Rishikesan Kamaleswaran

Assistant Professor at Biomedical Engineering, Georgia Institute of Technology0 Followers

Dr. Rishikesan Kamaleswaran is solving complex problems in the realm of biomedical informatics, with a particular focus on enhancing patient outcomes in critical care settings. As an Assistant Professor at Emory University in the Department of Biomedical Informatics, he holds secondary appointments in Pediatrics and Emergency Medicine, reflecting his interdisciplinary approach to healthcare challenges. Dr. Kamaleswaran's academic journey began with a Ph.D. in Computer Science from the University of Ontario Institute of Technology, where he laid the groundwork for his innovative research in healthcare technology. Prior to his current role, Dr. Kamaleswaran contributed significantly to the Center for Biomedical Informatics and the Division of Critical Care Medicine at the University of Tennessee Health Science Center. His work there involved pioneering research in the detection and management of severe sepsis and multi-organ dysfunction syndrome. By leveraging his expertise in computer science and biomedical informatics, he has been instrumental in developing cutting-edge machine learning algorithms that aim to revolutionize the way sepsis is detected and treated. Dr. Kamaleswaran's research is characterized by its emphasis on integrating clinical data with biosensor data, a strategy that holds promise for improving sepsis outcomes. His work in multi-omics analysis is particularly noteworthy, as it seeks to unravel the complex biological interactions that underpin critical illnesses. By synthesizing data from various sources, he aims to create comprehensive models that can predict and mitigate the progression of severe sepsis, ultimately enhancing patient care. In addition to his research, Dr. Kamaleswaran is dedicated to mentoring the next generation of scientists and healthcare professionals. His commitment to education is evident in his involvement in various academic programs and initiatives that encourage interdisciplinary collaboration and innovation. Through his teaching and mentorship, he inspires students to explore the intersections of technology and medicine, preparing them to tackle the healthcare challenges of the future. Dr. Kamaleswaran's contributions to the field have been recognized through numerous publications and presentations at national and international conferences. His work not only advances scientific understanding but also has practical implications for improving healthcare delivery and patient outcomes. As he continues to push the boundaries of biomedical informatics, Dr. Kamaleswaran remains committed to translating his research into tangible benefits for patients and healthcare systems worldwide.

Research Interests

Severe sepsis detection multi-organ dysfunction syndrome machine learning algorithms multi-omics analysis clinical data integration biosensor data sepsis outcomes

Publications

1 - Prediction of Acute Respiratory Failure Requiring Advanced Respiratory Support in Advance of Interventions and Treatment: A Multivariable Prediction Model From Electronic Medical Record Data,Critical Care Explorations

An-Kwok I. Wong , Rishikesan Kamaleswaran , Azade Tabaie , Matthew A. Reyna , Christopher Josef , Chad Robichaux , Anne A. H. de Hond , Ewout W. Steyerberg , Andre L. Holder , Shamim Nemati , Timothy G. Buchman , James M. Blum ,

, e0402, 2021-05-01

2 - Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction,

Kelli Keats , Shiyuan Deng , Xianyan Chen , Tianyi Zhang , John W. Devlin , David J. Murphy , Susan E. Smith , Brian Murray , Rishikesan Kamaleswaran , Andrea Sikora ,

, 2024-03-22

3 - Coronavirus Disease 2019 Temperature Trajectories Correlate With Hyperinflammatory and Hypercoagulable Subphenotypes,Critical Care Medicine

Sivasubramanium V. Bhavani , Philip A. Verhoef , Cheryl L. Maier , Chad Robichaux , William F. Parker , Andre Holder , Rishikesan Kamaleswaran , May D. Wang , Matthew M. Churpek , Craig M. Coopersmith ,

, 212-223, 2022-02-01

5 - Altered Heart Rate Variability Early in ICU Admission Differentiates Critically Ill Coronavirus Disease 2019 and All-Cause Sepsis Patients,Critical Care Explorations

Rishikesan Kamaleswaran , Ofer Sadan , Prem Kandiah , Qiao Li , Craig M. Coopersmith , Timothy G. Buchman ,

, e0570, 2021-12-01

6 - Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 Hours post ICU admission,

Shayantan Banerjee , Akram Mohammed , Hector R. Wong , Nades Palaniyar , Rishikesan Kamaleswaran ,

, 2020-06-15

7 - Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model,Critical Care

Andrea Sikora , Alireza Rafiei , Milad Ghiasi Rad , Kelli Keats , Susan E. Smith , John W. Devlin , David J. Murphy , Brian Murray , Rishikesan Kamaleswaran ,

, 2023-05-02

8 - The Precision Resuscitation With Crystalloids in Sepsis (PRECISE) Trial,JAMA Network Open

Sivasubramanium V. Bhavani , Andre Holder , Danielle Miltz , Rishikesan Kamaleswaran , Sharaf Khan , Kirk Easley , David J. Murphy , Nicole Franks , David W. Wright , Colleen Kraft , Matthew W. Semler , Matthew M. Churpek , Greg S. Martin , Craig M. Coopersmith ,

, e2434197, 2024-09-18

9 - Externally Validated Deep Learning Model to Identify Prodromal Parkinson’s Disease from Electrocardiogram,

Ibrahim Karabayir , Fatma Gunturkun , Liam Butler , Samuel M Goldman , Rishikesan Kamaleswaran , Robert L Davis , Kalea Colletta , Lokesh Chinthala , John L Jefferies , Kathleen Bobay , Webb Ross , Helen Petrovitch , Kamal Masaki , Carolina Tanner , Oguz Akbilgic ,

, 2022-11-21

11 - RNA Sequencing Analysis of Monocytes Exposed to Airway Fluid From Children With Pediatric Acute Respiratory Distress Syndrome,Critical Care Explorations

Jocelyn R. Grunwell , Min Huang , Susan T. Stephenson , Mallory Tidwell , Michael J. Ripple , Anne M. Fitzpatrick , Rishikesan Kamaleswaran ,

, e1125, 2024-10-01

12 - Generalization in Clinical Prediction Models: The Blessing and Curse of Measurement Indicator Variables,Critical Care Explorations

Joseph Futoma , Morgan Simons , Finale Doshi-Velez , Rishikesan Kamaleswaran ,

, e0453, 2021-06-25

13 - Abstract 4819: Assessing the environmental factors in two Ontario communities with diverging colorectal cancer incidence rates .,Cancer Research

Jeavana Sritharan , Rishikesan Kamaleswaran , Ken McFarlan , Manon Lemonde , Clemon George , Otto Sanchez ,

, 4819-4819, 2013-04-01

14 - 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

Jocelyn R. Grunwell , Milad G. Rad , Michael J. Ripple , Nadir Yehya , Hector R. Wong , Rishikesan Kamaleswaran ,

, 2023-03-17

15 - Augmenting mortality prediction with medication data and machine learning models,

Brian Murray , Tianyi Zhang , Amoreena Most , Xianyan Chen , Susan E. Smith , John W. Devlin , David J. Murphy , Andrea Sikora , Rishikesan Kamaleswaran ,

, 2024-04-19

16 - Racial Differences in Accuracy of Predictive Models for High-Flow Nasal Cannula Failure in COVID-19,Critical Care Explorations

Philip Yang , Ismail A. Gregory , Chad Robichaux , Andre L. Holder , Greg S. Martin , Annette M. Esper , Rishikesan Kamaleswaran , Judy W. Gichoya , Sivasubramanium V. Bhavani ,

, e1059, 2024-03-01

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