Matthew B. A. McDermott

4.4k total citations · 2 hit papers
23 papers, 1.6k citations indexed

About

Matthew B. A. McDermott is a scholar working on Artificial Intelligence, Health Informatics and Epidemiology. According to data from OpenAlex, Matthew B. A. McDermott has authored 23 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 5 papers in Health Informatics and 4 papers in Epidemiology. Recurrent topics in Matthew B. A. McDermott's work include Machine Learning in Healthcare (8 papers), Topic Modeling (5 papers) and Artificial Intelligence in Healthcare and Education (5 papers). Matthew B. A. McDermott is often cited by papers focused on Machine Learning in Healthcare (8 papers), Topic Modeling (5 papers) and Artificial Intelligence in Healthcare and Education (5 papers). Matthew B. A. McDermott collaborates with scholars based in United States, Canada and United Kingdom. Matthew B. A. McDermott's co-authors include Marzyeh Ghassemi, Tristan Naumann, Wei‐Hung Weng, William Boag, Emily Alsentzer, John R. Murphy, Haoran Zhang, Irene Y. Chen, Laleh Seyyed-Kalantari and Shirly Wang and has published in prestigious journals such as Nature Medicine, Science Translational Medicine and Molecular Biology of the Cell.

In The Last Decade

Matthew B. A. McDermott

22 papers receiving 1.5k citations

Hit Papers

2 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Publicly Available Clinical

2019 781 citations Emily Alsentzer, John R. Murphy et al. profile →
Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations

2021 361 citations Laleh Seyyed-Kalantari, Haoran Zhang et al. Nature Medicine profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Matthew B. A. McDermott United States	12	1.0k	421	327	316	154	23	1.6k
Xi Yang United States	19	760 0.7×	338 0.8×	294 0.9×	169 0.5×	160 1.0×	59	1.6k
Brett K. Beaulieu‐Jones United States	16	624 0.6×	266 0.6×	167 0.5×	191 0.6×	170 1.1×	33	1.4k
Jason Fries United States	16	874 0.9×	266 0.6×	156 0.5×	162 0.5×	146 0.9×	41	1.4k
Wei‐Hung Weng United States	14	1.1k 1.0×	185 0.4×	419 1.3×	232 0.7×	120 0.8×	25	1.6k
Laura Gutiérrez Singapore	8	687 0.7×	756 1.8×	135 0.4×	476 1.5×	100 0.6×	20	1.7k
Kabilan Elangovan Singapore	7	692 0.7×	747 1.8×	137 0.4×	390 1.2×	96 0.6×	23	1.7k
Emily Alsentzer United States	9	827 0.8×	255 0.6×	333 1.0×	134 0.4×	109 0.7×	22	1.2k
Benjamin Shickel United States	15	837 0.8×	288 0.7×	151 0.5×	225 0.7×	343 2.2×	58	1.5k
Ting Fang Tan Singapore	13	880 0.9×	970 2.3×	162 0.5×	579 1.8×	147 1.0×	27	2.2k
Arun James Thirunavukarasu United Kingdom	13	898 0.9×	1.1k 2.6×	172 0.5×	628 2.0×	135 0.9×	37	2.2k

Countries citing papers authored by Matthew B. A. McDermott

Since Specialization

Citations

This map shows the geographic impact of Matthew B. A. McDermott'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 Matthew B. A. McDermott with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew B. A. McDermott more than expected).

Fields of papers citing papers by Matthew B. A. McDermott

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matthew B. A. McDermott. 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 Matthew B. A. McDermott. The network helps show where Matthew B. A. McDermott may publish in the future.

Co-authorship network of co-authors of Matthew B. A. McDermott

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew B. A. McDermott. A scholar is included among the top collaborators of Matthew B. A. McDermott 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 Matthew B. A. McDermott. Matthew B. A. McDermott is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Oufattole, Nassim, Matthew B. A. McDermott, Jarosław Wąs, et al.. (2025). Foundation model of electronic medical records for adaptive risk estimation. GigaScience. 14.

Brat, Gabriel A., Joshua C. Mandel, & Matthew B. A. McDermott. (2024). Do We Need Data Standards in the Era of Large Language Models?. NEJM AI. 1(8). 4 indexed citations

Angelotti, Giovanni, et al.. (2024). A Closer Look at AUROC and AUPRC under Class Imbalance. 44102–44163. 2 indexed citations

McDermott, Matthew B. A., et al.. (2023). Structure-inducing pre-training. Nature Machine Intelligence. 5(6). 612–621. 12 indexed citations

Kwong, Jethro C.C., Adree Khondker, Katherine Lajkosz, et al.. (2023). APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for Clinical Decision Support. JAMA Network Open. 6(9). e2335377–e2335377. 51 indexed citations

McDermott, Matthew B. A., Bret Nestor, & Peter Szolovits. (2022). Clinical Artificial Intelligence. Clinics in Laboratory Medicine. 43(1). 29–46. 3 indexed citations

McDermott, Matthew B. A. & Jason Rife. (2022). Enhanced Laser-Scan Matching with Online Error Estimation for Highway and Tunnel Driving. Proceedings of the Institute of Navigation ... International Technical Meeting/Proceedings of the ... International Technical Meeting of The Institute of Navigation. 643–654. 6 indexed citations

McDermott, Matthew B. A., Shirly Wang, Nikki Marinsek, et al.. (2021). Reproducibility in machine learning for health research: Still a ways to go. Science Translational Medicine. 13(586). 151 indexed citations

Seyyed-Kalantari, Laleh, Haoran Zhang, Matthew B. A. McDermott, Irene Y. Chen, & Marzyeh Ghassemi. (2021). Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nature Medicine. 27(12). 2176–2182. 361 indexed citations breakdown →

10.

Roy, Subhrajit, Emily Alsentzer, Matthew B. A. McDermott, et al.. (2020). Machine Learning for Health (ML4H) 2020: Advancing Healthcare for All. 1–11. 6 indexed citations

11.

Nestor, Bret, Matthew B. A. McDermott, Willie Boag, et al.. (2019). Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks. 381–405. 5 indexed citations

12.

McDermott, Matthew B. A., Shirly Wang, Nikki Marinsek, et al.. (2019). Reproducibility in Machine Learning for Health. International Conference on Learning Representations. 3 indexed citations

13.

Liu, Guanxiong, Tzu-Ming Harry Hsu, Matthew B. A. McDermott, et al.. (2019). Clinically Accurate Chest X-Ray Report Generation.. 249–269. 16 indexed citations

14.

Alsentzer, Emily, John R. Murphy, William Boag, et al.. (2019). Publicly Available Clinical. 72–78. 781 indexed citations breakdown →

15.

Boag, William, et al.. (2019). Baselines for Chest X-Ray Report Generation. 126–140. 13 indexed citations

16.

Wang, Shirly, et al.. (2019). MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III. arXiv (Cornell University). 19 indexed citations

17.

McDermott, Matthew B. A., Tristan Naumann, Nathan Hunt, et al.. (2018). Semi-Supervised Biomedical Translation With Cycle Wasserstein Regression GANs. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 13 indexed citations

18.

Jin, Di, et al.. (2018). MIT-MEDG at SemEval-2018 Task 7: Semantic Relation Classification via Convolution Neural Network. 798–804. 5 indexed citations

19.

Coffman, Valerie C., et al.. (2016). Stronger net posterior cortical forces and asymmetric microtubule arrays produce simultaneous centration and rotation of the pronuclear complex in the earlyCaenorhabditis elegansembryo. Molecular Biology of the Cell. 27(22). 3550–3562. 8 indexed citations

20.

Wright, Steve, et al.. (2011). Expert SharePoint 2010 Practices. Apress eBooks. 1 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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