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
Administrative Support
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
Rohan Khera, MD, MS
Andreas Coppi
Machine Learning
Data Science
Publications
2024
A machine learning framework to adjust for learning effects in medical device safety evaluation
Koola J, Ramesh K, Mao J, Ahn M, Davis S, Govindarajulu U, Perkins A, Westerman D, Ssemaganda H, Speroff T, Ohno-Machado L, Ramsay C, Sedrakyan A, Resnic F, Matheny M. A machine learning framework to adjust for learning effects in medical device safety evaluation. Journal Of The American Medical Informatics Association 2024, ocae273. PMID: 39471493, DOI: 10.1093/jamia/ocae273.Peer-Reviewed Original ResearchConceptsMachine learning frameworkSynthetic datasetsLearning frameworkMachine learningCapacity of MLLearning effectFeature correlationDepartment of Veterans AffairsSynthetic dataData generationAbsence of learning effectsTraditional statistical methodsML methodsSuperior performanceDatasetSafety signal detectionSignal detectionDevice signalsVeterans AffairsTime-varying covariatesLearningMachinePhysician experienceLimitations of traditional statistical methodsMedical device post-market surveillanceSecure Federated Learning Integrated Statistical Modeling for Healthcare Data
Jiang X, Kim J, Kuo T, Ohno-Machado L. Secure Federated Learning Integrated Statistical Modeling for Healthcare Data. 2024, 313-324. DOI: 10.1201/9781003185284-24.Peer-Reviewed Original ResearchA primer for quantum computing and its applications to healthcare and biomedical research
Durant T, Knight E, Nelson B, Dudgeon S, Lee S, Walliman D, Young H, Ohno-Machado L, Schulz W. A primer for quantum computing and its applications to healthcare and biomedical research. Journal Of The American Medical Informatics Association 2024, 31: 1774-1784. PMID: 38934288, PMCID: PMC11258415, DOI: 10.1093/jamia/ocae149.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsAltmetricBiomedical blockchain with practical implementations and quantitative evaluations: a systematic review
Lacson R, Yu Y, Kuo T, Ohno-Machado L. Biomedical blockchain with practical implementations and quantitative evaluations: a systematic review. Journal Of The American Medical Informatics Association 2024, 31: 1423-1435. PMID: 38726710, PMCID: PMC11105130, DOI: 10.1093/jamia/ocae084.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsElectronic health recordsSystematic reviewData sharingMedical data sharingSpeed metricsPreferred Reporting ItemsCertificate storageDecentralized InternetNetwork permissionsBlockchain platformBlockchain applicationsEvaluation metricsBiomedical domainBlockchainBiomedical data managementHealth recordsData managementData storageReporting ItemsHealth sectorQuantitative metricsMedical facilitiesMetricsTrial managementClinical trial management
2023
JAMIA at 30: looking back and forward
Stead W, Miller R, Ohno-Machado L, Bakken S. JAMIA at 30: looking back and forward. Journal Of The American Medical Informatics Association 2023, 31: 1-9. PMID: 38134400, PMCID: PMC10746314, DOI: 10.1093/jamia/ocad215.Peer-Reviewed Original ResearchCitationsAltmetricGuiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care
Chin M, Afsar-Manesh N, Bierman A, Chang C, Colón-Rodríguez C, Dullabh P, Duran D, Fair M, Hernandez-Boussard T, Hightower M, Jain A, Jordan W, Konya S, Moore R, Moore T, Rodriguez R, Shaheen G, Snyder L, Srinivasan M, Umscheid C, Ohno-Machado L. Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care. JAMA Network Open 2023, 6: e2345050. PMID: 38100101, PMCID: PMC11181958, DOI: 10.1001/jamanetworkopen.2023.45050.Peer-Reviewed Original ResearchCitationsAltmetricSevere aortic stenosis detection by deep learning applied to echocardiography
Holste G, Oikonomou E, Mortazavi B, Coppi A, Faridi K, Miller E, Forrest J, McNamara R, Ohno-Machado L, Yuan N, Gupta A, Ouyang D, Krumholz H, Wang Z, Khera R. Severe aortic stenosis detection by deep learning applied to echocardiography. European Heart Journal 2023, 44: 4592-4604. PMID: 37611002, PMCID: PMC11004929, DOI: 10.1093/eurheartj/ehad456.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsExamining sociodemographic correlates of opioid use, misuse, and use disorders in the All of Us Research Program.
Yeh H, Peltz-Rauchman C, Johnson C, Pawloski P, Chesla D, Waring S, Stevens A, Epstein M, Joseph C, Miller-Matero L, Gui H, Tang A, Boerwinkle E, Cicek M, Clark C, Cohn E, Gebo K, Loperena R, Mayo K, Mockrin S, Ohno-Machado L, Schully S, Ramirez A, Qian J, Ahmedani B. Examining sociodemographic correlates of opioid use, misuse, and use disorders in the All of Us Research Program. PLOS ONE 2023, 18: e0290416. PMID: 37594966, PMCID: PMC10437856, DOI: 10.1371/journal.pone.0290416.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsOpioid use disorderOpioid usePrescription opioidsElectronic health recordsReduced oddsDiagnosis of OUDSociodemographic characteristicsPrevalence of OUDNonmedical useLifetime opioid useEHR dataNon-Hispanic white participantsImportant clinical informationNon-medical useLifetime prevalenceStreet opioidsHigher oddsOpioidsClinical informationUse disordersUs Research ProgramSociodemographic correlatesLogistic regressionPrevalenceHealth recordsFamily and personal history of cancer in the All of Us research program for precision medicine
Bruce L, Paul P, Kim K, Kim J, Keegan T, Hiatt R, Ohno-Machado L, Investigators O. Family and personal history of cancer in the All of Us research program for precision medicine. PLOS ONE 2023, 18: e0288496. PMID: 37459328, PMCID: PMC10351738, DOI: 10.1371/journal.pone.0288496.Peer-Reviewed Original ResearchAltmetricMeSH Keywords and ConceptsPatient and researcher stakeholder preferences for use of electronic health record data: a qualitative study to guide the design and development of a platform to honor patient preferences.
Morse B, Kim K, Xu Z, Matsumoto C, Schilling L, Ohno-Machado L, Mak S, Keller M. Patient and researcher stakeholder preferences for use of electronic health record data: a qualitative study to guide the design and development of a platform to honor patient preferences. Journal Of The American Medical Informatics Association 2023, 30: 1137-1149. PMID: 37141581, PMCID: PMC10198527, DOI: 10.1093/jamia/ocad058.Peer-Reviewed Original ResearchCitationsAltmetric
Academic Achievements & Community Involvement
honor Inaugural Helen M. Ranney Award
National AwardAssociation of American PhysiciansDetails04/05/2024United Stateshonor Elected Member
National AwardAssociation of American PhysiciansDetails03/08/2024United Stateshonor Distinguished Fellow
National AwardAmerican College of Medical InformaticsDetails11/12/2023United Stateshonor William W. Stead Award for Thought Leadership in Informatics
National AwardAmerican Medical Informatics AssociationDetails01/01/2019United Stateshonor IT/NIST Blockchain Challenge Award
National AwardOffice of the National Coordinator for HealthcareDetails01/01/2016United States
News & Links
News
- November 19, 2024Source: WFSB
AI Breakthrough in Healthcare to Revolutionize Cardiology and More
- September 27, 2024
Biomedical Informatics and Data Science (BIDS) Secures a $7.88 Million NIH Grant to Advance Mental Health Research Using AI Technology
- September 11, 2024
Yale Cancer Center Team Receives Yosemite—American Cancer Society Award
- June 18, 2024
Blockchain Trainee Headed to Med School
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