikael, R. Kamaleswaran, C. McGregor, and J. M. Eklund, “A method for clinical and physiological event stream processing,” in Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, 2010, vol. 1, pp. 1170–1173.
  24. M. Blount, C. McGregor, A. James, D. Sow, R. Kamaleswaran, S. Tuuha, J. Percival, N. Percival. (2010). On the integration of an artifact system and a real-time healthcare analytics system. 1st ACM International Health Informatics Symposium
  25. R. Kamaleswaran, C. McGregor, J. Percival. (2009). Service oriented architecture for the integration of clinical and physiological data for real-time event stream processing. Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE


  1. R. Kamaleswaran “Early prediction of sepsis in children and adults using machine learning” Internal Medicine Conference, Allegheny General Hospital, Pittsburgh, PA, April 2019
  2. R. Kamaleswaran “Artificial Intelligence Applied to Continuous Physiological Data Streams for Prediction of Clinical Deterioration” Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, March 2019
  3. R. Kamaleswaran “Sepsis Prediction using High-Frequency Data Streams: A path towards clinical implementation” West Penn Hospital Academic Meeting, Pittsburgh, PA, February 2019
  4. R. Kamaleswaran “Artificial Intelligence in Critical Care: Progress towards a Real-time Intelligent Learning System” Centre hospitalier universitaire Sainte-Justine, Montreal, QC, Canada, January 2019
  5. R. Kamaleswaran O. Akbilgic and R.L. Davis “Integrating EMR and biomarkers with continuous high frequency data streams” Transplant Research Institute, Memphis TN, December 2018
  6. R. Kamaleswaran and S. H. Shah “Applying Artificial Intelligence in the PICU” Center for Health Systems Improvement, Memphis TN, September 2018
  7. R. Kamaleswaran, “Machine learning in Medicine”, Hu Lab, University of California San Francisco, San Francisco, CA. August 2018.
  8. R. Kamaleswaran, “Predictive Analytics of Abnormal Events in the Critical Care Unit using Physiological Data Streams”, ADEPT4 Workshop, Office of Pediatric Therapeutics (OPT), FDA September 2017
  9. R. Kamaleswaran, “Big Data in Critical Care”, Statistical Research Group, University of Tennessee Health Science Center, July 2017
  10. R. Kamaleswaran, “Moving towards Predictive Analytics in Real Time in Intensive Care Units”, Indian Institute of Information Technology (IIIT Bangalore), June 2017, Bangalore India
  11. R. Kamaleswaran, “Event Stream Processing of Biosensor Data in the Intensive Care Unit”, Indian Institute of Science (IISc Bangalore), June 2017, Bangalore India
  12. R. Kamaleswaran, “Dynamic Visual Analytics and Online Event Stream Analytics”, UT/KBRIN Bioinformatics Summit April 21-23, 2017 Montgomery Bell State Park, TN.
  13. R. Kamaleswaran, “Precision Medicine: Applying analytics at the bedside through real-time complex event stream processing of heterogeneous sensor networks”, Computer Science Colloquium Series, University of Memphis, March 17, 2017
  14. R. Kamaleswaran, O. Akbilgic “Machine Learning in Healthcare” Center for Biomedical Informatics, University of Tennessee Health Science Center, Memphis, TN January 2017
  15. R. Kamaleswaran, “Data-driven Healthcare” Le Bonheur Research Center, Memphis, TN December 2016
  16. R. Kamaleswaran, “Complex Business Processes for Event Stream Processing”, University of Ontario Institute of Technology, Oshawa, Canada, August 2016
  17. R. Kamaleswaran, “PhysioEx: visual analysis of physiological event streams”, 18th EG/VGTC Conference on Visualization, Groningen, Netherlands, June 2016
  18. R. Kamaleswaran “Visualizing Neonatal Spells: Temporal Visualization of High Frequency Cardiorespiratory Physiological Event Streams” Oshawa, Canada, June 2016


Nature Communications, Nature Scientific Review, Critical Care, Pediatrics, Pediatrics Research, The Journal of Pediatrics, Physiological Measurements, MDPI Bioengineering, IEEE Computer Based Medical Systems, IEEE Engineering in Medicine and Biology, AAAI Health Intelligence, International Journal of Medical Informatics, BMJ Open, Canadian Journal of Physiology and Pharmacology, Computers in Biology and Medicine, Journal of American Informatics Association (JAMIA), IEEE Access