The C4AI uses a use-directed, problem-focused, patient-centered approach to enhance the use of novel artificial intelligence methods for children and adults with congenital heart disease.


The vision of the C4AI is to bring novel technologies, centered around artificial intelligence and machine learning, to improve the care of children and adults with congenital heart disease. The C4AI is a multidisciplinary group of clinician-scientists, led by Dr. Animesh (Aashoo) Tandon, MD, MS, and Dr. Tara Karamlou, MD, MSc.

Ongoing projects

The C4AI has a broad vision of projects that will use both breadth and depth of data to answer important clinical questions for our patients. This includes the use of large, national databases; longitudinal and time-series physiological data from patients; and the development of novel datasources including wearable biosensors.

Dr. Karamlou's work focuses on large datasets to elicit the drivers of surgical outcomes; social and economic determinants of health; and use of large language models to derive semantic understanding in literature and in clinical documentation. 

Dr. Baloglu’s research is focused on predictive analytics. His projects utilize mathematical modeling and machine learning techniques to predict and potentially prevent adverse events (e.g., cardiac arrest) in intensive care unit patients.

Another project, led by Drs. Baloglu and Tandon, focuses on the detection of low cardiac output syndrome (LCOS) after surgery for congenital heart disease. We are using novel noninvasive biosensor technologies, combined with time series data analytics, to find physiologic patterns to predict LCOS.

Work with us

Our team would love to work with you. For questions or to request more information, please email Dr. Tandon.

Faculty Selected Publications

Selected Publications

Bhattacharya S, Nikbakht M, Alden A, Tan P, Wang J, Alhalimi TA, Kim S, Wang P, Tanaka H, Tandon A, Coyle EF, Inan OT, Lu N. A Chest-Conformable, Wireless Electro-Mechanical E-Tattoo for Measuring Multiple Cardiac Time Intervals. Advanced Electronic Materials. 2023. https://doi.org/10.1002/aelm.202201284.

Tandon A, Nguyen HH, Avula S, Seshadri DR, Patel A, Fares M, Baloglu O, Amdani S, Jafari R, Inan OT, Drummond CK. Wearable Biosensors in Congenital Heart Disease. JACC: Advances. 2023; 2(2):100267. https://pubmed.ncbi.nlm.nih.gov/37152621/. doi: https://doi.org/10.1016/j.jacadv.2023.100267.

Drummond CK, Tandon A. Advancing Wearable Technology for Monitoring Heart Activity in Pediatric Populations. Canadian Journal of Cardiology: Pediatric & Congenital Heart Disease. 2023. https://doi.org/10.1016/j.cjcpc.2023.06.003.

Tandon A, Bhattacharya S, Morca A, Inan OT, Munther DS, Ryan SD, Latifi SQ, Lu N, Lasa JJ, Marino BS, Baloglu O. Non-invasive Cardiac Output Monitoring in Congenital Heart Disease. Current Treatment Options in Pediatrics. 2023. https://doi.org/10.1007/s40746-023-00274-1

Nagy M, Onder AM, Rosen D, Mullett CJ, Morca M, Baloglu O. Predicting pediatric cardiac surgery-associated acute kidney injury using machine learning. Pediatr Nephrol. Published Online November 07, 2023.  https://doi.org/10.1007/s00467-023-06197-1

Baloglu O, Ryan SD, Onder AM, Rosen D, Mullett CJ, Munther DS. A Clinical Mathematical Model Estimating Postoperative Urine Output in Children Underwent Cardiopulmonary By-pass for Congenital Heart Surgery. J Pediatr Intensive Care.  2022. DOI: 10.1055/s-0042-1758474

Baloglu O, Kormos K, Worley S, Latifi SQ. A Novel Situational Awareness Scoring System in Pediatric Cardiac Intensive Care Unit Patients. J Pediatr Intensive Care. Published online18 February 2022. DOI: 10.1055/s-0042-1742675

Baloglu O, Nagy M, Ezetendu C, Latifi SQ, Nazha A. Simplified Paediatric Index Of Mortality 3 (PIM3) Score By Explainable Machine Learning Algorithm. Crit Care Explor. 2021 Oct 19;3(10):e0561

Baloglu O, Latifi SQ, Nazha A. What is Machine Learning? Arch Dis Child Educ Pract Ed. 2021 Feb 8:edpract-2020-319415.