Emily Pfaff

4.9k total citations · 1 hit paper
50 papers, 638 citations indexed

About

Emily Pfaff is a scholar working on Artificial Intelligence, Health Information Management and Neurology. According to data from OpenAlex, Emily Pfaff has authored 50 papers receiving a total of 638 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 11 papers in Health Information Management and 10 papers in Neurology. Recurrent topics in Emily Pfaff's work include Machine Learning in Healthcare (11 papers), Long-Term Effects of COVID-19 (10 papers) and COVID-19 Clinical Research Studies (9 papers). Emily Pfaff is often cited by papers focused on Machine Learning in Healthcare (11 papers), Long-Term Effects of COVID-19 (10 papers) and COVID-19 Clinical Research Studies (9 papers). Emily Pfaff collaborates with scholars based in United States, Norway and United Kingdom. Emily Pfaff's co-authors include Christopher G. Chute, Stanley C. Ahalt, Karamarie Fecho, Melissa Haendel, Hao Xu, Ashok Krishnamurthy, Elaine Hill, Richard A. Moffitt, Robert L. Bradford and Johanna Loomba and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Gastroenterology.

In The Last Decade

Emily Pfaff

48 papers receiving 626 citations

Hit 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.

Coding long COVID: characterizing a new disease through an ICD-10 lens

2023 84 citations Emily Pfaff, Charisse Madlock‐Brown et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Emily Pfaff United States	15	123	105	82	69	67	50	638
Jing Huang United States	16	33 0.3×	24 0.2×	42 0.5×	52 0.8×	22 0.3×	87	818
Wai Keong Wong United Kingdom	15	94 0.8×	15 0.1×	42 0.5×	187 2.7×	36 0.5×	36	1.1k
Kenan Direk United Kingdom	12	13 0.1×	46 0.4×	58 0.7×	22 0.3×	22 0.3×	20	799
Chengyun Liu China	15	174 1.4×	16 0.2×	22 0.3×	292 4.2×	29 0.4×	43	864
Helen Strongman United Kingdom	15	29 0.2×	18 0.2×	13 0.2×	47 0.7×	21 0.3×	35	1.1k
Anna Ostropolets United States	11	33 0.3×	51 0.5×	64 0.8×	107 1.6×	5 0.1×	33	459
Enrico Longato Italy	12	145 1.2×	60 0.6×	65 0.8×	337 4.9×	8 0.1×	31	880
Christian Haverkamp Germany	9	101 0.8×	33 0.3×	32 0.4×	31 0.4×	20 0.3×	18	310
John Fontanesi United States	15	48 0.4×	27 0.3×	12 0.1×	36 0.5×	10 0.1×	48	1.1k
Rupert Major United Kingdom	13	64 0.5×	17 0.2×	18 0.2×	57 0.8×	7 0.1×	32	868

Countries citing papers authored by Emily Pfaff

Since Specialization

Citations

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

Fields of papers citing papers by Emily Pfaff

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Emily Pfaff

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

All Works

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20 of 20 papers shown

Kahn, Michael G., Tellen D. Bennett, Rachel Deer, et al.. (2025). Identifying who has long COVID in the USA: a machine learning approach using N3C data. UNC Libraries.

Powers, John, Abhishek Bhatia, Charisse Madlock‐Brown, et al.. (2025). Identifying commonalities and differences between EHR representations of PASC and ME/CFS in the RECOVER EHR cohort. Communications Medicine. 5(1). 109–109. 1 indexed citations

Preiss, Alexander, M. Daniel Brannock, John M. Baratta, et al.. (2025). Re-engineering a machine learning phenotype to adapt to the changing COVID-19 landscape: a machine learning modelling study from the N3C and RECOVER consortia. The Lancet Digital Health. 7(8). 100887–100887. 1 indexed citations

Preiss, Alexander, Abhishek Bhatia, John M. Baratta, et al.. (2025). Effect of Paxlovid treatment during acute COVID-19 on Long COVID onset: An EHR-based target trial emulation from the N3C and RECOVER consortia. PLoS Medicine. 22(9). e1004711–e1004711. 1 indexed citations

Preisser, John S., Emily Pfaff, Rujin Wang, et al.. (2025). Predictors of new persistent opioid use after surgery in adults. PubMed. 3(1). 2–2. 1 indexed citations

O’Neil, Shawn T., Charisse Madlock‐Brown, Kenneth J. Wilkins, et al.. (2024). Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs. npj Digital Medicine. 7(1). 296–296. 2 indexed citations

Hadley, Emily, Yun Jae Yoo, Bryan Laraway, et al.. (2024). Insights from an N3C RECOVER EHR-based cohort study characterizing SARS-CoV-2 reinfections and Long COVID. SHILAP Revista de lepidopterología. 4(1). 129–129. 18 indexed citations

Yadaw, Arjun S., David Sahner, Hythem Sidky, et al.. (2023). Preexisting Autoimmunity Is Associated With Increased Severity of Coronavirus Disease 2019: A Retrospective Cohort Study Using Data From the National COVID Cohort Collaborative (N3C). Clinical Infectious Diseases. 77(6). 816–826. 6 indexed citations

Suver, Christine, Jeremy Harper, Johanna Loomba, et al.. (2023). The N3C governance ecosystem: A model socio-technical partnership for the future of collaborative analytics at scale. Journal of Clinical and Translational Science. 7(1). e252–e252. 3 indexed citations

10.

Hill, Elaine, Hemalkumar B. Mehta, Suchetha Sharma, et al.. (2023). Risk factors associated with post-acute sequelae of SARS-CoV-2: an N3C and NIH RECOVER study. BMC Public Health. 23(1). 2103–2103. 25 indexed citations

11.

Harding, Jessica L., Emily Pfaff, Edward J. Boyko, & Pandora L. Wander. (2023). Addressing common sources of bias in studies of new-onset type 2 diabetes following COVID that use electronic health record data. SHILAP Revista de lepidopterología. 14. 100193–100193. 1 indexed citations

12.

Fecho, Karamarie, Stanley C. Ahalt, Ashok Krishnamurthy, et al.. (2022). Leveraging Open Electronic Health Record Data and Environmental Exposures Data to Derive Insights Into Rare Pulmonary Disease. Frontiers in Artificial Intelligence. 5. 918888–918888. 1 indexed citations

13.

Paquin, Ryan S., et al.. (2022). Using a Patient Portal to Increase Enrollment in a Newborn Screening Research Study: Observational Study. JMIR Pediatrics and Parenting. 5(1). e30941–e30941. 10 indexed citations

14.

Bookman, Richard J., James J. Cimino, Christopher A. Harle, et al.. (2021). Research informatics and the COVID-19 pandemic: Challenges, innovations, lessons learned, and recommendations. SHILAP Revista de lepidopterología. 5(1). e110–e110. 9 indexed citations

15.

Fecho, Karamarie, Stanley C. Ahalt, Stephen J. Appold, et al.. (2021). Development and Application of an Open Tool for Sharing and Analyzing Integrated Clinical and Environmental Exposures Data: Asthma Use Case. JMIR Formative Research. 6(4). e32357–e32357. 4 indexed citations

16.

Fecho, Karamarie, Stanley C. Ahalt, Saravanan Arunachalam, et al.. (2019). Sex, obesity, diabetes, and exposure to particulate matter among patients with severe asthma: Scientific insights from a comparative analysis of open clinical data sources during a five-day hackathon. Journal of Biomedical Informatics. 100. 103325–103325. 13 indexed citations

17.

Pfaff, Emily, et al.. (2019). Clinical Annotation Research Kit (CLARK): Computable Phenotyping Using Machine Learning. JMIR Medical Informatics. 8(1). e16042–e16042. 8 indexed citations

18.

Pfaff, Emily, Robert L. Bradford, Marshall Clark, et al.. (2019). Fast Healthcare Interoperability Resources (FHIR) as a Meta Model to Integrate Common Data Models: Development of a Tool and Quantitative Validation Study. JMIR Medical Informatics. 7(4). e15199–e15199. 43 indexed citations

19.

Ahalt, Stanley C., Christopher G. Chute, Karamarie Fecho, et al.. (2019). Clinical Data: Sources and Types, Regulatory Constraints, Applications. Clinical and Translational Science. 12(4). 329–333. 15 indexed citations

20.

Hoffman, Sarah R., Anissa I. Vines, Jacqueline R. Halladay, et al.. (2018). Optimizing research in symptomatic uterine fibroids with development of a computable phenotype for use with electronic health records. American Journal of Obstetrics and Gynecology. 218(6). 610.e1–610.e7. 4 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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