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.  

Phenotypic overlap between rare disease patients and variant carriers in a large population cohort informs biological mechanisms.
Authors: Fitzsimmons L, Beaulieu-Jones B, Kobren SN.
medRxiv
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Disease progression strikingly differs in research and real-world Parkinson's populations.
Authors: Beaulieu-Jones BK, Frau F, Bozzi S, Chandross KJ, Peterschmitt MJ, Cohen C, Coulovrat C, Kumar D, Kruger MJ, Lipnick SL, Fitzsimmons L, Kohane IS, Scherzer CR.
NPJ Parkinsons Dis
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Disease progression strikingly differs in research and real-world Parkinson's populations.
Authors: Beaulieu-Jones BK, Frau F, Bozzi S, Chandross KJ, Peterschmitt MJ, Cohen C, Coulovrat C, Kumar D, Kruger MJ, Lipnick SL, Fitzsimmons L, Kohane IS, Scherzer CR.
medRxiv
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Emerging therapeutic drug monitoring technologies: considerations and opportunities in precision medicine.
Authors: Liang WS, Beaulieu-Jones B, Smalley S, Snyder M, Goetz LH, Schork NJ.
Front Pharmacol
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Predicting seizure recurrence after an initial seizure-like episode from routine clinical notes using large language models: a retrospective cohort study.
Authors: Beaulieu-Jones BK, Villamar MF, Scordis P, Bartmann AP, Ali W, Wissel BD, Alsentzer E, de Jong J, Patra A, Kohane I.
Lancet Digit Health
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Zero-shot interpretable phenotyping of postpartum hemorrhage using large language models.
Authors: Alsentzer E, Rasmussen MJ, Fontoura R, Cull AL, Beaulieu-Jones B, Gray KJ, Bates DW, Kovacheva VP.
NPJ Digit Med
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Zero-shot Interpretable Phenotyping of Postpartum Hemorrhage Using Large Language Models.
Authors: Alsentzer E, Rasmussen MJ, Fontoura R, Cull AL, Beaulieu-Jones B, Gray KJ, Bates DW, Kovacheva VP.
medRxiv
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Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies.
Authors: Hou J, Zhao R, Gronsbell J, Lin Y, Bonzel CL, Zeng Q, Zhang S, Beaulieu-Jones BK, Weber GM, Jemielita T, Wan SS, Hong C, Cai T, Wen J, Ayakulangara Panickan V, Liaw KL, Liao K, Cai T.
J Med Internet Res
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Developing better digital health measures of Parkinson's disease using free living data and a crowdsourced data analysis challenge.
Authors: Sieberts SK, Borzymowski H, Guan Y, Huang Y, Matzner A, Page A, Bar-Gad I, Beaulieu-Jones B, El-Hanani Y, Goschenhofer J, Javidnia M, Keller MS, Li YC, Saqib M, Smith G, Stanescu A, Venuto CS, Zielinski R, Jayaraman A, Evers LJW, Foschini L, Mariakakis A, Pandey G, Shawen N, Synder P, Omberg L.
PLOS Digit Health
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Multi-center retrospective cohort study applying deep learning to electrocardiograms to identify left heart valvular dysfunction.
Authors: Vaid A, Argulian E, Lerakis S, Beaulieu-Jones BK, Krittanawong C, Klang E, Lampert J, Reddy VY, Narula J, Nadkarni GN, Glicksberg BS.
Commun Med (Lond)
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