Articles

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.

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.

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.

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).

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.

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

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.

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. 

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

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. 

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.

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.

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.

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.

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.

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).

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.

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.

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)

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

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.

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.

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.

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).

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).

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

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

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.

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.

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.

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.

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.

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.

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

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.

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

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

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.

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.

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 

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.

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

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.

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.

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.