Ziad Obermeyer

68 papers receiving 9.0k citations

Hit Papers

Dissecting racial bias in an algorithm used to manage t...2015202620182022201920162015202150010001.5k2.0k2.5k

Peers

Ziad Obermeyer
Comparison fields: 5 of 209
  • Health Informatics 2.4k
  • Artificial Intelligence 2.0k
  • Public Health, Environmental and Occupational Health 1.4k
  • Radiology, Nuclear Medicine and Imaging 1.3k
  • General Health Professions 1.2k
Replace Nigam H. Shah with:
Nigam H. Shah United States
Leo Anthony Celi United States
Christine Vogeli United States
Andrew L. Beam United States
Brian W. Powers United States
Marzyeh Ghassemi United States
Enrico Coiera Australia
Ezekiel Emanuel United States
I. Glenn Cohen United States
Kenneth D. Mandl United States
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Citations per field
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Citations per year

Countries citing papers authored by Ziad Obermeyer

Since Specialization
Citations

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

Fields of papers citing papers by Ziad Obermeyer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ziad Obermeyer. 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 Ziad Obermeyer. The network helps show where Ziad Obermeyer may publish in the future.

Co-authorship network of co-authors of Ziad Obermeyer

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 6
2 0
3 4
4 148
5
An algorithmic approach to reducing unexplained pain disparities in underserved populationsbreakdown →
158
6
Group Testing in a Pandemic: The Role of Frequent Testing, Correlated Risk, and Machine Learning
3
7 12
8
Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography
3
9
Direct Uncertainty Prediction with Applications to Healthcare.
3
10 59
11 75
12 62
13 8
14 6
15 196
16 2
17 72
18
Knowledge, attitudes and beliefs about tuberculosis in urban Morocco.
17
19 71
20 489

About Ziad Obermeyer

Ziad Obermeyer is a scholar working on Health Informatics, Health Information Management and Emergency Medicine, having authored 71 papers that have together received 9.3k indexed citations. Recurring topics across this work include Emergency and Acute Care Studies (14 papers), Healthcare Policy and Management (13 papers) and Machine Learning in Healthcare (12 papers). The work is most often cited by research in Health Informatics (2.4k citations), Health Information Management (702 citations) and Safety Research (759 citations). Ziad Obermeyer has collaborated with scholars based in United States, Switzerland and United Kingdom. Frequent co-authors include Ezekiel Emanuel, Sendhil Mullainathan, Brian W. Powers, Christine Vogeli, Emmanuela Gakidou, Stella Nordhagen, David Cutler, Jon Kleinberg, Jens Ludwig and Maggie Makar. Their work appears in journals such as Science, New England Journal of Medicine and Proceedings of the National Academy of Sciences.

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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