Rishi Kamaleswaran

Trauma, Acute, and Critical Care Surgery

Associate Professor in Surgery

Rishi Kamaleswaran Profile Photo

Bio

My research focuses on the application of artificial intelligence, machine learning, and data analytics in healthcare, particularly in critical care and perioperative medicine; and cystic fibrosis. I have published numerous papers on the development of predictive models for sepsis, acute respiratory distress syndrome, and other critical conditions. My work utilizes large datasets, electronic health records, and physiological waveform analysis to improve patient outcomes. I have also explored the use of deep learning techniques for disease diagnosis and prediction, including the detection of cardiac arrhythmias and Parkinson's disease. Additionally, my research has investigated the potential of wearable sensors and remote patient monitoring to enhance healthcare delivery. Through collaborations with clinicians and researchers, I have validated and translated my models into clinical practice. Overall, my goal is to leverage data-driven approaches to transform healthcare and improve patient care.

Education

B.H.S. University of Ontario Institute of Technology (Canada), 2009
M.S. University of Ontario Institute of Technology (Canada), 2011
Ph.D. University of Ontario Institute of Technology (Canada), 2016

Positions

Associate Professor in Surgery
Associate Professor in the Department of Electrical and Computer Engineering
Associate Professor of Biomedical Engineering
Associate Professor in Anesthesiology
Associate Professor of Biostatistics & Bioinformatics

Courses Taught

BME 791: Graduate Independent Study
BME 789: Internship in Biomedical Engineering

Publications

Rad M, Rafiei A, Grunwell J, Kamaleswaran R. Tackling the small imbalanced horizontal dataset regressions by Stability Selection and SMOGN: a case study of ventilation-free days prediction in the pediatric intensive care unit and the importance of PRISM. Int J Med Inform. 2025 Apr;196:105809.
Upadhyaya P, Wang J, Mathew DT, Ali A, Tallowin S, Gann E, et al. PREDICTING SEPSIS-INDUCED HYPOTENSION PATIENT ATTRIBUTES FOR RESTRICTIVE VERSUS LIBERAL FLUID STRATEGY. Shock. 2025 Mar 1;63(3):399u2013405.
Murphy DJ, Anderson W, Heavner SH, Al-Hakim T, Cruz-Cano R, Laudanski K, et al. Development of a Core Critical Care Data Dictionary With Common Data Elements to Characterize Critical Illness and Injuries Using a Modified Delphi Method. Crit Care Med. 2025 Feb 21;
Henry K, Deng S, Chen X, Zhang T, Devlin JW, Murphy DJ, et al. Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction. Pharmacotherapy. 2025 Feb;45(2):76u201386.
Shi H, Book WM, Ivey LC, Rodriguez FH, Raskind-Hood C, Downing KF, et al. A Generalized Machine Learning Model for Identifying Congenital Heart Defects (CHDs) Using ICD Codes. Birth Defects Res. 2025 Feb;117(2):e2440.
Smith S, Zhao B, Deng S, Hu M, Zhang T, Kong Y, et al. 1368: MACHINE LEARNING-BASED PREDICTION OF ICU COMPLICATIONS USING MEDICATION DATA: A VALIDATION STUDY. In: Critical Care Medicine. Ovid Technologies (Wolters Kluwer Health); 2025.
Krishnan P, Sikora A, Upadhyaya P, Murray B, Yang P, Esper A, et al. 1047: MULTIMODAL TREATMENT EFFECT ON HEART RATE VARIABILITY AMONG VASOACTIVE MEDICATION USE IN SEPSIS. In: Critical Care Medicine. Ovid Technologies (Wolters Kluwer Health); 2025.
Pathak A, Marshall C, Davis C, Yang P, Kamaleswaran R. RespBERT: A Multi-Site Validation of a Natural Language Processing Algorithm, of Radiology Notes to Identify Acute Respiratory Distress Syndrome (ARDS). IEEE Journal of Biomedical and Health Informatics. 2025 Jan 1;29(2):1455u201363.
Moore R, Chanci D, Brown S, Ripple MJ, Bishop NR, Grunwell J, et al. PROGNOSTIC ACCURACY OF MACHINE LEARNING MODELS FOR IN-HOSPITAL MORTALITY AMONG CHILDREN WITH PHOENIX SEPSIS ADMITTED TO THE PEDIATRIC INTENSIVE CARE UNIT. Shock. 2025 Jan 1;63(1):80u20137.
Song J, McNeany J, Wang Y, Daley T, Stecenko A, Kamaleswaran R. Riemannian manifold-based geometric clustering of continuous glucose monitoring to improve personalized diabetes management. Comput Biol Med. 2024 Dec;183:109255.
Wang WK, Jeong H, Hershkovich L, Cho P, Singh K, Lederer L, et al. Tree-based classification model for Long-COVID infection prediction with age stratification using data from the National COVID Cohort Collaborative. JAMIA Open. 2024 Dec;7(4):ooae111.
Huxford C, Rafiei A, Nguyen V, Wiens MO, Ansermino JM, Kissoon N, et al. The 2024 Pediatric Sepsis Challenge: Predicting In-Hospital Mortality in Children With Suspected Sepsis in Uganda. Pediatr Crit Care Med. 2024 Nov 1;25(11):1047u201350.
Chen Y-N, Zhou J, Kirkham HS, Witt EA, Jenness SM, Wall KM, et al. Understanding Typology of Preexposure Prophylaxis (PrEP) Persistence Trajectories Among Male PrEP Users in the United States. Open Forum Infect Dis. 2024 Nov;11(11):ofae584.
Grunwell JR, Huang M, Stephenson ST, Tidwell M, Ripple MJ, Fitzpatrick AM, et al. RNA Sequencing Analysis of Monocytes Exposed to Airway Fluid from Children with Pediatric Acute Respiratory Distress Syndrome. Critical Care Explorations. 2024 Oct 4;6(10):e1125.
Choudhary T, Upadhyaya P, Davis CM, Yang P, Tallowin S, Lisboa FA, et al. Derivation and validation of generalized sepsis-induced acute respiratory failure phenotypes among critically ill patients: a retrospective study. Crit Care. 2024 Oct 1;28(1):321.
Kunz M, Rott KW, Hurwitz E, Kunisaki K, Sun J, Wilkins KJ, et al. The Intersections of COVID-19, HIV, and Race/Ethnicity: Machine Learning Methods to Identify and Model Risk Factors for Severe COVID-19 in a Large U.S. National Dataset. AIDS Behav. 2024 Oct;28(Suppl 1):5u201321.
Kobara S, Yamamoto R, Rad MG, Grunwell JR, Hikota N, Uzawa Y, et al. Association between comorbidities at ICU admission and post-Sepsis physical impairment: A retrospective cohort study. J Crit Care. 2024 Oct;83:154833.
Bhavani SV, Holder A, Miltz D, Kamaleswaran R, Khan S, Easley K, et al. The Precision Resuscitation With Crystalloids in Sepsis (PRECISE) Trial: A Trial Protocol. JAMA Netw Open. 2024 Sep 3;7(9):e2434197.
Rafiei A, Moore R, Jahromi S, Hajati F, Kamaleswaran R. Meta-learning in Healthcare: A Survey. SN Computer Science. 2024 Aug 1;5(6).
Atreya MR, Huang M, Moore AR, Zheng H, Hasin-Brumshtein Y, Fitzgerald JC, et al. Identification and transcriptomic assessment of latent profile pediatric septic shock phenotypes. Crit Care. 2024 Jul 17;28(1):246.
Iytha Sridhar R, Kamaleswaran R. Lung segment anything model (LuSAM): a decoupled prompt-integrated framework for automated lung segmentation on chest x-Ray images. Biomed Phys Eng Express. 2024 Jul 10;10(5).
Fitzpatrick AM, Grunwell JR, Gaur H, Kobara S, Kamaleswaran R. Plasma metabolomics identifies differing endotypes of recurrent wheezing in preschool children differentiated by symptoms and social disadvantage. Sci Rep. 2024 Jul 9;14(1):15813.
Sikora A, Keats K, Murphy DJ, Devlin JW, Smith SE, Murray B, et al. A common data model for the standardization of intensive care unit medication features. JAMIA Open. 2024 Jul 1;7(2).
Soliman MM, Marshall C, Kimball JP, Choudhary T, Clermont G, Pinsky MR, et al. Parsimonious waveform-derived features consisting of pulse arrival time and heart rate variability predicts the onset of septic shock. Biomedical Signal Processing and Control. 2024 Jun 1;92.
Fitzpatrick AM, Huang M, Mohammad AF, Stephenson ST, Kamaleswaran R, Grunwell JR. Dysfunctional neutrophil type 1 interferon responses in preschool children with recurrent wheezing and IL-4u2013mediated aeroallergen sensitization. Journal of Allergy and Clinical Immunology: Global. 2024 May 1;3(2).
Choudhary T, Upadhyaya P, Davis CM, Yang P, Tallowin S, Lisboa FA, et al. Derivation and Validation of Generalized Sepsis-induced Acute Respiratory Failure Phenotypes Among Critically Ill Patients: A Retrospective Study. Res Sq. 2024 Apr 30;
Murray B, Zhang T, Most A, Chen X, Smith SE, Devlin JW, et al. Augmenting mortality prediction with medication data and machine learning models. Cold Spring Harbor Laboratory. 2024.
Pacilli M, Kamaleswaran R. New Genetic Biomarkers to Diagnose Pediatric Appendicitis. JAMA Pediatr. 2024 Apr 1;178(4):341u20132.
Heneghan JA, Walker SB, Fawcett A, Bennett TD, Dziorny AC, Sanchez-Pinto LN, et al. The Pediatric Data Science and Analytics Subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators Network: Use of Supervised Machine Learning Applications in Pediatric Critical Care Medicine Research. Pediatr Crit Care Med. 2024 Apr 1;25(4):364u201374.
Keats K, Deng S, Chen X, Zhang T, Devlin JW, Murphy DJ, et al. Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction. Cold Spring Harbor Laboratory. 2024.
Huneault HE, Gent AE, Cohen CC, He Z, Jarrell ZR, Kamaleswaran R, et al. Validation of a screening panel for pediatric metabolic dysfunction-associated steatotic liver disease using metabolomics. Hepatol Commun. 2024 Mar 1;8(3).
Chanci D, Grunwell JR, Rafiei A, Moore R, Bishop NR, Rajapreyar P, et al. Development and Validation of a Model for Endotracheal Intubation and Mechanical Ventilation Prediction in PICU Patients. Pediatr Crit Care Med. 2024 Mar 1;25(3):212u201321.
Yang P, Gregory IA, Robichaux C, Holder AL, Martin GS, Esper AM, et al. Racial Differences in Accuracy of Predictive Models for High-Flow Nasal Cannula Failure in COVID-19. Crit Care Explor. 2024 Mar;6(3):e1059.
Kobara S, Yang C, Fensore C, Gaur H, Natarajan K, Davis CM, et al. ALTERED SPHINGOLIPID BIOSYNTHESIS DURING ARDS WAS ASSOCIATED WITH PHYSICAL IMPAIRMENT AT HOSPITAL DISCHARGE IN SURGICAL ICU PATIENTS. In: SHOCK. 2024. p. 9u20139.
Rafiei A, Moore R, Choudhary T, Marshall C, Smith G, Roback JD, et al. Robust Meta-Model for Predicting the Likelihood of Receiving Blood Transfusion in Non-traumatic Intensive Care Unit Patients. Health Data Science. 2024 Jan 1;4.
Kobara S, Rafiei A, Nateghi M, Bozkurt S, Kamaleswaran R, Sarker A. Social Media as a Sensor: Analyzing Twitter Data for Breast Cancer Medication Effects Using Natural Language Processing. In 2024. p. 345u201354.
Huneault H, Tiwari P, Jarrell Z, Smith M, Tovar AR, Sanchez-Torres C, et al. CLINICALLY DISTINCT METABOTYPES OF PEDIATRIC METABOLIC DYSFUNCTIONASSOCIATED STEATOTIC LIVER DISEASE: AN UNSUPERVISED MACHINE LEARNING ANALYSIS OF CHILDREN ENROLLED IN NASH CRN STUDIES. In: HEPATOLOGY. 2024. p. S1u20132.
Chanci D, Grunwell J, Rafiei A, Brown S, Ripple M, Bishop N, et al. DEVELOPMENTANDVALIDATIONOFAMACHINELEARNING MODELTO PREDICT SEPSIS IN CRITICALLY ILL CHILDREN. In: SHOCK. 2024. p. 33u201333.
Atreya MR, Banerjee S, Lautz AJ, Alder MN, Varisco BM, Wong HR, et al. Machine learning-driven identification of the gene-expression signature associated with a persistent multiple organ dysfunction trajectory in critical illness. EBioMedicine. 2024 Jan;99:104938.
Upadhyaya P, Wang J, De Vale JS, Lisboa F, Schobel S, Elster E, et al. CHARACTERIZING SEPSIS-INDUCED HYPOTENSION PATIENTS WHO BENEFIT FROM AN EARLY VASOPRESSOR STRATEGY. In: CRITICAL CARE MEDICINE. 2024.
Varma JK, Zang C, Carton TW, Block JP, Khullar DJ, Zhang Y, et al. Excess burden of respiratory and abdominal conditions following COVID-19 infections during the ancestral and Delta variant periods in the United States: An EHR-based cohort study from the RECOVER program. PLoS One. 2024;19(6):e0282451.
Rafiei A, Ghiasi Rad M, 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. Comput Biol Med. 2024 Jan;168:107749.
Keats K, Deng S, Chen X, Zhang T, Devlin J, Murphy D, et al. UNSUPERVISED MACHINE LEARNING OF ICU MEDICATION USE PATTERNS TO PREDICT FLUID OVERLOAD. In: CRITICAL CARE MEDICINE. 2024.
Newsome AS, Devlin J, Murray B, Rowe S, Kamaleswaran R, Zhang T, et al. ASSOCIATION BETWEEN MEDICATION REGIMEN COMPLEXITY AND ICU MORTALITY IN A LARGE COHORT. In: CRITICAL CARE MEDICINE. 2024.
Fitzpatrick AM, Mohammad AF, Huang M, Stephenson ST, Patrignani J, Kamaleswaran R, et al. Functional immunophenotyping of blood neutrophils identifies novel endotypes of viral response in preschool children with recurrent wheezing. J Allergy Clin Immunol. 2023 Dec;152(6):1433u201343.
Wong A-KI, Kamaleswaran R. POSTCARDS from a SIESTA: Crossing the Translational and Generalizability Gap for Predictive Models of Acute Respiratory Distress Syndrome-Related Mortality. Crit Care Med. 2023 Dec 1;51(12):1814u20136.
Sanchez-Perez JA, Gazi AH, Mabrouk SA, Berkebile JA, Ozmen GC, Kamaleswaran R, et al. Enabling Continuous Breathing-Phase Contextualization via Wearable-Based Impedance Pneumography and Lung Sounds: A Feasibility Study. IEEE J Biomed Health Inform. 2023 Dec;27(12):5734u201344.
Wang J, de Vale JS, Gupta S, Upadhyaya P, Lisboa FA, Schobel SA, et al. ClotCatcher: a novel natural language model to accurately adjudicate venous thromboembolism from radiology reports. BMC Med Inform Decis Mak. 2023 Nov 16;23(1):262.
Sikora A, Zhang T, Murphy DJ, Smith SE, Murray B, Kamaleswaran R, et al. Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU. Sci Rep. 2023 Nov 10;13(1):19654.
Shi H, Book W, Raskind-Hood C, Downing KF, Farr SL, Bell MN, et al. A machine learning model for predicting congenital heart defects from administrative data. Birth Defects Res. 2023 Nov 1;115(18):1693u2013707.
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):671u20137.
Krishnan P, Rad MG, Agarwal P, Marshall C, Yang P, Bhavani SV, et al. HIRA: Heart Rate Interval based Rapid Alert score to characterize autonomic dysfunction among patients with sepsis-related acute respiratory failure (ARF). Physiol Meas. 2023 Oct 13;44(10).
Ramesh DB, Iytha Sridhar R, Upadhyaya P, Kamaleswaran R. Lung Grounded-SAM (LuGSAM): A Novel Framework for Integrating Text prompts to Segment Anything Model (SAM) for Segmentation Tasks of ICU Chest X-Rays. Institute of Electrical and Electronics Engineers (IEEE). 2023.
Ramesh DB, Iytha Sridhar R, Upadhyaya P, Kamaleswaran R. Lung Grounded-SAM (LuGSAM): A Novel Framework for Integrating Text prompts to Segment Anything Model (SAM) for Segmentation Tasks of ICU Chest X-Rays. Institute of Electrical and Electronics Engineers (IEEE). 2023.
Holder AL, Kamaleswaran R. Facilitating the Next Paradigm Shift in Critical Care Through Artificial Intelligence. Crit Care Clin. 2023 Oct;39(4):xiiiu2013xiv.
Jones SE, Bradwell KR, Chan LE, McMurry JA, Olson-Chen C, Tarleton J, et al. Who is pregnant? Defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C). JAMIA Open. 2023 Oct 1;6(3).
Sikora A, Jeong H, Yu M, Chen X, Murray B, Kamaleswaran R. Cluster analysis driven by unsupervised latent feature learning of medications to identify novel pharmacophenotypes of critically ill patients. Sci Rep. 2023 Sep 20;13(1):15562.
Lyu T, Liang C, Liu J, Hung P, Zhang J, Campbell B, et al. Risk for stillbirth among pregnant individuals with SARS-CoV-2 infection varied by gestational age. Am J Obstet Gynecol. 2023 Sep;229(3):288.e1-288.e13.
Arora M, Davis CM, Gowda NR, Foster DG, Mondal A, Coopersmith CM, et al. Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome. Bioengineering (Basel). 2023 Aug 8;10(8).
Wei S, Xie Y, Josef CS, Kamaleswaran R. Granger Causal Chain Discovery for Sepsis-Associated Derangements via Continuous-Time Hawkes Processes. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2023. p. 2536u201346.
Cottrill KA, Chandler JD, Kobara S, Stephenson ST, Mohammad AF, Tidwell M, et al. 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).
Karabayir I, Gunturkun F, Butler L, Goldman SM, Kamaleswaran R, Davis RL, et al. Externally validated deep learning model to identify prodromal Parkinson's disease from electrocardiogram. Sci Rep. 2023 Jul 29;13(1):12290.
Vinson AJ, Anzalone A, Schissel M, Dai R, French ET, Olex AL, et al. Hormone replacement therapy and COVID-19 outcomes in solid organ transplant recipients compared with the general population. Am J Transplant. 2023 Jul;23(7):1035u201347.
Rafiei A, Rad MG, Sikora A, Kamaleswaran R. Improving irregular temporal modeling by integrating synthetic data to the electronic medical record using conditional GANs: a case study of fluid overload prediction in the intensive care unit. medRxiv. 2023 Jun 27;
Ripple MJ, Huang M, Stephenson ST, Mohammad AF, Tidwell M, Fitzpatrick AM, et al. RNA Sequencing Analysis of CD4⁺T Cells Exposed to Airway Fluid from Children with Pediatric Acute Respiratory Distress Syndrome. Critical Care Explorations. 2023 Jun 23;5(7):E0935.
Nikbakht M, Gazi AH, Zia J, An S, Lin DJ, Inan OT, et al. Synthetic seismocardiogram generation using a transformer-based neural network. J Am Med Inform Assoc. 2023 Jun 20;30(7):1266u201373.
Iytha Sridhar R, Kamaleswaran R. Lung Segment Anything Model (LuSAM): A Prompt-integrated Framework for Automated Lung Segmentation on ICU Chest X-Ray Images. Institute of Electrical and Electronics Engineers (IEEE). 2023.
Iytha Sridhar R, Kamaleswaran R. Lung Segment Anything Model (LuSAM): A Prompt-integrated Framework for Automated Lung Segmentation on ICU Chest X-Ray Images. Institute of Electrical and Electronics Engineers (IEEE). 2023.
Grunwell JR, Rad MG, Ripple MJ, Yehya N, Wong HR, Kamaleswaran R. Identification of a pediatric acute hypoxemic respiratory failure signature in peripheral blood leukocytes at 24 hours post-ICU admission with machine learning. Frontiers in Pediatrics. 2023 May 3;11.
Sikora A, Rafiei A, Rad MG, Keats K, Smith SE, Devlin JW, et al. Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model. Crit Care. 2023 May 2;27(1):167.
Cottrill KA, Rad MG, Ripple MJ, Stephenson ST, Mohammad AF, Tidwell M, et al. Cluster analysis of plasma cytokines identifies two unique endotypes of children with asthma in the pediatric intensive care unit. Sci Rep. 2023 Mar 2;13(1):3521.
Verhagen NB, Koerber NK, Szabo A, Taylor B, Wainaina JN, Evans DB, et al. Vaccination Against SARS-CoV-2 Decreases Risk of Adverse Events in Patients who Develop COVID-19 Following Cancer Surgery. Ann Surg Oncol. 2023 Mar;30(3):1305u20138.
Tabaie A, Orenstein EW, Kandaswamy S, Kamaleswaran R. Integrating structured and unstructured data for timely prediction of bloodstream infection among children. Pediatr Res. 2023 Mar;93(4):969u201375.
Sinclair EM, Cohen CC, Tran V, Jones DP, Alvarez JA, Kamaleswaran R, et al. Untargeted, High-Resolution Metabolomics in Pediatric Eosinophilic Esophagitis. J Pediatr Gastroenterol Nutr. 2023 Mar 1;76(3):355u201363.
Gadrey SM, Mohanty P, Haughey SP, Jacobsen BA, Dubester KJ, Webb KM, et al. Overt and Occult Hypoxemia in Patients Hospitalized with COVID-19. Critical Care Explorations. 2023 Jan 20;5(1):E0825.
Yang P, Sharma AA, Enriquez AB, Lin A, Latif MB, Esper AM, et al. Systemic TGF-Beta and Lower IFN-Gamma Drive Kidney, Lung, and Brain Damage in Sepsis. In: AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE. 2023.
Marshall CE, Narendrula S, Wang J, Vale JDS, Jeong H, Krishnan P, et al. A Comparison Between Sensitivity Analysis of Oxygenation Severity and Acute Respiratory Distress Syndrome (ARDS) of a Machine Learning Algorithm to Predict Hypoxic Respiratory Failure by Utilizing Features Derived From Electrocardiogram (ECG) and Routine Clinical Data. In: AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE. 2023.
Sikora A, Zhang T, Murphy D, Smith S, Murray B, Kamaleswaran R, et al. Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU. medRxiv. 2023.
Jones M, Winger A, Wernz C, Michel J, Jiang S, Zhou A, et al. Investigating the Impact of Temporal Labeling of Emergency Department Visits for COVID-19: Comparing Healthcare Disparities Analyses Using Comprehensive, Single-Site Data with National COVID Cohort Collaborative (N3C) Data. In: 2023 Systems and Information Engineering Design Symposium, SIEDS 2023. 2023. p. 297u2013302.
Huneault HE, Gent AE, Cohen CC, He Z, Jarrell ZR, Kamaleswaran R, et al. VALIDATION OF A PLASMA SCREENING PANEL FOR PEDIATRIC NONALCOHOLIC FATTY LIVER DISEASE USING METABOLOMICS. In: HEPATOLOGY. 2023. p. S902u2013S902.
Krishnan P, Marshall C, Narendrula S, Vale JGDS, Song J, Yang P, et al. Heart Rate Variability (HRV) Based Interpretable Machine Learning Algorithm for Prediction of All-cause Acute Respiratory Failure (ARF) Among ICU Patients. In: AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE. 2023.
Wei S, Xie Y, Josef CS, Kamaleswaran R. Causal Graph Discovery From Self and Mutually Exciting Time Series. IEEE Journal on Selected Areas in Information Theory. 2023 Jan 1;4:747u201361.
Arora M, Davis CM, Mondal A, Gowda NR, Foster DG, Kamaleswaran R. Optimizing the Synergistic Potential of Pseudo-Labels from Radiology Notes and Annotated Ground Truth in Identifying Pulmonary Opacities on Chest Radiographs for Early Detection of Acute Respiratory Distress Syndrome. AMIA Annu Symp Proc. 2023;2023:270u20139.
Cottrill KA, Stephenson ST, Mohammad AF, Kim SO, McCarty NA, Kamaleswaran R, et al. Exacerbation-prone pediatric asthma is associated with arginine, lysine, and methionine pathway alterations. J Allergy Clin Immunol. 2023 Jan;151(1):118-127.e10.
Kobara S, Rad MG, Grunwell JR, Coopersmith CM, Kamaleswaran R. Bioenergetic Crisis in ICU-Acquired Weakness Gene Signatures Was Associated with Sepsis-Related Mortality: A Brief Report. Critical Care Explorations. 2022 Dec 14;4(12):E0818.
Atreya MR, Sanchez-Pinto LN, Kamaleswaran R. Commentary: 'Critical illness subclasses: all roads lead to Rome'. Crit Care. 2022 Dec 14;26(1):387.
Ackerman K, Mohammed A, Chinthala L, Davis RL, Kamaleswaran R, Shafi NI. Features derived from blood pressure and intracranial pressure predict elevated intracranial pressure events in critically ill children. Sci Rep. 2022 Dec 12;12(1):21473.
Zeidan A, Evans DP, Smith RN, Tabaie A, Kamaleswaran R. Estimating the Prevalence of Intimate Partner Violence at an Urban Hospital Before and During the COVID-19 Pandemic Using a Novel Natural Language Processing Algorithm. Violence and Gender. 2022 Dec 1;9(4):164u20139.
Marshall CE, Narendrula S, Wang J, De Souza Vale JG, Jeong H, Krishnan P, et al. A Machine Learning Algorithm to Predict Hypoxic Respiratory Failure and risk of Acute Respiratory Distress Syndrome (ARDS) by Utilizing Features Derived from Electrocardiogram (ECG) and Routinely Clinical Data. Cold Spring Harbor Laboratory. 2022.
Grunwell JR, Rad MG, Stephenson ST, Mohammad AF, Opolka C, Fitzpatrick AM, et al. Functional immunophenotyping of children with critical status asthmaticus identifies differential gene expression responses in neutrophils exposed to a poly(I:C) stimulus. Sci Rep. 2022 Nov 16;12(1):19644.
Jeong H, Kamaleswaran R. Pivotal challenges in artificial intelligence and machine learning applications for neonatal care. Semin Fetal Neonatal Med. 2022 Oct;27(5):101393.
Tabaie A, Zeidan AJ, Evans DP, Smith RN, Kamaleswaran R. A Novel Technique to Identify Intimate Partner Violence in a Hospital Setting. West J Emerg Med. 2022 Sep 12;23(5):781u20138.
Ack SE, Loiseau SY, Sharma G, Goldstein JN, Lissak IA, Duffy SM, et al. Neurocritical Care Performance Measures Derived from Electronic Health Record Data are Feasible and Reveal Site-Specific Variation: A CHoRUS Pilot Project. Neurocrit Care. 2022 Aug;37(Suppl 2):276u201390.
Liu Z, Khojandi A, Li X, Mohammed A, Davis RL, Kamaleswaran R. A Machine Learningu2013Enabled Partially Observable Markov Decision Process Framework for Early Sepsis Prediction. INFORMS Journal on Computing. 2022 Jul 1;34(4):2039u201357.
Smith RN, Nyame-Mireku A, Zeidan A, Tabaie A, Meyer C, Muralidharan V, et al. Intimate Partner Violence at a Level-1 Trauma Center During the COVID-19 Pandemic: An Interrupted Time Series Analysis. Am Surg. 2022 Jul;88(7):1551u20133.
Kandaswamy S, Orenstein EW, Quincer E, Fernandez AJ, Gonzalez MD, Lu L, et al. Automated Identification of Immunocompromised Status in Critically Ill Children. Methods Inf Med. 2022 May;61(1u201302):46u201354.
Arora M, Zambrzycki SC, Levy JM, Esper A, Frediani JK, Quave CL, et al. Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS. Metabolites. 2022 Mar 1;12(3).
Sanchez-Perez JA, Berkebile JA, Nevius BN, Ozmen GC, Nichols CJ, Ganti VG, et al. A Wearable Multimodal Sensing System for Tracking Changes in Pulmonary Fluid Status, Lung Sounds, and Respiratory Markers. Sensors (Basel). 2022 Feb 2;22(3).
Bhavani SV, Verhoef PA, Maier CL, Robichaux C, Parker WF, Holder A, et al. Coronavirus Disease 2019 Temperature Trajectories Correlate With Hyperinflammatory and Hypercoagulable Subphenotypes. Crit Care Med. 2022 Feb 1;50(2):212u201323.
Loftus TJ, Tighe PJ, Ozrazgat-Baslanti T, Davis JP, Ruppert MM, Ren Y, et al. Ideal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible. PLOS Digit Health. 2022;1(1).
Wang J, Moore R, Xie Y, Kamaleswaran R. Improving sepsis prediction model generalization with optimal transport. In: Proceedings of Machine Learning Research. 2022. p. 474u201388.
Ripple M, Rad M, Stephenson S, Mohammad A, Opolka C, Fitzpatrick A, et al. CLUSTER ANALYSIS AND PROFILING OF AIRWAY FLUID METABOLITES IN PEDIATRIC ARDS. In: CRITICAL CARE MEDICINE. 2022. p. 584u2013584.
Xu Y, Khare A, Matlin G, Ramadoss M, Kamaleswaran R, Zhang C, et al. UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification. In: Advances in Neural Information Processing Systems. 2022.
Sanchez-Perez JA, Mabrouk S, Berkebile JA, Esper A, Yang P, Kamaleswaran R, et al. Initial Validation of Multi-Frequency Patch-Based Impedance Pneumography in Hospital Settings. In: Proceedings of IEEE Sensors. 2022.
Karabayir I, Butler L, Goldman SM, Kamaleswaran R, Gunturkun F, Davis RL, et al. Predicting Parkinson's Disease and Its Pathology via Simple Clinical Variables. J Parkinsons Dis. 2022;12(1):341u201351.
Zhou A, Beyah R, Kamaleswaran R. OnAI-Comp: An Online AI Experts Competing Framework for Early Sepsis Detection. IEEE/ACM Trans Comput Biol Bioinform. 2022;19(6):3595u2013603.
Singh V, Kamaleswaran R, Chalfin D, Buu00f1o-Soto A, San Roman J, Rojas-Kenney E, et al. A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers. iScience. 2021 Dec 17;24(12).
Kamaleswaran R, Sadan O, Kandiah P, Li Q, Coopersmith CM, Buchman TG. Altered Heart Rate Variability Early in ICU Admission Differentiates Critically Ill Coronavirus Disease 2019 and All-Cause Sepsis Patients. Critical Care Explorations. 2021 Dec 2;3(12):E0570.
Grunwell JR, Rad MG, Stephenson ST, Mohammad AF, Opolka C, Fitzpatrick AM, et al. Cluster analysis and profiling of airway fluid metabolites in pediatric acute hypoxemic respiratory failure. Sci Rep. 2021 Nov 26;11(1):23019.
Wong A-KI, Charpignon M, Kim H, Josef C, de Hond AAH, Fojas JJ, et al. Analysis of Discrepancies Between Pulse Oximetry and Arterial Oxygen Saturation Measurements by Race and Ethnicity and Association With Organ Dysfunction and Mortality. JAMA Netw Open. 2021 Nov 1;4(11):e2131674.
Tabaie A, Orenstein EW, Nemati S, Basu RK, Clifford GD, Kamaleswaran R. Deep Learning Model to Predict Serious Infection Among Children With Central Venous Lines. Frontiers in Pediatrics. 2021 Sep 15;9.
Kamaleswaran R, Sataphaty SK, Mas VR, Eason JD, Maluf DG. Artificial Intelligence May Predict Early Sepsis After Liver Transplantation. Frontiers in Physiology. 2021 Sep 6;12.
Steinberg R, Anderson B, Hu Z, Johnson TM, Ou2019Keefe JB, Plantinga LC, et al. Associations between remote patient monitoring programme responsiveness and clinical outcomes for patients with COVID-19. BMJ Open Qual. 2021 Sep;10(3).
Bennett TD, Moffitt RA, Hajagos JG, Amor B, Anand A, Bissell MM, 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 Network Open. 2021 Jul 13;4(7):e2116901u2013e2116901.
Goldstick JE, Guy GP, Losby JL, Baldwin G, Myers M, Bohnert ASB. Changes in Initial Opioid Prescribing Practices After the 2016 Release of the CDC Guideline for Prescribing Opioids for Chronic Pain. JAMA Network Open. 2021 Jul 13;4(7):e2116860u2013e2116860.
Mohammed A, Van Wyk F, Chinthala LK, Khojandi A, Davis RL, Coopersmith CM, et al. Temporal Differential Expression of Physiomarkers Predicts Sepsis in Critically Ill Adults. Shock. 2021 Jul 1;56(1):58u201364.
Futoma J, Simons M, Doshi-Velez F, Kamaleswaran R. Generalization in Clinical Prediction Models: The Blessing and Curse of Measurement Indicator Variables. Critical Care Explorations. 2021 Jun 25;3(7):E0453.
Grunwell JR, Rad MG, Stephenson ST, Mohammad AF, Opolka C, Fitzpatrick AM, et al. Machine Learning-Based Discovery of a Gene Expression Signature in Pediatric Acute Respiratory Distress Syndrome. Critical Care Explorations. 2021 Jun 15;3(6):E0431.
Tabaie A, Orenstein EW, Nemati S, Basu RK, Kandaswamy S, Clifford GD, et al. Predicting presumed serious infection among hospitalized children on central venous lines with machine learning. Comput Biol Med. 2021 May;132:104289.
Wong A-KI, Kamaleswaran R, Tabaie A, Reyna MA, Josef C, Robichaux C, et al. Prediction of Acute Respiratory Failure Requiring Advanced Respiratory Support in Advance of Interventions and Treatment: A Multivariable Prediction Model From Electronic Medical Record Data. Crit Care Explor. 2021 May;3(5):e0402.
Liu Z, Khojandi A, Mohammed A, Li X, Chinthala LK, Davis RL, et al. HeMA: A hierarchically enriched machine learning approach for managing false alarms in real time: A sepsis prediction case study. Comput Biol Med. 2021 Apr;131:104255.
Haendel MA, Chute CG, Bennett TD, Eichmann DA, Guinney J, Kibbe WA, et al. The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment. J Am Med Inform Assoc. 2021 Mar 1;28(3):427u201343.
Krishnan P, Marshall C, Yang P, Bhavani S, Holder A, Esper A, et al. Autonomic dysfunction characterized by Heart Rate Variability among patients with Sepsis-related Acute Respiratory Failure. medRxiv. 2021.
Kamaleswaran R, Sadan O, Kandiah P, Li Q, Thomas T, Blum J, et al. ALTERED HEART RATE VARIABILITY PREDICTS MORTALITY EARLY AMONG CRITICALLY ILL COVID-19 PATIENTS. In: CRITICAL CARE MEDICINE. 2021. p. 99u201399.
Ackerman K, Mohammed A, Chinthala L, Davis R, Kamaleswaran R, Shafi NI. HEMODYNAMIC FEATURES PREDICT ELEVATED INTRACRANIAL PRESSURE IN CRITICALLY ILL CHILDREN. In: JOURNAL OF NEUROTRAUMA. 2021. p. A32u2013A32.
Karabayir I, Butler L, Goldman S, Kamaleswaran R, Gunturkun F, Davis R, et al. Machine Learning (ML) Prediction of Parkinson's Disease (PD) Risk and Nigral Neuron Density. In: MOVEMENT DISORDERS. 2021. p. S278u20139.
Bhavani S, Holder A, Kamaleswaran R, Verhoef PA, Churpek MM, Wang M, et al. Body Temperature Trajectory Associated with Venous Thromboembolism in COVID-19 Patients. In: AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE. 2021.
Banerjee S, Mohammed A, Wong HR, Palaniyar N, Kamaleswaran R. 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. 2021;12:592303.
Singhal L, Garg Y, Yang P, Tabaie A, Wong AI, Mohammed A, et al. 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. 2021;16(9):e0257056.
Subbiah S, Aravindh R, Kamaleswaran R, Nayagam S. Detectroops - A Street Mascarar. In: Proceedings of the 2021 4th International Conference on Computing and Communications Technologies, ICCCT 2021. 2021. p. 111u20136.
Kodnad K, Tabaie A, Rosenblum JM, Kamaleswaran R. Predicting Same Hospital Readmission following Fontan Cavopulmonary Anastomosis using Machine Learning. In: Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. 2021. p. 933u20138.
Wong AKI, Cheung PC, Kamaleswaran R, Martin GS, Holder AL. Machine Learning Methods to Predict Acute Respiratory Failure and Acute Respiratory Distress Syndrome. Frontiers in Big Data. 2020 Nov 23;3.
Akbilgic O, Kamaleswaran R, Mohammed A, Ross GW, Masaki K, Petrovitch H, et al. Electrocardiographic changes predate Parkinson's disease onset. Sci Rep. 2020 Jul 9;10(1):11319.
Mahajan R, Kamaleswaran R, Akbilgic O. Comparative analysis between convolutional neural network learned and engineered features: A case study on cardiac arrhythmia detection. Cardiovascular Digital Health Journal. 2020 Jul 1;1(1):37u201344.
Mohammed A, Podila PSB, Davis RL, Ataga KI, Hankins JS, Kamaleswaran R. Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients With Sickle Cell Disease: Retrospective Study. J Med Internet Res. 2020 May 13;22(5):e14693.
Hegde C, Jiang Z, Suresha PB, Zelko J, Seyedi S, Smith MA, et al. AutoTriage - An Open Source Edge Computing Raspberry Pi-based Clinical Screening System. Cold Spring Harbor Laboratory. 2020.
McMahon AW, Cooper WO, Brown JS, Carleton B, Doshi-Velez F, Kohane I, et al. Big Data in the Assessment of Pediatric Medication Safety. Pediatrics. 2020 Feb;145(2).
Banerjee S, Mohammed A, Wong H, Palaniyar N, Kamaleswaran R. Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 Hours post ICU admission. bioRxiv. 2020.
Kamaleswaran R, Sadan O, Kandiah P, Li Q, Blum J, Coopersmith C, et al. Changes in non-linear and time-domain heart rate variability indices between critically ill COVID-19 and all-cause sepsis patients u2013 a retrospective study. medRxiv. 2020.
Srinivasan S, Begoli E, Peterson G, Muthiah M, Kamaleswaran R. MARKERS IN UNSTRUCTURED PROGRESS NOTES PREDICT IMMINENT ICU ADMISSION USING MACHINE LEARNING. In: CRITICAL CARE MEDICINE. 2020.
Kamaleswaran R, Lian J, Lin D-L, Molakapuri H, Nunna S, Shah P, et al. Predicting Volume Responsiveness Among Sepsis Patients Using Clinical Data and Continuous Physiological Waveforms. AMIA Annu Symp Proc. 2020;2020:619u201328.
Silvas K, Ou2019Neill J, Monk B, Schorr R, Kamaleswaran R. 114 Emergency Department Factors Associated With Early Rapid Responses Activation After Admission. In: Annals of Emergency Medicine. Elsevier BV; 2019. p. S46u2013S46.
Mohammed A, Cui Y, Mas VR, Kamaleswaran R. Differential gene expression analysis reveals novel genes and pathways in pediatric septic shock patients. Sci Rep. 2019 Aug 2;9(1):11270.
van Wyk F, Khojandi A, Williams B, MacMillan D, Davis RL, Jacobson DA, et al. A Cost-Benefit Analysis of Automated Physiological Data Acquisition Systems Using Data-Driven Modeling. Journal of Healthcare Informatics Research. 2019 Jun 15;3(2):245u201363.
van Wyk F, Khojandi A, Williams B, MacMillan D, Davis RL, Jacobson DA, et al. Correction to: A Cost-Benefit Analysis of Automated Physiological Data Acquisition Systems Using Data-Driven Modeling (Journal of Healthcare Informatics Research, (2019), 3, 2, (245-263), 10.1007/s41666-018-0040-y). Journal of Healthcare Informatics Research. 2019 Jun 15;3(2):264u20135.
Mohammed A, Podila PSB, Davis RL, Ataga KI, Hankins JS, Kamaleswaran R. Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients With Sickle Cell Disease: Retrospective Study (Preprint). JMIR Publications Inc. 2019.
van Wyk F, Khojandi A, Kamaleswaran R. Improving Prediction Performance Using Hierarchical Analysis of Real-Time Data: A Sepsis Case Study. IEEE J Biomed Health Inform. 2019 May;23(3):978u201386.
Kamaleswaran R, Akbilgic O, Hallman MA, West AN, Davis RL, Shah SH. Artificial Intelligence: Progress Towards an Intelligent Clinical Support System. Pediatr Crit Care Med. 2019 Apr;20(4):399.
van Wyk F, Khojandi A, Mohammed A, Begoli E, Davis RL, Kamaleswaran R. A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier. Int J Med Inform. 2019 Feb;122:55u201362.
Mohammed A, Podila P, Davis R, Ataga K, Hankins J, Kamaleswaran R. Machine learning predicts early-onset acute organ failure in critically ill patients with sickle cell disease. bioRxiv. 2019.
Mohammed A, Cui Y, Mas V, Kamaleswaran R. Differential gene expression analysis reveals novel genes and pathways in pediatric septic shock patients. bioRxiv. 2019.
Sutton JR, Mahajan R, Akbilgic O, Kamaleswaran R. PhysOnline: An Open Source Machine Learning Pipeline for Real-Time Analysis of Streaming Physiological Waveform. IEEE J Biomed Health Inform. 2019 Jan;23(1):59u201365.
Shaban-Nejad A, Kamaleswaran R, Shin EK, Akbilgic O. Health intelligence. In: Biomedical Information Technology. 2019. p. 197u2013215.
Kamaleswaran R, Koo C, Helmick R, Mas V, Eason J, Maluf D. Predicting early post-operative sepsis in liver transplantation applying artificial intelligence. In: TRANSPLANTATION. 2019. p. 42u201342.
Kamaleswaran R, Mahajan R, Akbilgic O, Shafi N, Davis R. MACHINE LEARNING APPLIED TO CONTINUOUS PHYSIOLOGIC DATA PREDICTS FEVER IN CRITICALLY ILL CHILDREN. In: CRITICAL CARE MEDICINE. 2019.
Kamaleswaran R, Akbilgic O, Hallman MA, West AN, Davis RL, Shah SH. Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the PICU. Pediatr Crit Care Med. 2018 Oct;19(10):e495u2013503.
Mahajan R, Kamaleswaran R, Akbilgic O. A hybrid feature extraction method to detect Atrial Fibrillation from single lead ECG recording. In: 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018. 2018. p. 116u20139.
Kamaleswaran R, Mahajan R, Akbilgic O. A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length. Physiol Meas. 2018 Mar 27;39(3):035006.
van Wyk F, Khojandi A, Davis R, Kamaleswaran R. Physiomarkers in Real-Time Physiological Data Streams Predict Adult Sepsis Onset Earlier Than Clinical Practice. bioRxiv. 2018.
Kamaleswaran R, Akbilgic O, Hallman M, West A, Davis R, Shah S. 1524: PHYSIOMARKER VARIABILITY FOR EARLY PREDICTION OF SEVERE SEPSIS IN THE PEDIATRIC INTENSIVE CARE UNIT. In: Critical Care Medicine. Ovid Technologies (Wolters Kluwer Health); 2018. p. 745u2013745.
Van Wyk F, Khojandi A, Kamaleswaran R, Akbilgic O, Nemati S, Davis RL. How much data should we collect? A case study in sepsis detection using deep learning. In: 2017 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2017. 2017. p. 109u201312.
Mahajan R, Kamaleswaran R, Howe JA, Akbilgic O. Cardiac rhythm classification from a short single lead ECG recording via random forest. In: Computing in Cardiology. 2017. p. 1u20134.
Mahajan R, Kamaleswaran R, Akbilgic O. Effects of varying sampling frequency on the analysis of continuous ECG data streams. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2017. p. 73u201387.
Kamaleswaran R, Collins C, James A, McGregor C. CoRAD: Visual Analytics for Cohort Analysis. In: Proceedings - 2016 IEEE International Conference on Healthcare Informatics, ICHI 2016. 2016. p. 517u201326.
Kamaleswaran R, McGregor C. A Review of Visual Representations of Physiologic Data. JMIR Med Inform. 2016 Nov 21;4(4):e31.
Kamaleswaran R, Collins C, James A, McGregor C. PhysioEx: Visual Analysis of Physiological Event Streams. Computer Graphics Forum. 2016 Jun 1;35(3):331u201340.
Kamaleswaran R, Wehbe RR, Edward Pugh J, Nacke L, McGregor C, James A. Collaborative multi-touch clinical handover system for the neonatal intensive care unit. Electronic Journal of Health Informatics. 2015 Jan 1;9(1).
Sritharan J, Kamaleswaran R, McFarlan K, Lemonde M, George C, Sanchez O. Environmental factors in an Ontario community with disparities in colorectal cancer incidence. Glob J Health Sci. 2014 Mar 24;6(3):175u201385.
Kamaleswaran R, McGregor C. A real-time multi-dimensional visualization framework for critical and complex environments. In: Proceedings - IEEE Symposium on Computer-Based Medical Systems. 2014. p. 325u20138.
Kamaleswaran R, Thommandram A, Zhou Q, Eklund M, Cao Y, Wang WP, et al. Cloud framework for real-time synchronous physiological streams to support rural and remote critical care. In: Proceedings of CBMS 2013 - 26th IEEE International Symposium on Computer-Based Medical Systems. 2013. p. 473u20136.
Sritharan J, Kamaleswaran R, McFarlan K, Lemonde M, George C, Sanchez O. Abstract 4819: Assessing the environmental factors in two Ontario communities with diverging colorectal cancer incidence rates . In: Cancer Research. American Association for Cancer Research (AACR); 2013. p. 4819u20134819.
Kamaleswaran R, Thommandram A, Zhou Q, Eklund M, Cao Y, Wang WP, et al. Cloud framework for real-time synchronous physiological streams to support rural and remote Critical Care. In: Proceedings - IEEE Symposium on Computer-Based Medical Systems. 2013. p. 473u20136.
Kamaleswaran R, McGregor C. CBPSP: Complex business processes for stream processing. In: 2012 25th IEEE Canadian Conference on Electrical and Computer Engineering: Vision for a Greener Future, CCECE 2012. 2012.
Kamaleswaran R, McGregor C, James A. A novel framework for event stream processing of clinical practice guidelines. In: Proceedings - IEEE-EMBS International Conference on Biomedical and Health Informatics: Global Grand Challenge of Health Informatics, BHI 2012. 2012. p. 933u20136.
Kamaleswaran R, McGregor C. CBP_SP: COMPLEX BUSINESS PROCESSES FOR STREAM PROCESSING. In: 2012 25TH IEEE CANADIAN CONFERENCE ON ELECTRICAL & COMPUTER ENGINEERING (CCECE). 2012.
Tomlinson C, Rafii M, Kamaleswaran R, Ball RO, Pencharz PB. Fractional synthesis rate of creatine from arginine in healthy adult men. In: FASEB JOURNAL. 2012.
Kamaleswaran R, McGregor C. Integrating complex business processes for knowledge-driven clinical decision support systems. In: Annu Int Conf IEEE Eng Med Biol Soc. 2012. p. 1306u20139.
Kawaleswaran R, Eklund M. A method for interactive hypothesis testing for clinical decision support systems using Ptolemy II. In: Canadian Conference on Electrical and Computer Engineering. 2011. p. 001278u201381.
Kawaleswaran R, Eklund M. A method for interactive hypothesis testing for Clinical Decision Support Systems using Ptolemy II. In: 2011 24TH CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE). 2011. p. 1278u201381.
Blount M, McGregor C, James A, Sow D, Kamaleswaran R, Tuuha S, et al. On the integration of an artifact system and a real-time healthcare analytics system. In: IHIu201910 - Proceedings of the 1st ACM International Health Informatics Symposium. 2010. p. 647u201355.
Percival J, McGregor C, Percival N, Kamaleswaran R, Tuuha S. A framework for nursing documentation enabling integration with HER and real-time patient monitoring. In: Proceedings - IEEE Symposium on Computer-Based Medical Systems. 2010. p. 468u201373.
Kamaleswaran R, McGregor C, Eklund J. A method for clinical and physiological event stream processing. In: Annu Int Conf IEEE Eng Med Biol Soc. 2010. p. 1170u20133.
Kamaleswaran R, McGregor C, Percival J. Service oriented architecture for the integration of clinical and physiological data for real-time event stream processing. In: Annu Int Conf IEEE Eng Med Biol Soc. 2009. p. 1667u201370.

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