Lucila Ohno-Machado, MD, MBA, PhD
Waldemar von Zedtwitz Professor of Medicine and Biomedical Informatics and Data Science; Deputy Dean for Biomedical Informatics; Chair, Department of Biomedical Informatics and Data ScienceCards
Appointments
About
Titles
Waldemar von Zedtwitz Professor of Medicine and Biomedical Informatics and Data Science; Deputy Dean for Biomedical Informatics; Chair, Department of Biomedical Informatics and Data Science
Biography
Lucila Ohno-Machado, MD, PhD, MBA, is the Deputy Dean for Biomedical Informatics and the Chair of Biomedical Informatics and Data Science. As Deputy Dean for Biomedical Informatics, Ohno-Machado oversees the infrastructure related to biomedical informatics research across the academic health system.
Biomedical Informatics and Data Science serves as the hub for biomedical collaboration at Yale. It brings informatics to the clinic and the bedside; innovates new approaches to the analysis of big data across the biomedical research spectrum from basic genetic, proteomic, cellular, and systems biology to the understanding of the social determinants of health; and works in concert with colleagues in data science.
Ohno-Machado was health sciences associate dean for informatics and technology, founding chief of the Division of Biomedical Informatics in the Department of Medicine, and distinguished professor of medicine at the University of California San Diego (UCSD). She also was founding chair of the UCSD Health Department of Biomedical Informatics and founding faculty of the UCSD Halicioğlu Data Science Institute in La Jolla, California. She received her medical degree from the University of São Paulo, Brazil; her MBA from the Escola de Administração de São Paulo, Fundação Getúlio Vargas, Brazil; and her PhD in medical information sciences and computer science at Stanford University. She has led informatics centers that were funded by various NIH initiatives and by agencies such as AHRQ, PCORI, and NSF.
She organized the first large-scale initiative to share clinical data across five UC medical systems and later extended it to various institutions in California and around the country. Prior to joining UCSD, she was distinguished chair in biomedical informatics at Brigham and Women’s Hospital, and faculty at Harvard Medical School and at MIT’s Health Sciences and Technology Division. She is an elected member of the National Academy of Medicine, the American Society for Clinical Investigation, the American Institute for Medical and Biological Engineering, the American College of Medical Informatics, and the International Academy of Health Sciences Informatics. She is a recipient of the American Medical Informatics Association leadership award, as well as the William W. Stead Award for Thought Leadership in Informatics.
Long fascinated by the combination of life science and computer science, Ohno-Machado has conducted research in predictive models and data sharing since the start of her career. Her doctoral thesis work involved neural network models for survival analysis, and she subsequently focused on new methods to evaluate predictive performance of models based on clinical and molecular data. Since AI models require large amounts of data, and institutions prefer to keep the data locally, she worked on innovative algorithms to distribute the computation so that data could stay local, but multivariate models could be built and evaluated in a federated manner.
Appointments
Biomedical Informatics & Data Science
ChairDualOffice of the Dean, School of Medicine
Deputy DeanDualBiomedical Informatics & Data Science
ProfessorPrimary
Other Departments & Organizations
Education & Training
- Non Degree Program
- Brigham and Women’s Hospital/Harvard Business School Leadership Program
- PhD
- Stanford University, Medical Information Sciences and Computer Science
- Research Fellow in Medicine
- Stanford University Medical Center
- MBA
- Escola de Administração de São Paulo
- Resident
- University of São Paulo
- MD
- University of São Paulo
- Intern
- University of São Paulo
Research
Overview
Medical Research Interests
ORCID
0000-0002-8005-7327
Research at a Glance
Yale Co-Authors
Publications Timeline
Research Interests
Jihoon Kim, PhD
Tsung-Ting Kuo, PhD
Hua Xu, PhD
Daniella Meeker, PhD
Cyprien Rivier, MD, MSc
Guido J. Falcone, MD, ScD, MPH
Machine Learning
Data Science
Publications
2025
Differential results of genetic risk scoring for multiple sclerosis in European and African American populations
Rivier C, Xu L, Clocchiatti-Tuozzo S, Zhao H, Ohno-Machado L, Hafler D, Falcone G, Longbrake E. Differential results of genetic risk scoring for multiple sclerosis in European and African American populations. Multiple Sclerosis Journal 2025, 31: 1304-1313. PMID: 40991630, DOI: 10.1177/13524585251377607.Peer-Reviewed Original ResearchAltmetricMeSH Keywords and ConceptsConceptsGenetic risk scoreInternational ClassificationSystematized Nomenclature of MedicineAfrican ancestryRisk scoreCross-sectional studyHigh-risk individualsMultiple sclerosisSystematized NomenclatureAfrican American populationAfrican ancestry groupMS prevalenceAncestry groupsMS riskNomenclature of MedicineAmerican populationParticipantsAfrican participantsAncestryEuropean populationsClinical trialsDifferentiation resultsScoresIndividualsPopulationPolygenic Resistance to Blood Pressure Treatment and Stroke Risk: Insights from the All of Us Research Program
Huo S, Rivier C, Clocchiatti‐Tuozzo S, Renedo D, Petersen N, de Havenon A, Meeker D, Zhao H, Ohno‐Machado L, Sheth K, Falcone G. Polygenic Resistance to Blood Pressure Treatment and Stroke Risk: Insights from the All of Us Research Program. Annals Of Neurology 2025 PMID: 40827941, DOI: 10.1002/ana.78009.Peer-Reviewed Original ResearchCitationsAltmetricConceptsUs Research ProgramUncontrolled hypertensionSystolic BPGenetic riskSusceptibility to hypertensionRates of uncontrolled hypertensionPolygenic risk scoresAssociated with higher systolic BPOdds of uncontrolled hypertensionCox proportional hazards regressionUnited Kingdom BiobankGenetic association studiesHigh-risk individualsHigher systolic BPProportional hazards regressionBlood pressure treatmentIncident strokeStroke hazardBlood pressureHigher RatesSystolic blood pressureHypertensive adultsDiagnosed hypertensionBP medicationsHazards regressionA Statistical Framework to Detect and Quantify Operator-Learning Curves in Medical Device Safety Evaluation
Ssemaganda H, Davis S, Govindarajulu U, Koola J, Mao J, Westerman D, Perkins A, Speroff T, Ramsay C, Sedrakyan A, Ohno-Machado L, Matheny M, Resnic F. A Statistical Framework to Detect and Quantify Operator-Learning Curves in Medical Device Safety Evaluation. Medical Devices Evidence And Research 2025, 18: 361-375. PMID: 40626234, PMCID: PMC12230321, DOI: 10.2147/mder.s520191.Peer-Reviewed Original ResearchConceptsDepartment of Veterans Affairs facilitiesVeterans Affairs facilitiesUS DepartmentPatient harmPatient safetyLikelihood ratioHigh-risk medical devicesLearning effectAnalysis teamGeneralized additive modelDevice effectsMedical devicesPresence of LESafety of medical devicesAdditive modelPatientsSafety issuesGoodness-of-fitSignificant costMedicationHarmTeamSafety signalsClinical distributionIQRSuccess and Challenges Querying OMOP-transformed EHR Data from Different Healthcare Organizations
Purmal C, Matheny M, Ohno-Machado L, Tarasovsky G, Larsen R, Whooley M. Success and Challenges Querying OMOP-transformed EHR Data from Different Healthcare Organizations. ACI Open 2025, 09: e42-e53. DOI: 10.1055/a-2668-3461.Peer-Reviewed Original ResearchConceptsHealth information exchangeStructured query languageHealthcare organizationsInformation exchangeStructured Query Language queryVA healthcare systemEHR-dataQuery languageCode vocabularyEHR dataPatient identifiersHealthcare systemHealthcare informationData modelInteroperabilitySource dataHealthQuerySan FranciscoDataUniversity of CaliforniaSemanticsPatientsHealthcareSyntaxPolygenic Risk Factor Profiling and Risk of Intracerebral Hemorrhage In Patients with Atrial Fibrillation on Apixaban (P9-13.008)
Clocchiatti-Tuozzo S, Rivier C, Huo S, Gilmore E, Shoamanesh A, Kamel H, Murthy S, Ohno-Machado L, Sheth K, Gill T, Falcone G. Polygenic Risk Factor Profiling and Risk of Intracerebral Hemorrhage In Patients with Atrial Fibrillation on Apixaban (P9-13.008). Neurology 2025, 104 DOI: 10.1212/wnl.0000000000212525.Peer-Reviewed Original ResearchMedical foundation large language models for comprehensive text analysis and beyond
Xie Q, Chen Q, Chen A, Peng C, Hu Y, Lin F, Peng X, Huang J, Zhang J, Keloth V, Zhou X, Qian L, He H, Shung D, Ohno-Machado L, Wu Y, Xu H, Bian J. Medical foundation large language models for comprehensive text analysis and beyond. Npj Digital Medicine 2025, 8: 141. PMID: 40044845, PMCID: PMC11882967, DOI: 10.1038/s41746-025-01533-1.Peer-Reviewed Original ResearchCitationsAltmetricConceptsText analysis tasksAnalysis tasksLanguage modelDomain-specific knowledgeZero-ShotHuman evaluationSupervised settingTask-specific instructionsClinical data sourcesSpecialized medical knowledgeChatGPTText analysisPretrainingTaskData sourcesMedical applicationsMedical knowledgeEnhanced performanceTextPerformanceDistributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals
Kuo T, Gabriel R, Koola J, Schooley R, Ohno-Machado L. Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals. Nature Communications 2025, 16: 1371. PMID: 39910076, PMCID: PMC11799213, DOI: 10.1038/s41467-025-56510-9.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsHeart disease dataParts of informationLearning counterpartsCentralized solutionVertical scenariosPatient privacyPredictive analyticsFederated modelSynchronization timePrivacyUC San DiegoPatient-level recordsDisease dataPatient dataPrediction modelPatient careHealthcare centersUniversity of CaliforniaCalifornia hospitalsHealthcare systemQuality improvementPatient recordsPolygenic Susceptibility to Diabetes and Poor Glycemic Control in Stroke Survivors
Demarais Z, Conlon C, Rivier C, Clocchiatti-Tuozzo S, Renedo D, Torres-Lopez V, Sheth K, Meeker D, Zhao H, Ohno-Machado L, Acosta J, Huo S, Falcone G. Polygenic Susceptibility to Diabetes and Poor Glycemic Control in Stroke Survivors. Neurology 2025, 104: e210276. PMID: 39889253, DOI: 10.1212/wnl.0000000000210276.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsStroke survivorsWorse glycemic controlPoor glycemic controlStroke patientsAssociated with worse glycemic controlGlycemic controlPolygenic risk scoresClinical management of stroke patientsAssociated with poor glycemic controlManagement of stroke patientsCross-sectional designGenetic association studiesUncontrolled diabetesSusceptibility to T2DMUK BiobankType 2 diabetes mellitusAdverse vascular outcomesRisk scoreAssociation studiesHemoglobin A1cSurvivorsVascular outcomesSusceptibility to diabetesStrokeDiabetes
2024
Predicting positive Clostridioides difficile test results using large-scale longitudinal data of demographics and medication history
Pham A, El-Kareh R, Myers F, Ohno-Machado L, Kuo T. Predicting positive Clostridioides difficile test results using large-scale longitudinal data of demographics and medication history. Heliyon 2024, 11: e41350. PMID: 39958729, PMCID: PMC11825254, DOI: 10.1016/j.heliyon.2024.e41350.Peer-Reviewed Original ResearchAltmetricConceptsArea under the receiver operating characteristic curveMedical historyClostridioides difficile infectionMonths of medical historyPatient's chancesData of demographicsReceiver operating characteristic curveLogistic regression modelsHealth patientsModerate sample sizesPregnant womenHealthcare institutionsClostridioides difficileAntibiotic useOdds ratioNegative casesLarge-scale longitudinal dataFinancial incentivesPositive testLogistic regressionPatientsIncreased susceptibilityCharacteristic curveRegression modelsSignificant covariatesPatient-Centered and Practical Privacy to Support AI for Healthcare
Liu R, Lee H, Bhavani S, Jiang X, Ohno-Machado L, Xiong L. Patient-Centered and Practical Privacy to Support AI for Healthcare. 2024, 00: 265-272. DOI: 10.1109/tps-isa62245.2024.00038.Peer-Reviewed Original ResearchCitationsConceptsDifferential privacyArtificial intelligenceState-of-the-art approachesPrivacy-sensitive domainsState-of-the-artSensitive patient informationIntegration of artificial intelligenceClinical decision supportPrivacy requirementsPrivacy guaranteesPrivacy solutionsPractical privacyPotential research directionsPrivacy concernsVision paperPrivacy needsAI systemsAI modelsPrivacyDecision supportResearch directionsPatient informationTrade-OffsModel's utilityPrediction model
Academic Achievements & Community Involvement
Honors
honor Inaugural Helen M. Ranney Award
04/05/2024National AwardAssociation of American PhysiciansDetailsUnited Stateshonor Elected Member
03/08/2024National AwardAssociation of American PhysiciansDetailsUnited Stateshonor Distinguished Fellow
11/12/2023National AwardAmerican College of Medical InformaticsDetailsUnited Stateshonor William W. Stead Award for Thought Leadership in Informatics
01/01/2019National AwardAmerican Medical Informatics AssociationDetailsUnited Stateshonor IT/NIST Blockchain Challenge Award
01/01/2016National AwardOffice of the National Coordinator for HealthcareDetailsUnited States
News & Links
News
- October 15, 2025
Program Spotlight: New Human Genome Sciences PhD Track
- September 17, 2025Source: NIH Reporter
Yale BIDS Awarded $2.7 Million NIH Grant to Develop AI Explainability Tools for Clinical Decision-Making
- September 16, 2025Source: NIH
Yale Team Recognized in NIH $1 Million Data Sharing Challenge
- May 14, 2025Source: Yale Medicine Magazine
Chatbot Revolution: From Me-LLaMA to GutGPT, YSM researchers leverage LLMs
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