Daniel J. Riskin

779 total citations
12 papers, 512 citations indexed

About

Daniel J. Riskin is a scholar working on Surgery, Public Health, Environmental and Occupational Health and Neurology. According to data from OpenAlex, Daniel J. Riskin has authored 12 papers receiving a total of 512 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Surgery, 4 papers in Public Health, Environmental and Occupational Health and 2 papers in Neurology. Recurrent topics in Daniel J. Riskin's work include Machine Learning in Healthcare (2 papers), Health and Medical Research Impacts (2 papers) and Ethics in Clinical Research (2 papers). Daniel J. Riskin is often cited by papers focused on Machine Learning in Healthcare (2 papers), Health and Medical Research Impacts (2 papers) and Ethics in Clinical Research (2 papers). Daniel J. Riskin collaborates with scholars based in United States. Daniel J. Riskin's co-authors include Thomas M. Krummel, Michael T. Longaker, Susan I. Brundage, Mary-Anne Purtill, David A. Spain, Thomas C. Tsai, Paul M. Maggio, Tina Hernandez‐Boussard, Gianluca Turcatel and Néstor A. Molfino and has published in prestigious journals such as Annals of Surgery, The American Journal of Surgery and Journal of the American College of Surgeons.

In The Last Decade

Daniel J. Riskin

12 papers receiving 499 citations

Peers

Daniel J. Riskin
Jeffrey A. Bailey United States
Jeffrey W. Simmons United States
Nancy L. Szaflarski United States
K. Pendry United Kingdom
Megan Rowley United Kingdom
Irwin Gross United States
David G. Bishop South Africa
Robert C. Jacoby United States
Charles P. Shahan United States
Nicole M. Tapia United States
Jeffrey A. Bailey United States
Daniel J. Riskin
Citations per year, relative to Daniel J. Riskin Daniel J. Riskin (= 1×) peers Jeffrey A. Bailey

Countries citing papers authored by Daniel J. Riskin

Since Specialization
Citations

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

Fields of papers citing papers by Daniel J. Riskin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel J. Riskin. 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 Daniel J. Riskin. The network helps show where Daniel J. Riskin may publish in the future.

Co-authorship network of co-authors of Daniel J. Riskin

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

All Works

12 of 12 papers shown
1.
Riskin, Daniel J., Keri L. Monda, Joshua J. Gagne, et al.. (2025). Implementing Accuracy, Completeness, and Traceability for Data Reliability. JAMA Network Open. 8(3). e250128–e250128. 3 indexed citations
2.
Garan, A.R., Keri L. Monda, Ricardo E. Dent‐Acosta, Daniel J. Riskin, & Ty J. Gluckman. (2023). Retrospective comparison of traditional and artificial intelligence-based heart failure phenotyping in a US health system to enable real-world evidence. BMJ Open. 13(8). e073178–e073178. 3 indexed citations
3.
Molfino, Néstor A., Gianluca Turcatel, & Daniel J. Riskin. (2023). Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review. Advances in Therapy. 41(2). 534–552. 21 indexed citations
4.
Menon, Jayant & Daniel J. Riskin. (2015). Technological Innovation and Ethical Response in Neurosurgery. The AMA Journal of Ethic. 17(1). 62–68. 1 indexed citations
5.
Koppel, Ross, et al.. (2014). Re-examining health IT policy: what will it take to derive value from our investment?. Journal of the American Medical Informatics Association. 22(2). 459–464. 12 indexed citations
6.
Riskin, Daniel J., et al.. (2010). A Patient-Centered, Ethical Approach to Medical Device Innovation. The AMA Journal of Ethic. 12(2). 91–95. 6 indexed citations
7.
Riskin, Daniel J., Thomas C. Tsai, Tina Hernandez‐Boussard, et al.. (2009). Massive Transfusion Protocols: The Role of Aggressive Resuscitation Versus Product Ratio in Mortality Reduction. Journal of the American College of Surgeons. 209(2). 198–205. 271 indexed citations
8.
Garland, Adella, Daniel J. Riskin, Susan I. Brundage, et al.. (2007). A county hospital surgical practice: a model for acute care surgery. The American Journal of Surgery. 194(6). 758–764. 24 indexed citations
9.
Krummel, Thomas M., Michael Gertner, Josh Makower, et al.. (2006). Inventing our future: Training the next generation of surgeon innovators. Seminars in Pediatric Surgery. 15(4). 309–318. 19 indexed citations
10.
Riskin, Daniel J., et al.. (2006). Innovation in Surgery. Annals of Surgery. 244(5). 686–693. 130 indexed citations
11.
Riskin, Daniel J., Michael T. Longaker, & Thomas M. Krummel. (2006). The ethics of innovation in pediatric surgery. Seminars in Pediatric Surgery. 15(4). 319–323. 16 indexed citations
12.
Riskin, Daniel J. & Steven D. Schwaitzberg. (2003). A comparison of holding strength of various surgical clips. Surgical Endoscopy. 17(4). 654–656. 6 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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