Information

Related Research Units

Critical Care Medicine Research
Perioperative Anesthesia Research
Computational Health Informatics Program Research

Research Overview

Dr. Geva conducts research at the intersection of biomedical informatics, health services research, quality improvement, and critical care medicine. Using methods learned during his NRSA fellowship with Dr. Kenneth Mandl, he applies computational methods to the study of health systems and, in particular, to the care of critically ill children. His current projects focus on cohort identification and characterization using electronic health records data, automatic patient registry population using electronic health records data, assessment of medication use and safety, and development of clinical decision support applications for the bedside.

 

Research Background

Dr. Geva is a board-certified in pediatric critical care medicine and practices in the Medical-Surgical and Cardiac Intensive Care Units. In addition to his clinical training at Harvard Medical School and Boston Children's Hospital, he completed a Masters in Public Health with a focus on Quantitative Methods at the Harvard T. H. Chan School of Public Health and a post-doctoral NRSA (T32) fellowship in Biomedical Informatics under the mentorship of Professor Kenneth Mandl.

 

Education

Graduate School

MPH Harvard School of Public Health
2010 Boston MA

Medical School

Harvard Medical School
2010 Boston MA

Residency

Boston Children's Hospital
2013 Boston MA

Fellowship

2016 Boston MA

Publications

  1. Using Implementation Science to Assess Barriers to Agreement on Sedation Goal Setting and Assessment. Pediatr Crit Care Med. 2025 Jan 01; 26(1):e51-e61. View Abstract
  2. Accuracy of ICD-10 Codes for Suicidal Ideation and Action in Pediatric Emergency Department Encounters. Pediatr Emerg Care. 2024 Dec 30. View Abstract
  3. Multisource representation learning for pediatric knowledge extraction from electronic health records. NPJ Digit Med. 2024 Nov 13; 7(1):319. View Abstract
  4. Electrocardiogram-Based Deep Learning to Predict Mortality in Repaired Tetralogy of Fallot. JACC Clin Electrophysiol. 2024 Dec; 10(12):2600-2612. View Abstract
  5. Accuracy of ICD-10 codes for suicidal ideation and action in pediatric emergency department encounters. medRxiv. 2024 Jul 24. View Abstract
  6. Development and Validation of a Mortality Risk Score for Repaired Tetralogy of Fallot. J Am Heart Assoc. 2024 Jun 18; 13(12):e034871. View Abstract
  7. Using the Past to Inform the Future: How a Classic Respiratory Physiology Equation Informs Computer-Based Simulators and Clinical Decision Support Systems. Pediatr Crit Care Med. 2024 May 01; 25(5):466-468. View Abstract
  8. Moving Biosurveillance Beyond Coded Data Using AI for Symptom Detection From Physician Notes: Retrospective Cohort Study. J Med Internet Res. 2024 Apr 04; 26:e53367. View Abstract
  9. Biomarker Assessment of a High-Risk, Data-Driven Pediatric Sepsis Phenotype Characterized by Persistent Hypoxemia, Encephalopathy, and Shock. Pediatr Crit Care Med. 2024 Jun 01; 25(6):512-517. View Abstract
  10. Comparing the Quality of Domain-Specific Versus General Language Models for Artificial Intelligence-Generated Differential Diagnoses in PICU Patients. Pediatr Crit Care Med. 2024 Jun 01; 25(6):e273-e282. View Abstract
  11. Automated Calculator for the Pediatric Sequential Organ Failure Assessment Score: Development and External Validation in a Single-Center 7-Year Cohort, 2015-2021. Pediatr Crit Care Med. 2024 May 01; 25(5):434-442. View Abstract
  12. Dynamic Prediction of Mortality Using Longitudinally Measured Pediatric Sequential Organ Failure Assessment Scores: A Joint Modeling Approach. Pediatr Crit Care Med. 2024 May 01; 25(5):443-451. View Abstract
  13. Emergency department visits and boarding for pediatric patients with suicidality before and during the COVID-19 pandemic. PLoS One. 2023; 18(11):e0286035. View Abstract
  14. Distinct clinical phenotypes in paediatric cancer patients with sepsis are associated with different outcomes-an international multicentre retrospective study. EClinicalMedicine. 2023 Nov; 65:102252. View Abstract
  15. Knowledge-Driven Online Multimodal Automated Phenotyping System. medRxiv. 2023 Oct 02. View Abstract
  16. External validation and biomarker assessment of a high-risk, data-driven pediatric sepsis phenotype characterized by persistent hypoxemia, encephalopathy, and shock. Res Sq. 2023 Aug 02. View Abstract
  17. A computable case definition for patients with SARS-CoV2 testing that occurred outside the hospital. JAMIA Open. 2023 Oct; 6(3):ooad047. View Abstract
  18. Derivation, Validation, and Clinical Relevance of a Pediatric Sepsis Phenotype With Persistent Hypoxemia, Encephalopathy, and Shock. Pediatr Crit Care Med. 2023 Oct 01; 24(10):795-806. View Abstract
  19. Heart Rate Change as a Potential Digital Biomarker of Brain Death in Critically Ill Children With Acute Catastrophic Brain Injury. Crit Care Explor. 2023 May; 5(5):e0908. View Abstract
  20. Validation and Extension of the Association Between Potentially Excess Oxygen Exposure and Death in Mechanically Ventilated Children. Pediatr Crit Care Med. 2023 09 01; 24(9):e434-e440. View Abstract
  21. Examining the Evidence for Escalating Antimicrobial Regimens in Febrile Oncology and Hematopoietic Stem Cell Transplant Patients Admitted to the PICU: An Observational Study. Pediatr Crit Care Med. 2023 06 01; 24(6):e292-e296. View Abstract
  22. A computable phenotype for patients with SARS-CoV2 testing that occurred outside the hospital. medRxiv. 2023 Jan 19. View Abstract
  23. Weakly Semi-supervised phenotyping using Electronic Health records. J Biomed Inform. 2022 10; 134:104175. View Abstract
  24. Implementation Science in Pediatric Critical Care - Sedation and Analgesia Practices as a Case Study. Front Pediatr. 2022; 10:864029. View Abstract
  25. Scalable relevance ranking algorithm via semantic similarity assessment improves efficiency of medical chart review. J Biomed Inform. 2022 08; 132:104109. View Abstract
  26. Supraventricular Tachycardia in Infants With Congenital Diaphragmatic Hernia: Prevalence, Associations, and Outcomes. Pediatr Crit Care Med. 2022 07 01; 23(7):e329-e337. View Abstract
  27. Clinical Characteristics and Outcomes of Children with Acute Catastrophic Brain Injury: A 13-Year Retrospective Cohort Study. Neurocrit Care. 2022 06; 36(3):715-726. View Abstract
  28. Implementation of an Analgesia-Sedation Protocol Is Associated With Reduction in Midazolam Usage in the PICU. Pediatr Crit Care Med. 2021 10 01; 22(10):e513-e523. View Abstract
  29. eSIMPLER: A Dynamic, Electronic Health Record-Integrated Checklist for Clinical Decision Support During PICU Daily Rounds. Pediatr Crit Care Med. 2021 10 01; 22(10):898-905. View Abstract
  30. Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents. EClinicalMedicine. 2021 Oct; 40:101112. View Abstract
  31. Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data. J Am Med Inform Assoc. 2021 07 14; 28(7):1411-1420. View Abstract
  32. A high-throughput phenotyping algorithm is portable from adult to pediatric populations. J Am Med Inform Assoc. 2021 06 12; 28(6):1265-1269. View Abstract
  33. Coagulations Studies Do Not Correlate With Each Other or With Hematologic Complications During Pediatric Extracorporeal Membrane Oxygenation. Pediatr Crit Care Med. 2021 06 01; 22(6):542-552. View Abstract
  34. International Analysis of Electronic Health Records of Children and Youth Hospitalized With COVID-19 Infection in 6 Countries. JAMA Netw Open. 2021 06 01; 4(6):e2112596. View Abstract
  35. A Machine Learning Classifier Improves Mortality Prediction Compared With Pediatric Logistic Organ Dysfunction-2 Score: Model Development and Validation. Crit Care Explor. 2021 May; 3(5):e0426. View Abstract
  36. Adding Continuous Vital Sign Information to Static Clinical Data Improves the Prediction of Length of Stay After Intubation: A Data-Driven Machine Learning Approach. Respir Care. 2020 Sep; 65(9):1367-1377. View Abstract
  37. Adverse drug event presentation and tracking (ADEPT): semiautomated, high throughput pharmacovigilance using real-world data. JAMIA Open. 2020 Oct; 3(3):413-421. View Abstract
  38. Adverse drug event rates in pediatric pulmonary hypertension: a comparison of real-world data sources. J Am Med Inform Assoc. 2020 02 01; 27(2):294-300. View Abstract
  39. Noninvasive Ventilation Is Interrupted Frequently and Mostly Used at Night in the Pediatric Intensive Care Unit. Respir Care. 2020 Mar; 65(3):341-346. View Abstract
  40. Extracting Adverse Drug Event Information with Minimal Engineering. Proc Conf. 2019 Jun; 2019:22-27. View Abstract
  41. Feature extraction for phenotyping from semantic and knowledge resources. J Biomed Inform. 2019 03; 91:103122. View Abstract
  42. Provider Connectedness to Other Providers Reduces Risk of Readmission After Hospitalization for Heart Failure. Med Care Res Rev. 2019 02; 76(1):115-128. View Abstract
  43. Simulation of a Novel Schedule for Intensivist Staffing to Improve Continuity of Patient Care and Reduce Physician Burnout. Crit Care Med. 2017 Jul; 45(7):1138-1144. View Abstract
  44. A Computable Phenotype Improves Cohort Ascertainment in a Pediatric Pulmonary Hypertension Registry. J Pediatr. 2017 09; 188:224-231.e5. View Abstract
  45. Learning a Comorbidity-Driven Taxonomy of Pediatric Pulmonary Hypertension. Circ Res. 2017 Aug 04; 121(4):341-353. View Abstract
  46. Therapeutic Hypothermia in Children. N Engl J Med. 2015 09 03; 373(10):979. View Abstract
  47. Long-term outcomes and risk factors for aortic regurgitation after discrete subvalvular aortic stenosis resection in children. Heart. 2015 Oct; 101(19):1547-53. View Abstract
  48. A novel electroencephalography-based tool for objective assessment of network dynamics activated by nociceptive stimuli. Eur J Pain. 2016 Feb; 20(2):250-62. View Abstract
  49. Spread of methicillin-resistant Staphylococcus aureus in a large tertiary NICU: network analysis. Pediatrics. 2011 Nov; 128(5):e1173-80. View Abstract
  50. A quantitative analysis of optimal treatment capacity for perinatal asphyxia. Med Decis Making. 2012 Mar-Apr; 32(2):266-72. View Abstract
  51. Dexamethasone and tonsillectomy bleeding: a meta-analysis. Otolaryngol Head Neck Surg. 2011 Jun; 144(6):838-43. View Abstract
  52. Network analysis of team structure in the neonatal intensive care unit. Pediatrics. 2010 Jun; 125(6):e1460-7. View Abstract
  53. CoolSim: using industrial modeling techniques to examine the impact of selective head cooling in a model of perinatal regionalization. Pediatrics. 2008 Jan; 121(1):28-36. View Abstract
  54. Risk factors for reoperation after repair of discrete subaortic stenosis in children. J Am Coll Cardiol. 2007 Oct 09; 50(15):1498-504. View Abstract
  55. Hemoglobin Jamaica plain--a sickling hemoglobin with reduced oxygen affinity. N Engl J Med. 2004 Oct 07; 351(15):1532-8. View Abstract
  56. Hemolytic anemia and severe rhabdomyolysis caused by compound heterozygous mutations of the gene for erythrocyte/muscle isozyme of aldolase, ALDOA(Arg303X/Cys338Tyr). Blood. 2004 Mar 15; 103(6):2401-3. View Abstract
  57. Chronic disseminated intravascular coagulation and childhood-onset skin necrosis resulting from homozygosity for a protein C Gla domain mutation, Arg15Trp. J Pediatr Hematol Oncol. 2002 Nov; 24(8):685-8. View Abstract

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