John X. Qiu

513 total citations
10 papers, 320 citations indexed

About

John X. Qiu is a scholar working on Artificial Intelligence, Molecular Biology and Management Science and Operations Research. According to data from OpenAlex, John X. Qiu has authored 10 papers receiving a total of 320 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 6 papers in Molecular Biology and 2 papers in Management Science and Operations Research. Recurrent topics in John X. Qiu's work include Topic Modeling (8 papers), Biomedical Text Mining and Ontologies (6 papers) and Machine Learning in Healthcare (4 papers). John X. Qiu is often cited by papers focused on Topic Modeling (8 papers), Biomedical Text Mining and Ontologies (6 papers) and Machine Learning in Healthcare (4 papers). John X. Qiu collaborates with scholars based in United States. John X. Qiu's co-authors include Georgia D. Tourassi, Hong‐Jun Yoon, Paul Fearn, Shang Gao, M. Todd Young, Mohammed Alawad, Xiao‐Cheng Wu, Lynne Penberthy, Linda Coyle and Arvind Ramanathan and has published in prestigious journals such as BMC Bioinformatics, Journal of the American Medical Informatics Association and IEEE Journal of Biomedical and Health Informatics.

In The Last Decade

John X. Qiu

9 papers receiving 313 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
John X. Qiu United States	7	240	144	51	30	26	10	320
Merlijn Sevenster United States	11	191 0.8×	62 0.4×	113 2.2×	26 0.9×	44 1.7×	36	328
Mitra Montazeri Iran	7	115 0.5×	44 0.3×	56 1.1×	42 1.4×	10 0.4×	20	254
R Jakobovits United States	8	181 0.8×	188 1.3×	87 1.7×	29 1.0×	8 0.3×	16	351
Girish Chavan United States	5	167 0.7×	156 1.1×	22 0.4×	26 0.9×	20 0.8×	7	252
Yifan Yang United States	7	199 0.8×	80 0.6×	61 1.2×	28 0.9×	171 6.6×	12	430
Rishabh Kapoor United States	8	141 0.6×	54 0.4×	84 1.6×	10 0.3×	10 0.4×	29	263
Andre Quina United States	4	147 0.6×	36 0.3×	24 0.5×	63 2.1×	31 1.2×	7	265
Pilar López‐Úbeda Spain	9	182 0.8×	68 0.5×	126 2.5×	19 0.6×	103 4.0×	46	298
Stefan Hegselmann Germany	6	132 0.6×	43 0.3×	29 0.6×	29 1.0×	53 2.0×	18	233

Countries citing papers authored by John X. Qiu

Since Specialization

Citations

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

Fields of papers citing papers by John X. Qiu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John X. Qiu. 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 John X. Qiu. The network helps show where John X. Qiu may publish in the future.

Co-authorship network of co-authors of John X. Qiu

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

All Works

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10 of 10 papers shown

Alawad, Mohammed, Shang Gao, John X. Qiu, et al.. (2019). Deep Transfer Learning Across Cancer Registries for Information Extraction from Pathology Reports. PubMed. 2019. 1–4. 15 indexed citations

Gao, Shang, John X. Qiu, Mohammed Alawad, et al.. (2019). Classifying cancer pathology reports with hierarchical self-attention networks. Artificial Intelligence in Medicine. 101. 101726–101726. 40 indexed citations

Alawad, Mohammed, Shang Gao, John X. Qiu, et al.. (2019). Automatic extraction of cancer registry reportable information from free-text pathology reports using multitask convolutional neural networks. Journal of the American Medical Informatics Association. 27(1). 89–98. 56 indexed citations

Qiu, John X., Shang Gao, Mohammed Alawad, et al.. (2019). Semi-Supervised Information Extraction for Cancer Pathology Reports. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1–4. 2 indexed citations

Qiu, John X., Hong‐Jun Yoon, Kshitij Srivastava, et al.. (2018). Scalable deep text comprehension for Cancer surveillance on high-performance computing. BMC Bioinformatics. 19(S18). 488–488. 10 indexed citations

Hengartner, Nicolas, et al.. (2018). CAT: computer aided triage improving upon the Bayes risk through ε-refusal triage rules. BMC Bioinformatics. 19(S18). 485–485. 2 indexed citations

Yoon, Hong‐Jun, et al.. (2018). Filter pruning of Convolutional Neural Networks for text classification: A case study of cancer pathology report comprehension. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 345–348. 11 indexed citations

Gao, Shang, M. Todd Young, John X. Qiu, et al.. (2017). Hierarchical attention networks for information extraction from cancer pathology reports. Journal of the American Medical Informatics Association. 25(3). 321–330. 89 indexed citations

Qiu, John X., Hong‐Jun Yoon, Paul Fearn, & Georgia D. Tourassi. (2017). Deep Learning for Automated Extraction of Primary Sites From Cancer Pathology Reports. IEEE Journal of Biomedical and Health Informatics. 22(1). 244–251. 94 indexed citations

10.

Qiu, John X.. (2014). Alternative Revenues: A Quantitative Study on In- App Purchases. 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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