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.
A large language model for electronic health records
2022434 citationsXi Yang, Aokun Chen et al.npj Digital Medicineprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Cheryl Martin'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 Cheryl Martin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cheryl Martin more than expected).
This network shows the impact of papers produced by Cheryl Martin. 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 Cheryl Martin. The network helps show where Cheryl Martin may publish in the future.
Co-authorship network of co-authors of Cheryl Martin
This figure shows the co-authorship network connecting the top 25 collaborators of Cheryl Martin.
A scholar is included among the top collaborators of Cheryl Martin 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 Cheryl Martin. Cheryl Martin is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Yang, Xi, Aokun Chen, Kaleb E Smith, et al.. (2022). A large language model for electronic health records. npj Digital Medicine. 5(1). 194–194.434 indexed citations breakdown →
Martin, Cheryl, et al.. (2016). Evaluating Methods for Distinguishing Between Human-Readable Text and Garbled Text.. The Florida AI Research Society. 276–281.1 indexed citations
Martin, Cheryl, et al.. (2012). Crowdsourcing Evaluations of Classifier Interpretability. National Conference on Artificial Intelligence. 21–26.4 indexed citations
7.
Ghosh, Joydeep, et al.. (2007). A framework for analyzing skew in evaluation metrics. National Conference on Artificial Intelligence. 22–27.2 indexed citations
Liu, Alexander, et al.. (2006). AI Lessons Learned from Experiments in Insider Threat Detection.. National Conference on Artificial Intelligence. 49–55.6 indexed citations
Schreckenghost, Debra, et al.. (2005). Teams of Engineers and Agents for Managing the Recovery of Water.. National Conference on Artificial Intelligence. 101–108.1 indexed citations
12.
Cañamero, Lola, Zachary Dodds, Lloyd Greenwald, et al.. (2004). The 2004 AAAI Spring Symposium Series. AI Magazine. 25(4). 95–95.5 indexed citations
Goel, Anuj Kumar, et al.. (2000). Sensible agents: the distributed architecture and test bed. IEICE Transactions on Communications. 83(5). 951–960.7 indexed citations
Barber, K. Suzanne, et al.. (1999). Simulation testbed for sensible agent-based systems in dynamic and uncertain environments. 16(4). 186–203.2 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.