Brett K. Beaulieu-Jones, PhD

Brett Beaulieu-Jones, PhD

Former Instructor in Biomedical Informatics
Former Research Fellow in Neurology, Brigham and Women's Hospital
Biomedical Informatics Research Training Fellow, 2017-2019

Brett Beaulieu-Jones received his PhD from the Perelman School of Medicine at the University of Pennsylvania under the supervision of Dr. Jason Moore and Dr. Casey Greene. Beaulieu-Jones’ doctoral research focused on using machine learning-based methods to more precisely define phenotypes from large-scale biomedical data repositories, e.g. those contained in clinical records. At DBMI he is expanding this concentration to include large-scale data integration (genomic, therapeutic, imaging) to both better understand disease etiology as well as provide precise therapeutic recommendations. Initially, he is working to develop targeted models of drug selection for patients with refractory epilepsy and to further develop machine learning methods that model the way patients progress over time using longitudinal data.  

Heterogenous effect of automated alerts on mortality.
Authors: Wissel BD, Percy Z, Zachem TJ, Beaulieu-Jones B, Kohane IS, Goldstein SL, Gecili E, Dexheimer JW.
J Am Med Inform Assoc
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Advancing healthcare AI governance through a comprehensive maturity model based on systematic review.
Authors: Hussein R, Zink A, Ramadan B, Howard FM, Hightower M, Shah S, Beaulieu-Jones BK.
NPJ Digit Med
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Predicting Resection Weights of Reduction Mammaplasty: A Multi-Institutional Retrospective Analysis Using Machine Learning.
Authors: Clegg DJ, Boukovalas S, Beaulieu-Jones B, Onar GS, Hendizadeh AN, Brondeel KC, Seu MY, Khoo K, Phillips LG, Kokosis G.
Plast Reconstr Surg
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Predicting Postpartum Hemorrhage Using Clinical Features Extracted With Large Language Models.
Authors: Woo EG, Zighelboim I, Gifford T, Bell JG, Milthorpe H, Alsentzer E, Longman RE, Tolosa JE, Beaulieu-Jones BK.
O G Open
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Computational challenges arising in algorithmic fairness and health equity with generative AI.
Authors: Suriyakumar VM, Zink A, Hightower M, Ghassemi M, Beaulieu-Jones B.
Nat Comput Sci
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Heterogeneous Effect of Automated Alerts on Mortality.
Authors: Wissel BD, Percy Z, Zachem TJ, Beaulieu-Jones B, Kohane IS, Goldstein SL, Gecili E, Dexheimer JW.
medRxiv
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ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning.
Authors: Sethi S, Chen D, Statchen T, Burkhart MC, Bhandari N, Ramadan B, Beaulieu-Jones B.
ArXiv
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ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning.
Authors: Sethi S, Chen D, Statchen T, Burkhart MC, Bhandari N, Ramadan B, Beaulieu-Jones B.
Proc Mach Learn Res
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Synthetic data distillation enables the extraction of clinical information at scale.
Authors: Woo EG, Burkhart MC, Alsentzer E, Beaulieu-Jones BK.
NPJ Digit Med
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The MI-CLAIM-GEN checklist for generative artificial intelligence in health.
Authors: Miao BY, Chen IY, Williams CYK, Davidson J, Garcia-Agundez A, Sun S, Zack T, Saria S, Arnaout R, Quer G, Sadaei HJ, Torkamani A, Beaulieu-Jones B, Yu B, Gianfrancesco M, Butte AJ, Norgeot B, Sushil M.
Nat Med
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