Beau Norgeot

1.3k total citations
17 papers, 762 citations indexed

About

Beau Norgeot is a scholar working on Artificial Intelligence, Statistics and Probability and Health Information Management. According to data from OpenAlex, Beau Norgeot has authored 17 papers receiving a total of 762 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 6 papers in Statistics and Probability and 3 papers in Health Information Management. Recurrent topics in Beau Norgeot's work include Machine Learning in Healthcare (10 papers), Advanced Causal Inference Techniques (5 papers) and Health Systems, Economic Evaluations, Quality of Life (3 papers). Beau Norgeot is often cited by papers focused on Machine Learning in Healthcare (10 papers), Advanced Causal Inference Techniques (5 papers) and Health Systems, Economic Evaluations, Quality of Life (3 papers). Beau Norgeot collaborates with scholars based in United States and United Kingdom. Beau Norgeot's co-authors include Atul J. Butte, Benjamin S. Glicksberg, Milena Gianfrancesco, Bin Yu, Raquel Dias, Suchi Saria, Ali Torkamani, Brett K. Beaulieu‐Jones, Giorgio Quer and Isaac S. Kohane and has published in prestigious journals such as Nature Medicine, Nature Communications and Journal of Medical Internet Research.

In The Last Decade

Beau Norgeot

16 papers receiving 746 citations

Peers

Beau Norgeot
Mona G. Flores United States
Selen Bozkurt Türkiye
Luke Gompels United Kingdom
Shinjini Kundu United States
Milap Shah India
Nhan Do United States
Seth A. Gross United States
Irene Y. Chen United States
Aditya U. Kale United Kingdom
Vyas Kumar Rathaur India
Mona G. Flores United States
Beau Norgeot
Citations per year, relative to Beau Norgeot Beau Norgeot (= 1×) peers Mona G. Flores

Countries citing papers authored by Beau Norgeot

Since Specialization
Citations

This map shows the geographic impact of Beau Norgeot's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Beau Norgeot with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Beau Norgeot more than expected).

Fields of papers citing papers by Beau Norgeot

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Beau Norgeot. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Beau Norgeot. The network helps show where Beau Norgeot may publish in the future.

Co-authorship network of co-authors of Beau Norgeot

This figure shows the co-authorship network connecting the top 25 collaborators of Beau Norgeot. A scholar is included among the top collaborators of Beau Norgeot based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Beau Norgeot. Beau Norgeot is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
1.
Miao, Brenda Y., Irene Y. Chen, Christopher Y. K. Williams, et al.. (2025). The MI-CLAIM-GEN checklist for generative artificial intelligence in health. Nature Medicine. 31(5). 1394–1398. 7 indexed citations
2.
Miotto, Riccardo, Eyal Klang, Anthony Costa, et al.. (2025). Embedding Methods for Electronic Health Record Research. PubMed. 8(1). 563–590.
3.
Tang, Alice, et al.. (2024). Harnessing EHR data for health research. Nature Medicine. 30(7). 1847–1855. 16 indexed citations
4.
Norgeot, Beau, et al.. (2023). Learning end-to-end patient representations through self-supervised covariate balancing for causal treatment effect estimation. Journal of Biomedical Informatics. 140. 104339–104339. 4 indexed citations
5.
Gutfraind, Alexander, et al.. (2023). Victims of human trafficking and exploitation in the healthcare system: a retrospective study using a large multi-state dataset and ICD-10 codes. Frontiers in Public Health. 11. 1243413–1243413. 3 indexed citations
6.
Wang, Dong, et al.. (2022). Generating high-fidelity privacy-conscious synthetic patient data for causal effect estimation with multiple treatments. Frontiers in Artificial Intelligence. 5. 918813–918813. 13 indexed citations
7.
Belthangady, Chinmay, et al.. (2022). Causal deep learning reveals the comparative effectiveness of antihyperglycemic treatments in poorly controlled diabetes. Nature Communications. 13(1). 6921–6921. 8 indexed citations
8.
Norgeot, Beau, et al.. (2022). Learning Causal Effects From Observational Data in Healthcare: A Review and Summary. Frontiers in Medicine. 9. 864882–864882. 13 indexed citations
9.
10.
Belthangady, Chinmay, et al.. (2021). Minimizing bias in massive multi-arm observational studies with BCAUS: balancing covariates automatically using supervision. BMC Medical Research Methodology. 21(1). 190–190. 10 indexed citations
11.
Goldstein, Theodore C., et al.. (2021). MAgEC: Using Non-Homogeneous Ensemble Consensus for Predicting Drivers in Unexpected Mechanical Ventilation.. PubMed. 2021. 238–247. 1 indexed citations
12.
Norgeot, Beau, Benjamin S. Glicksberg, Boris Oskotsky, et al.. (2020). Protected Health Information filter (Philter): accurately and securely de-identifying free-text clinical notes. npj Digital Medicine. 3(1). 57–57. 61 indexed citations
13.
Norgeot, Beau, Giorgio Quer, Brett K. Beaulieu‐Jones, et al.. (2020). Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nature Medicine. 26(9). 1320–1324. 322 indexed citations
14.
Norgeot, Beau. (2019). DEEP LEARNING IN PERSONALIZED MEDICINE: APPLICATIONS IN PATIENT SIMILARITY, PROGNOSIS, AND OPTIMAL TREATMENT SELECTION. eScholarship (California Digital Library). 1 indexed citations
15.
Norgeot, Beau, Benjamin S. Glicksberg, Laura Trupin, et al.. (2019). Assessment of a Deep Learning Model Based on Electronic Health Record Data to Forecast Clinical Outcomes in Patients With Rheumatoid Arthritis. JAMA Network Open. 2(3). e190606–e190606. 145 indexed citations
16.
Norgeot, Beau, Benjamin S. Glicksberg, & Atul J. Butte. (2018). A call for deep-learning healthcare. Nature Medicine. 25(1). 14–15. 152 indexed citations
17.
Lituiev, Dmytro, Hari Trivedi, Maryam Panahiazar, et al.. (2018). Automatic Labeling of Special Diagnostic Mammography Views from Images and DICOM Headers. Journal of Digital Imaging. 32(2). 228–233. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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