Publications

2023

• Sikora A, Rafiei A, Rad MG, Keats K, Smith SE, Devlin JW, Murphy DJ, Murray B, Kamaleswaran R, MRC-ICU Investigator Team. Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model. Critical Care. 2023 May 2;27(1):167. PDF File

• Rafiei A, Rad MG, Sikora A, Kamaleswaran R. Improving mixed-integer temporal modeling by generating synthetic data using conditional generative adversarial networks: A case study of fluid overload prediction in the intensive care unit. Computers in Biology and Medicine. 2023 Nov 22:107749. PDF File 

• Krishnan P, Rad MG, Agarwal P, Marshall C, Yang P, Bhavani SV, Holder AL, Esper A, Kamaleswaran R. HIRA: Heart Rate Interval based Rapid Alert score to characterize autonomic dysfunction among patients with sepsis-related acute respiratory failure (ARF). Physiological Measurement. 2023 Oct 13;44(10):105006.  PDF File

• Wei S, Moore R, Zhang H, Xie Y, Kamaleswaran R. Transfer Causal Learning: Causal Effect Estimation with Knowledge Transfer. arXiv preprint arXiv:2305.09126. PDF File 

• Huang M, Atreya MR, Holder A, Kamaleswaran R. A MACHINE LEARNING MODEL DERIVED FROM ANALYSIS OF TIME-COURSE GENE-EXPRESSION DATASETS REVEALS TEMPORALLY STABLE GENE MARKERS PREDICTIVE OF SEPSIS MORTALITY. Shock. 2023 Nov 1;60(5):671-7. PDF File

• Cottrill KA, Chandler JD, Kobara S, Stephenson ST, Mohammad AF, Tidwell M, Mason C, Van Dresser M, Patrignani J, Kamaleswaran R, Fitzpatrick AM. Metabolomics identifies disturbances in arginine, phenylalanine, and glycine metabolism as differentiating features of exacerbating atopic asthma in children. Journal of Allergy and Clinical Immunology: Global. 2023 Aug 1;2(3):100115.  PDF File 

•  Arora M, Davis CM, Gowda NR, Foster DG, Mondal A, Coopersmith CM, Kamaleswaran R. Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome. Bioengineering. 2023 Aug 8;10(8):946. PDF File

• Shi H, Book W, Raskind‐Hood C, Downing KF, Farr SL, Bell MN, Sameni R, Rodriguez III FH, Kamaleswaran R. A machine learning model for predicting congenital heart defects from administrative data. Birth Defects Research. 2023 Nov 1;115(18):1693-707. PDF File
  
  

2022

• Kobara, S., Rad, M.G., Grunwell, J.R., Coopersmith, C.M., Kamaleswaran, R., 2022. Bioenergetic Crisis in ICU-Acquired Weakness Gene Signatures Was Associated With Sepsis-Related Mortality: A Brief Report. Critical Care Explorations 4(12):p e0818 PDF File

• Jeong, H., Kamaleswaran, R., 2022. Pivotal challenges in artificial intelligence and machine learning applications for neonatal care. Seminars in Fetal and Neonatal Medicine 27, 101393. PDF File

• Jie W., Ronald M., Yao X., Rishikesan K. Improving Sepsis Prediction Model Generalization With Optimal Transport. Proceedings of the 2nd Machine Learning for Health symposium, PMLR 193:474-488, 2022. PDF File

• Arora, M., Zambrzycki, S.C., Levy, J.M., Esper, A., Frediani, J.K., Quave, C.L., Fernández, F.M. and Kamaleswaran, R., 2022. Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS. Metabolites, 12(3), p.232. PDF File

• Bhavani, S.V., Verhoef, P.A., Maier, C.L., Robichaux, C., Parker, W.F., Holder, A., Kamaleswaran, R., Wang, M.D., Churpek, M.M. and Coopersmith, C.M., 2022. Coronavirus Disease 2019 Temperature Trajectories Correlate With Hyperinflammatory and Hypercoagulable Subphenotypes. Critical Care Medicine, 50(2), pp.212-223. PDF File

• Ack, S.E., Loiseau, S.Y., Sharma, G., Goldstein, J.N., Lissak, I.A., Duffy, S.M., Amorim, E., Vespa, P., Moorman, J.R., Hu, X. and Clermont, G. … Kamaleswaran, R., Foreman, B., Rosenthal, E., 2022. Neurocritical Care Performance Measures Derived from Electronic Health Record Data are Feasible and Reveal Site-Specific Variation: A CHoRUS Pilot Project. Neurocritical Care, pp.1-15. PDF File

• Smith, R.N., Nyame-Mireku, A., Zeidan, A., Tabaie, A., Meyer, C., Muralidharan, V., Kamaleswaran, R., Williams, K., Grant, A., Nguyen, J. and Hurst, S., 2022. Intimate partner violence at a level-1 trauma center during the COVID-19 pandemic: an interrupted time series analysis. The American Surgeon, p.00031348221083939. PDF File

• Sanchez-Perez, J.A., Berkebile, J.A., Nevius, B.N., Ozmen, G.C., Nichols, C.J., Ganti, V.G., Mabrouk, S.A., Clifford, G.D., Kamaleswaran, R., Wright, D.W. and Inan, O.T., 2022. A Wearable Multimodal Sensing System for Tracking Changes in Pulmonary Fluid Status, Lung Sounds, and Respiratory Markers. Sensors, 22(3), p.1130. PDF File

• Kandaswamy S, Orenstein E, Quincer EM, Fernandez A, Gonzalez M, Lu L, Kamaleswaran R, Banerjee I, Jaggi P. Automated Identification of Immunocompromised Status in Critically Ill Children. Methods of Information in Medicine. 2022 Apr 5(AAM). PDF File

• Liu Z, Khojandi A, Li X, Mohammed A, Davis RL, Kamaleswaran R. A Machine Learning–Enabled Partially Observable Markov Decision Process Framework for Early Sepsis Prediction. INFORMS Journal on Computing. 2022 Mar 22. PDF File

• Loftus, T.J., Tighe, P.J., Ozrazgat-Baslanti, T., Davis, J.P., Ruppert, M.M., Ren, Y., Shickel, B., Kamaleswaran, R., Hogan, W.R., Moorman, J.R. and Upchurch Jr, G.R., 2022. Ideal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible. PLOS Digital Health, 1(1), p.e0000006. PDF FIle

2021

• Haendel, M.A., Chute, C.G., Bennett, T.D., Eichmann, D.A., Guinney, J., Kibbe, W.A., Payne, P.R., Pfaff, E.R., Robinson, P.N., Saltz, J.H. et al.,  2021. The National COVID Cohort Collaborative (N3C): rationale, design, infrastructure, and deployment. Journal of the American Medical Informatics Association, 28(3), pp.427-443. (Consortium author) PDF File

• S. Banerjee, A. Mohammed, H.R Wong, N. Palaniyar and R. Kamaleswaran (2021) Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 H Post-ICU Admission. Front. Immunol. 12:592303. doi: 10.3389/fimmu.2021.592303 PDF File

• A. Tabaie, E.W. Orenstein, S. Nemati, R.K. Basu, S. Kandaswamy, G.D. Clifford, and R. Kamaleswaran, 2021. Predicting Presumed Serious Infection among Hospitalized Children with Central Venous Lines with Machine Learning. Computers in Biology and Medicine, p.104289. PDF File

• Z. Liu, A. Khojandi, A. Mohammed, X. Li, L. Chinthala, R.L. Davis, and R. Kamaleswaran, 2021. HeMA: A Hierarchically Enriched Machine Learning Approach for Managing False Alarms in Real Time: A Sepsis Prediction Case Study. Computers in Biology and Medicine, p.104255. PDF File

• A.I. Wong, R. Kamaleswaran, A. Tabaie, M. Reyna, C. Josef, C. Robichaux, A. de Hond, EW. Steyerberg, AL. Holder, S. Nemati, TG Buchman, JM Blum. “Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment (PARFAIT): A multivariable prediction model from electronic medical record data”, Critical Care Explorations 2021. PDF File

• Mohammed A, Van Wyk F, Chinthala LK, Khojandi A, Davis RL, Coopersmith CM, Kamaleswaran R. Temporal differential expression of physiomarkers predicts sepsis in critically ill adults. Shock. 2021 Jul 1;56(1):58-64. PDF File

• Bennett TD, Moffitt RA, Hajagos JG, et al. Clinical Characterization and Prediction of Clinical Severity of SARS-CoV-2 Infection Among US Adults Using Data From the US National COVID Cohort Collaborative. JAMA Netw Open. 2021;4(7):e2116901. doi:10.1001/jamanetworkopen.2021.16901 PDF File

• Futoma, J., Simons, M., Doshi-Velez, F. and Kamaleswaran, R., 2021. Generalization in Clinical Prediction Models: The Blessing and Curse of Measurement Indicator Variables. Critical Care Explorations, 3(7). PDF File

• Grunwell, J.R., Rad, M.G., Stephenson, S.T., Mohammad, A.F., Opolka, C., Fitzpatrick, A.M. and Kamaleswaran, R., 2021. Machine Learning–Based Discovery of a Gene Expression Signature in Pediatric Acute Respiratory Distress Syndrome. Critical care explorations, 3(6). PDF File

• Kamaleswaran R, Sataphaty SK, Mas VR, Eason JD and Maluf DG (2021) Artificial Intelligence May Predict Early Sepsis After Liver Transplantation. Front. Physiol. 12:692667. doi: 10.3389/fphys.2021.692667 PDF File

• Tabaie A, Orenstein EW, Nemati S, Basu RK, Clifford GD and Kamaleswaran R (2021) Deep Learning Model to Predict Serious Infection Among Children With Central Venous Lines. Front. Pediatr. 9:726870. doi: 10.3389/fped.2021.726870 PDF File

• Steinberg R, Anderson B, Hu Z, Johnson TM, O'Keefe JB, Plantinga LC, Kamaleswaran R, Anderson B. Associations between remote patient monitoring programme responsiveness and clinical outcomes for patients with COVID-19. BMJ Open Qual. 2021 Sep;10(3):e001496. doi: 10.1136/bmjoq-2021-001496. PMID: 34518302; PMCID: PMC8438571. PDF File

• Wong, A. K. I., Charpignon, M., Kim, H., Josef, C., de Hond, A. A., Fojas, J. J., ... & Celi, L. A. (2021). Analysis of Discrepancies Between Pulse Oximetry and Arterial Oxygen Saturation Measurements by Race and Ethnicity and Association With Organ Dysfunction and Mortality. JAMA network open, 4(11), e2131674-e2131674. PDF File

• Zhou, A., Raheem, B. and Kamaleswaran, R., OnAI-Comp: An Online AI Experts Competing Framework for Early Sepsis Detection. IEEE/ACM transactions on computational biology and bioinformatics. PDF File

• Singhal, L., Garg, Y., Yang, P., Tabaie, A., Wong, A. I., Mohammed, A., ... & Kamaleswaran, R. (2021). eARDS: A multi-center validation of an interpretable machine learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among critically ill adults with COVID-19. PloS one, 16(9), e0257056. PDF File

• Kamaleswaran, R., Sadan, O., Kandiah, P., Li, Q., Coopersmith, C.M. and Buchman, T.G., 2021. Altered Heart Rate Variability Early in ICU Admission Differentiates Critically Ill Coronavirus Disease 2019 and All-Cause Sepsis Patients. Critical Care Explorations, 3(12), p.e0570. PDF File

• Grunwell, J.R., Rad, M.G., Stephenson, S.T., Mohammad, A.F., Opolka, C., Fitzpatrick, A.M. and Kamaleswaran, R., 2021. Cluster analysis and profiling of airway fluid metabolites in pediatric acute hypoxemic respiratory failure. Scientific reports, 11(1), pp.1-11. PDF File

• Wong, A. K. I., Charpignon, M., Kim, H., Josef, C., de Hond, A. A., Fojas, J. J., ... & Celi, L. A. (2021). Analysis of Discrepancies Between Pulse Oximetry and Arterial Oxygen Saturation Measurements by Race and Ethnicity and Association With Organ Dysfunction and Mortality. JAMA network open, 4(11), e2131674-e2131674. PDF File

• Karabayir, I., Butler, L., Goldman, S.M., Kamaleswaran, R., Gunturkun, F., Davis, R.L., Ross, G.W., Petrovitch, H., Masaki, K., Tanner, C.M. and Tsivgoulis, G., 2021. Predicting Parkinson’s Disease and Its Pathology via Simple Clinical Variables. Journal of Parkinson's Disease, pp.1-11. PDF File

• Kamaleswaran, R., Sadan, O., Kandiah, P., Li, Q., Coopersmith, C.M. and Buchman, T.G., 2021. Altered Heart Rate Variability Early in ICU Admission Differentiates Critically Ill Coronavirus Disease 2019 and All-Cause Sepsis Patients. Critical Care Explorations, 3(12), p.e0570. PDF File

2020

• 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

• A. Mohammed, P.S.B. Podila, R. L. Davis, J. S. Hankins, R. Kamaleswaran, “Machine learning predicts early-onset organ failure in critically ill sickle cell disease patients” JMIR, April 2020 DOI: 10.2196/14693 PDF File

• O Akbilgic, R Kamaleswaran, A Mohammed, GW Ross, K Masaki, H Petrovitch, CM Tanner, RL Davis, SM Goldman, “Electrocardiographic changes predate Parkinson’s disease onset” Scientific Reports (Nature Publisher Group) 2020 Jul 9;10(1):1-6. PDF File

• AI Wong, PC Cheung, R. Kamaleswaran, GS Martin and AL Holder (2020) Machine Learning Methods to Predict Acute Respiratory Failure and Acute Respiratory Distress Syndrome. Front. Big Data 3:579774. doi: 10.3389/fdata.2020.579774 PDF File

2019

• A. Shaban-Nejad, R. Kamaleswaran, E. Kyong Shin, and O. Akbilgic, “Chapter 6: Health Intelligence,” in Biomedical Information Technology 2e, 2019. (In print)

• 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. 

• 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 PDF File

• 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. PDF File

• 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. PDF File

• 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 https://doi.org/10.1016/j.ijmedinf.2018.12.002 PDF File

• 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. PDF File

• 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 PDF File

• 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. PDF File

• 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) PDF File

• 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.

• 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) PDF File

• R. Kamaleswaran “Early prediction of sepsis in children and adults using machine learning” Internal Medicine Conference, Allegheny General Hospital, Pittsburgh, PA, April 2019 PDF File

• 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 PDF File

• R. Kamaleswaran “Sepsis Prediction using High-Frequency Data Streams: A path towards clinical implementation” West Penn Hospital Academic Meeting, Pittsburgh, PA, February 2019

• 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

2018

• 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 PDF File

• 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 https://doi.org/10.1007/s41666-018-0040-y 

• 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  

• 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. https://doi.org/10.1088/1361-6579/aaaa9d. 

• 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 

• 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 

• 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

• R. Kamaleswaran and S. H. Shah “Applying Artificial Intelligence in the PICU” Center for Health Systems Improvement, Memphis TN, September 2018

• R. Kamaleswaran, “Machine learning in Medicine”, Hu Lab, University of California San Francisco, San Francisco, CA. August 2018.

2017

• 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  PDF File

• 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. PDF File

• 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. PDF File

• 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 PDF File

• 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

• R. Kamaleswaran, “Big Data in Critical Care”, Statistical Research Group, University of Tennessee Health Science Center, July 2017

• R. Kamaleswaran, “Moving towards Predictive Analytics in Real Time in Intensive Care Units”, Indian Institute of Information Technology (IIIT Bangalore), June 2017, Bangalore India

• R. Kamaleswaran, “Event Stream Processing of Biosensor Data in the Intensive Care Unit”, Indian Institute of Science (IISc Bangalore), June 2017, Bangalore India

• R. Kamaleswaran, “Dynamic Visual Analytics and Online Event Stream Analytics”, UT/KBRIN Bioinformatics Summit April 21-23, 2017 Montgomery Bell State Park, TN.

• 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 PDF File

• R. Kamaleswaran, O. Akbilgic “Machine Learning in Healthcare” Center for Biomedical Informatics, University of Tennessee Health Science Center, Memphis, TN January 2017

2016

• R. Kamaleswaran and C. McGregor, “A Review of Visual Representations of Physiologic Data.,” JMIR Med. informatics, vol. 4, no. 4, p. e31, Nov. 2016. PDF File

• 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. PDF File

• 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 PDF File

• R. Kamaleswaran, “Data-driven Healthcare” Le Bonheur Research Center, Memphis, TN December 2016

• R. Kamaleswaran, “Complex Business Processes for Event Stream Processing”, University of Ontario Institute of Technology, Oshawa, Canada, August 2016 PDF File

• R. Kamaleswaran, “PhysioEx: visual analysis of physiological event streams”, 18th EG/VGTC Conference on Visualization, Groningen, Netherlands, June 2016 PDF File

• R. Kamaleswaran “Visualizing Neonatal Spells: Temporal Visualization of High Frequency Cardiorespiratory Physiological Event Streams” Oshawa, Canada, June 2016

2015

• 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. PDF File

2014

• 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. PDF File

• 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.

• 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. PDF File

• 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. PDF File

2013

• 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. PDF File

2012

• 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.

• R. Kamaleswaran, C. McGregor. (2012). CBPSP: Complex Business Processes For Stream Processing. 25th Canadian Conference on Electrical and Computer Engineering (CCECE)  PDF File

• 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 PDF File

2011

• 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 PDF File

2010

• 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 PDF File

• 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. PDF File

2009

• 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 PDF File

REVIEWER/REFEREE: 

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