Casey Overby Taylor

814 total citations
43 papers, 206 citations indexed

About

Casey Overby Taylor is a scholar working on Public Health, Environmental and Occupational Health, General Health Professions and Genetics. According to data from OpenAlex, Casey Overby Taylor has authored 43 papers receiving a total of 206 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Public Health, Environmental and Occupational Health, 10 papers in General Health Professions and 6 papers in Genetics. Recurrent topics in Casey Overby Taylor's work include Ethics in Clinical Research (6 papers), Mobile Health and mHealth Applications (6 papers) and Genomics and Rare Diseases (5 papers). Casey Overby Taylor is often cited by papers focused on Ethics in Clinical Research (6 papers), Mobile Health and mHealth Applications (6 papers) and Genomics and Rare Diseases (5 papers). Casey Overby Taylor collaborates with scholars based in United States, Israel and United Kingdom. Casey Overby Taylor's co-authors include Christopher G. Chute, Stanley C. Ahalt, Emily Pfaff, Karamarie Fecho, Marshall Clark, Hao Xu, Robert L. Bradford, Véra Ehrenstein, Hadi Kharrazi and Harold P. Lehmann and has published in prestigious journals such as PLoS ONE, Neurology and Scientific Reports.

In The Last Decade

Casey Overby Taylor

33 papers receiving 198 citations

Peers

Casey Overby Taylor

PHEOH

HIM

GHP

GENET

Nigel Hughes United Kingdom

Sebastian C. Semler Germany

Nathan Lea United Kingdom

Deevakar Rogith United States

Umberto Tachinardi United States

Michael Flynn United States

Matthias Ganzinger Germany

Nekane Larburu Spain

Václav Papež Czechia

Firouzeh Ghaffari Iran

Nigel Hughes United Kingdom

Casey Overby Taylor

38 ×0.8

PHEOH

29 ×0.8

32 ×0.9

HIM

22 ×0.7

GHP

7 ×0.3

GENET

Citations per year, relative to Casey Overby Taylor Casey Overby Taylor (= 1×) peers Nigel Hughes

Countries citing papers authored by Casey Overby Taylor

Since Specialization

Citations

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

Fields of papers citing papers by Casey Overby Taylor

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Casey Overby Taylor. 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 Casey Overby Taylor. The network helps show where Casey Overby Taylor may publish in the future.

Co-authorship network of co-authors of Casey Overby Taylor

This figure shows the co-authorship network connecting the top 25 collaborators of Casey Overby Taylor. A scholar is included among the top collaborators of Casey Overby Taylor 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 Casey Overby Taylor. Casey Overby Taylor 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

Applegate, Carolyn, et al.. (2025). Assessing genetic counseling efficiency with natural language processing. Journal of the American Medical Informatics Association. 33(2). 295–303.

Taylor, Casey Overby, et al.. (2025). DrIVeNN: Drug Interaction Vectors Neural Network. Journal of Computational Biology. 32(7). 696–706.

Speed, Traci J., et al.. (2025). Predicting postoperative chronic opioid use with fair machine learning models integrating multi-modal data sources: a demonstration of ethical machine learning in healthcare. Journal of the American Medical Informatics Association. 32(6). 985–997. 2 indexed citations

Yang, Jiyuan, et al.. (2025). Preoperative risk prediction of major cardiovascular events in noncardiac surgery using the 12-lead electrocardiogram: an explainable deep learning approach. British Journal of Anaesthesia. 135(5). 1161–1171.

Yan, Aixia, Traci J. Speed, & Casey Overby Taylor. (2025). Relapse prediction using wearable data through convolutional autoencoders and clustering for patients with psychotic disorders. Scientific Reports. 15(1). 18806–18806. 1 indexed citations

Sedoc, João, et al.. (2024). Usability, Engagement, and Report Usefulness of Chatbot-Based Family Health History Data Collection: Mixed Methods Analysis. Journal of Medical Internet Research. 26. e55164–e55164. 2 indexed citations

Zirikly, Ayah, et al.. (2024). Taxonomy-based prompt engineering to generate synthetic drug-related patient portal messages. Journal of Biomedical Informatics. 160. 104752–104752. 1 indexed citations

Green, Ariel R., Daniel Martin, Kelly T. Gleason, et al.. (2024). Characterizing patient portal use of people with cognitive impairment and potentially inappropriate medications. Journal of the American Geriatrics Society. 73(3). 750–758. 1 indexed citations

Flaks‐Manov, Natalie, et al.. (2024). Patterns of healthcare utilization according to health equity determinants during the first year of the pandemic at Johns Hopkins Medicine. JAMIA Open. 7(4). ooae093–ooae093.

10.

Wright, Robert A., et al.. (2024). Participant Contributions to Person-Generated Health Data Research Using Mobile Devices: Scoping Review. Journal of Medical Internet Research. 27. e51955–e51955. 2 indexed citations

11.

Speed, Traci J., et al.. (2024). Predicting Postoperative Pain and Opioid Use with Machine Learning Applied to Longitudinal Electronic Health Record and Wearable Data. Applied Clinical Informatics. 15(3). 569–582. 3 indexed citations

12.

Taylor, Casey Overby, et al.. (2023). Data Representativeness in Cardiovascular Disease Studies that use Consumer Wearables. 695–697. 1 indexed citations

13.

Chen, Yijia, et al.. (2023). Factors associated with resistance to SARS-CoV-2 infection discovered using large-scale medical record data and machine learning. PLoS ONE. 18(2). e0278466–e0278466. 1 indexed citations

14.

Bertram, Amanda, et al.. (2023). Will the Doctor “See” You Now? The Development and Implementation of a Targeted Telemedicine System for Primary Care. PubMed. 7(2). e71–e78. 1 indexed citations

15.

Taylor, Casey Overby, et al.. (2023). Modifying the Design Sprint Methodology: Leveraging Online Tools for Collaborating and Prototyping in Research Settings. 527–528.

16.

Taylor, Casey Overby, et al.. (2021). Willingness to Share Wearable Device Data for Research Among Mechanical Turk Workers: Web-Based Survey Study. Journal of Medical Internet Research. 23(10). e19789–e19789. 5 indexed citations

17.

Zhang, Xiaohan, et al.. (2020). Feature engineering with clinical expert knowledge: A case study assessment of machine learning model complexity and performance. PLoS ONE. 15(4). e0231300–e0231300. 27 indexed citations

18.

Williams, Marc S., Casey Overby Taylor, Nephi Walton, et al.. (2019). Genomic Information for Clinicians in the Electronic Health Record: Lessons Learned From the Clinical Genome Resource Project and the Electronic Medical Records and Genomics Network. Frontiers in Genetics. 10. 1059–1059. 33 indexed citations

19.

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

20.

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

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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