David C. Wilkins

1.2k total citations
49 papers, 651 citations indexed

About

David C. Wilkins is a scholar working on Artificial Intelligence, Surgery and Pulmonary and Respiratory Medicine. According to data from OpenAlex, David C. Wilkins has authored 49 papers receiving a total of 651 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Artificial Intelligence, 14 papers in Surgery and 6 papers in Pulmonary and Respiratory Medicine. Recurrent topics in David C. Wilkins's work include AI-based Problem Solving and Planning (19 papers), Bayesian Modeling and Causal Inference (14 papers) and Machine Learning and Algorithms (6 papers). David C. Wilkins is often cited by papers focused on AI-based Problem Solving and Planning (19 papers), Bayesian Modeling and Causal Inference (14 papers) and Machine Learning and Algorithms (6 papers). David C. Wilkins collaborates with scholars based in United States, United Kingdom and Canada. David C. Wilkins's co-authors include A W Lambert, S. Ashley, Ole J. Mengshoel, C Cosgrove, A. J. Walker, Bruce G. Buchanan, Dan Roth, Y.G. Wilson, Vadim Bulitko and J. Barwell and has published in prestigious journals such as British journal of surgery, Artificial Intelligence and IEEE Transactions on Knowledge and Data Engineering.

In The Last Decade

David C. Wilkins

46 papers receiving 592 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David C. Wilkins United States 16 322 203 168 75 65 49 651
Yen‐Cheng Chen Taiwan 14 146 0.5× 75 0.4× 90 0.5× 10 0.1× 12 0.2× 81 720
Peter V. Moulder United States 15 348 1.1× 17 0.1× 405 2.4× 26 0.3× 74 1.1× 74 857
Pekka Aho Finland 13 297 0.9× 26 0.1× 148 0.9× 149 2.0× 38 0.6× 48 605
Yuefeng Ma China 19 228 0.7× 143 0.7× 103 0.6× 10 0.1× 4 0.1× 90 1.1k
Mostafa A. Salama Egypt 14 120 0.4× 117 0.6× 63 0.4× 6 0.1× 3 0.0× 53 1.2k
John Zaki Egypt 10 116 0.4× 40 0.2× 24 0.1× 16 0.2× 32 0.5× 31 388
Cheng‐Chung Cheng Taiwan 19 118 0.4× 68 0.3× 113 0.7× 31 0.4× 3 0.0× 52 1.0k
Pasquale De Cata Italy 11 181 0.6× 165 0.8× 73 0.4× 9 0.1× 3 0.0× 19 869
Erdoğan İlkay Türkiye 17 222 0.7× 50 0.2× 240 1.4× 58 0.8× 6 0.1× 56 854
Rongrong Ren China 15 205 0.6× 32 0.2× 86 0.5× 4 0.1× 15 0.2× 45 656

Countries citing papers authored by David C. Wilkins

Since Specialization
Citations

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

Fields of papers citing papers by David C. Wilkins

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David C. Wilkins. 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 David C. Wilkins. The network helps show where David C. Wilkins may publish in the future.

Co-authorship network of co-authors of David C. Wilkins

This figure shows the co-authorship network connecting the top 25 collaborators of David C. Wilkins. A scholar is included among the top collaborators of David C. Wilkins 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 David C. Wilkins. David C. Wilkins 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.
Mengshoel, Ole J., David C. Wilkins, & Dan Roth. (2010). Initialization and Restart in Stochastic Local Search: Computing a Most Probable Explanation in Bayesian Networks. IEEE Transactions on Knowledge and Data Engineering. 23(2). 235–247. 25 indexed citations
2.
Mengshoel, Ole J., David C. Wilkins, & Dan Roth. (2006). Controlled generation of hard and easy Bayesian networks: Impact on maximal clique size in tree clustering. Artificial Intelligence. 170(16-17). 1137–1174. 22 indexed citations
3.
Wilkins, David C., et al.. (2003). Collaborative decision making and intelligent reasoning in judge advisor systems. 27. 9–9. 1 indexed citations
4.
Cosgrove, C, et al.. (2002). Surgical experience and supervision may influence the quality of lower limb amputation. Annals of The Royal College of Surgeons of England. 84(5). 344–347. 18 indexed citations
5.
Wilkins, David C., et al.. (2001). Supervisory Control System for Ship Damage Control: Volume 4 - Intelligent Reasoning. Defense Technical Information Center (DTIC). 1 indexed citations
6.
Bulitko, Vadim & David C. Wilkins. (1999). Automated instructor assistant for ship damage control. National Conference on Artificial Intelligence. 778–785. 21 indexed citations
7.
Barwell, J., et al.. (1999). Life-threatening retroperitoneal sepsis after hemorrhoid injection sclerotherapy. Diseases of the Colon & Rectum. 42(3). 421–423. 38 indexed citations
8.
Hsu, William H., et al.. (1998). Bayesian network models for generation of crisis management training scenarios. National Conference on Artificial Intelligence. 112(6). 1113–1120. 15 indexed citations
9.
Mengshoel, Ole J., et al.. (1998). Deceptive and Other Functions of Unitation as Bayesian Networks. Tobacco Control. 13(1). 23–8. 3 indexed citations
10.
Tytherleigh, M G, G. Charnley, & David C. Wilkins. (1998). Closed rupture of the posterior tibial artery secondary to a soccer injury.. PubMed. 80(4). 266–8. 4 indexed citations
11.
Wilkins, David C. & Keith A. Dookeran. (1997). Audit of general practitioner referrals to a surgical assessment unit: New methods to improve the efficiency of the acute surgical service. British journal of surgery. 84(5). 727–728. 2 indexed citations
12.
Wilson, Y.G., et al.. (1997). Duplex assessment of run-off before femorocrural reconstruction. British journal of surgery. 84(10). 1360–1363. 21 indexed citations
13.
Mengshoel, Ole J. & David C. Wilkins. (1996). Recognition and critiquing of erroneous agent actions. National Conference on Artificial Intelligence. 61–68. 1 indexed citations
14.
Wilkins, David C., et al.. (1994). Exploiting the ordering of observed problem-solving steps for knowledge base refinement: an apprenticeship approach. National Conference on Artificial Intelligence. 1443–1443. 1 indexed citations
15.
Wilkins, David C., et al.. (1994). Using apprenticeship techniques to guide constructive induction. 6(3). 295–314. 4 indexed citations
16.
Earnshaw, J J & David C. Wilkins. (1991). Vascular infection: another hazard of listeriosis.. PubMed. 32(4). 475–6. 6 indexed citations
17.
Park, Young-Tack & David C. Wilkins. (1990). Establishing the coherence of an explanation to improve refinement of an incomplete knowledge base. National Conference on Artificial Intelligence. 511–516. 8 indexed citations
18.
Earnshaw, J J, C Cosgrove, David C. Wilkins, & B P Bliss. (1990). Acute limb ischaemia: The place of intravenous streptokinase. British journal of surgery. 77(10). 1136–1139. 6 indexed citations
19.
Wilkins, David C.. (1987). Apprenticeship Learning Techniques for Knowledge Based Systems.. Deep Blue (University of Michigan). 12 indexed citations
20.
Wells, I.P., et al.. (1986). The impact of percutaneous transluminal angioplasty on the management of peripheral vascular disease. British journal of surgery. 73(1). 17–19. 22 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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