Keith Morse

1.5k total citations
26 papers, 292 citations indexed

About

Keith Morse is a scholar working on Artificial Intelligence, Health Information Management and General Health Professions. According to data from OpenAlex, Keith Morse has authored 26 papers receiving a total of 292 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 9 papers in Health Information Management and 8 papers in General Health Professions. Recurrent topics in Keith Morse's work include Machine Learning in Healthcare (8 papers), Electronic Health Records Systems (8 papers) and Artificial Intelligence in Healthcare and Education (7 papers). Keith Morse is often cited by papers focused on Machine Learning in Healthcare (8 papers), Electronic Health Records Systems (8 papers) and Artificial Intelligence in Healthcare and Education (7 papers). Keith Morse collaborates with scholars based in United States, Canada and Colombia. Keith Morse's co-authors include Nigam H. Shah, Veena G Jones, Nicolai P. Ostberg, Albert Chan, Birju Patel, Natalie M. Pageler, Steven C. Bagley, Ethan Steinberg, Alison Callahan and James Xie and has published in prestigious journals such as Nature Medicine, PLoS ONE and Journal of Medical Internet Research.

In The Last Decade

Keith Morse

25 papers receiving 288 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Keith Morse United States 10 89 83 80 70 48 26 292
John D. McGreevey United States 7 75 0.8× 84 1.0× 155 1.9× 53 0.8× 76 1.6× 9 396
Jackson Steinkamp United States 12 60 0.7× 55 0.7× 38 0.5× 90 1.3× 73 1.5× 23 339
Nancy Walton Canada 5 141 1.6× 69 0.8× 36 0.5× 45 0.6× 55 1.1× 8 363
Bryan D. Steitz United States 10 52 0.6× 156 1.9× 134 1.7× 44 0.6× 83 1.7× 33 355
Amanda J Moy United States 10 62 0.7× 119 1.4× 151 1.9× 61 0.9× 79 1.6× 19 432
Mollie Hobensack United States 10 170 1.9× 79 1.0× 121 1.5× 120 1.7× 84 1.8× 33 447
Lorraine J. Block Canada 6 142 1.6× 51 0.6× 55 0.7× 58 0.8× 51 1.1× 16 318
Nicoleta Economou-Zavlanos United States 6 104 1.2× 51 0.6× 29 0.4× 49 0.7× 55 1.1× 18 264
Seuli Bose‐Brill United States 12 48 0.5× 150 1.8× 65 0.8× 26 0.4× 184 3.8× 45 449
Ajay Dharod United States 11 49 0.6× 80 1.0× 36 0.5× 20 0.3× 99 2.1× 38 331

Countries citing papers authored by Keith Morse

Since Specialization
Citations

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

Fields of papers citing papers by Keith Morse

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Keith Morse

This figure shows the co-authorship network connecting the top 25 collaborators of Keith Morse. A scholar is included among the top collaborators of Keith Morse 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 Keith Morse. Keith Morse 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
1.
Morse, Keith, et al.. (2024). Natural Language Processing: Set to Transform Pediatric Research. Hospital Pediatrics. 15(1). e12–e14. 1 indexed citations
2.
Zhang, Dazheng, Jiayi Tong, Naimin Jing, et al.. (2024). Learning competing risks across multiple hospitals: one-shot distributed algorithms. Journal of the American Medical Informatics Association. 31(5). 1102–1112. 6 indexed citations
3.
Morse, Keith, Catherine Aftandilian, Ethan Steinberg, et al.. (2024). Characterizing the limitations of using diagnosis codes in the context of machine learning for healthcare. BMC Medical Informatics and Decision Making. 24(1). 51–51. 9 indexed citations
4.
Dussaq, Alex, et al.. (2024). Using a Large Language Model to Identify Adolescent Patient Portal Account Access by Guardians. JAMA Network Open. 7(6). e2418454–e2418454.
5.
Bedi, Suhana, Scott L. Fleming, Chia‐Chun Chiang, et al.. (2024). QUEST-AI: A System for Question Generation, Verification, and Refinement using AI for USMLE-Style Exams. PubMed. 30. 54–69. 2 indexed citations
6.
Johnson, Jeanene, et al.. (2024). Accuracy of a Proprietary Large Language Model in Labeling Obstetric Incident Reports. The Joint Commission Journal on Quality and Patient Safety. 50(12). 877–881. 1 indexed citations
7.
Goldstein, Rachel, et al.. (2024). Evaluation of a Large Language Model to Identify Confidential Content in Adolescent Encounter Notes. JAMA Pediatrics. 178(3). 308–308. 6 indexed citations
8.
Guo, Lin, Jason Fries, Ethan Steinberg, et al.. (2024). A multi-center study on the adaptability of a shared foundation model for electronic health records. npj Digital Medicine. 7(1). 171–171. 15 indexed citations
9.
Goldstein, Rachel, et al.. (2023). The Prevalence of Confidential Content in Adolescent Progress Notes Prior to the 21st Century Cures Act Information Blocking Mandate. Applied Clinical Informatics. 14(2). 337–344. 4 indexed citations
10.
Lorman, Vitaly, Hanieh Razzaghi, Xing Song, et al.. (2023). A machine learning-based phenotype for long COVID in children: An EHR-based study from the RECOVER program. PLoS ONE. 18(8). e0289774–e0289774. 4 indexed citations
11.
Calway, Tyler, et al.. (2023). Pseudorandomized Testing of a Discharge Medication Alert to Reduce Free-Text Prescribing. Applied Clinical Informatics. 14(3). 470–477. 2 indexed citations
12.
Steinberg, Ethan, et al.. (2023). A Natural Language Processing Model to Identify Confidential Content in Adolescent Clinical Notes. Applied Clinical Informatics. 14(3). 400–407. 5 indexed citations
13.
Guo, Lin, Ethan Steinberg, Keith Morse, et al.. (2023). Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks. Journal of the American Medical Informatics Association. 30(12). 2004–2011. 7 indexed citations
14.
Morse, Keith, Scott L. Fleming, David Scheinker, et al.. (2022). Monitoring Approaches for a Pediatric Chronic Kidney Disease Machine Learning Model. Applied Clinical Informatics. 13(2). 431–438. 3 indexed citations
15.
Xie, James, et al.. (2021). Ensuring Adolescent Patient Portal Confidentiality in the Age of the Cures Act Final Rule. Journal of Adolescent Health. 69(6). 933–939. 22 indexed citations
16.
Callahan, Alison, Saurabh Gombar, Eli M. Cahan, et al.. (2021). Using Aggregate Patient Data at the Bedside via an On-Demand Consultation Service. NEJM Catalyst. 2(10). 7 indexed citations
17.
Kashyap, Sehj, Keith Morse, Birju Patel, & Nigam H. Shah. (2021). A survey of extant organizational and computational setups for deploying predictive models in health systems. Journal of the American Medical Informatics Association. 28(11). 2445–2450. 17 indexed citations
18.
Morse, Keith, Nicolai P. Ostberg, Veena G Jones, & Albert Chan. (2020). Use Characteristics and Triage Acuity of a Digital Symptom Checker in a Large Integrated Health System: Population-Based Descriptive Study. Journal of Medical Internet Research. 22(11). e20549–e20549. 51 indexed citations
19.
Morse, Keith, Steven C. Bagley, & Nigam H. Shah. (2020). Estimate the hidden deployment cost of predictive models to improve patient care. Nature Medicine. 26(1). 18–19. 23 indexed citations
20.
Koullias, George J., et al.. (2020). Type I collagen matrix plus polyhexamethylene biguanide antimicrobial for the treatment of cutaneous wounds. Journal of Comparative Effectiveness Research. 9(10). 691–703. 13 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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