A photo of Eneida Mendonca.

Eneida Mendonca, MD, PhD, FAAP, FACMI, FIAHSI

  • Director, Division of Biomedical Informatics
  • Rieveschl Chair of Biomedical Informatics
  • Professor, UC Department of Pediatrics

About

MD: School of Medicine, Federal University of Pelotas, Pelotas, RS, Brazil, 1987.

MS: Medicine (Major: Cardiology), University Foundation of Cardiology, Heart Institute of Rio Grande do Sul, Porto Alegre, RS, Brazil, 1996.

PhD: Biomedical Informatics, Graduate School of Arts and Science, Columbia University, New York, NY, 2002.

Residency: Pediatrics, São Lucas Hospital, Catholic University of Rio Grande do Sul, Porto Alegre, RS, Brazil, 1988.

Residency: Pediatrics (Focus: Pediatric Intensive Care), São Lucas Hospital, Catholic University of Rio Grande do Sul, Porto Alegre, RS, Brazil, 1990.

Board Certification: Pediatrics, Brazilian Medical Association and Brazilian Society of Pediatrics, Brazil, 1990.

Board Certification: Pediatric Intensive Care, Brazilian Medical Association, Brazilian Society of Pediatrics, and Brazilian Association for Intensive Care Medicine, Brazil, 1991.

Research Areas

Publications

The PICU Data Collaborative: A Novel, Multi-Institutional, Pediatric Critical Care Dataset. Farris, RW D; Shah, SS; Bennett, TD; Brown, SR; Cornell, TT; Dziorny, AC; Flynn, A; Grunwell, J; Heneghan, JA; Kennedy, CE; Rogerson, C; Tawfik, DS; Wetzel, RC; Sanchez-Pinto, LN. Pediatric Critical Care Medicine. 2025; 26:e941-e951.

Developing a Computable Phenotype for Identifying Children, Adolescents, and Young Adults With Diabetes Using Electronic Health Records in the DiCAYA Network. Shao, H; Thorpe, LE; Islam, S; Bian, J; Guo, Y; Li, P; Bost, S; Dabelea, D; Conway, R; Crume, T; Rolka, D; Imperatore, G; Pavkov, ME; Divers, J. Diabetes Care. 2025; 48:914-921.

Bridging Artificial Intelligence and Medical Education: Navigating the Alignment Paradox. Turner, L; Knopp, MI; Mendonca, EA; Desai, S. ATS Scholar. 2025; 6:135-148.

Area-Level Indices and Health Care Use in a Pediatric Brain and Central Nervous System Tumor Cohort: Observational Study. Tran, YH; Park, S; Coven, SL; Mendonca, EA. JMIR Public Health and Surveillance. 2025; 11:e66834.

Pediatric Long COVID Subphenotypes: An EHR-based study from the RECOVER program. Lorman, V; Bailey, LC; Song, X; Rao, S; Hornig, M; Utidjian, L; Razzaghi, H; Mejias, A; Leikauf, JE; Brill, SB; Cummins, MR; Jhaveri, R; Blecker, S; Forrest, CB. PLOS Digital Health. 2025; 4:e0000747.

Environment scan of generative AI infrastructure for clinical and translational science. Idnay, B; Xu, Z; Adams, WG; Adibuzzaman, M; Anderson, NR; Bahroos, N; Bell, DS; Bumgardner, C; Campion, T; Castro, M; Xu, H; Bian, J; Weng, C; Peng, Y. 2025; 2:4.

1203: A MATCHED ANALYSIS OF THE USE OF HIGH-FLOW NASAL CANNULA FOR PEDIATRIC SEVERE ACUTE ASTHMA. Rogerson, C; Abu-Sultaneh, S; Nelson Sanchez-Pinto, L; Gaston, B; Wiehe, S; Schleyer, T; Tu, W; Mendonca, E. Critical Care Medicine. 2025; 53.

1208: IDENTIFICATION OF SEVERE ACUTE PEDIATRIC ASTHMA PHENOTYPES USING UNSUPERVISED MACHINE LEARNING. Rogerson, C; Nelson Sanchez-Pinto, L; Gaston, B; Wiehe, S; Schleyer, T; Tu, W; Mendonca, E. Critical Care Medicine. 2025; 53.

Associations between patient portal use and electronic health record (EHR) data timeliness in type 2 diabetes mellitus care. Wiley, K; Blackburn, J; Mendonca, E; Menachemi, N; De Groot, M; Vest, JR. Journal of Diabetes and Metabolic Disorders. 2024; 23:2073-2080.

Identification of severe acute pediatric asthma phenotypes using unsupervised machine learning. Rogerson, C; Nelson Sanchez-Pinto, L; Gaston, B; Wiehe, S; Schleyer, T; Tu, W; Mendonca, E. Pediatric Pulmonology. 2024; 59:3313-3321.

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