David C. Wilkins

1.2k citations
49 papers · 651 indexed · h-index 16
Topics
AI-based Problem Solving and Planning (19 papers)Bayesian Modeling and Causal Inference (14 papers)Machine Learning and Algorithms (6 papers)

In The Last Decade

David C. Wilkins

46 papers receiving 592 citations

Peers

David C. Wilkins
Comparison fields: 5 of 92
  • Surgery 322
  • Artificial Intelligence 203
  • Pulmonary and Respiratory Medicine 168
  • Internal Medicine 75
  • Anesthesiology and Pain Medicine 65
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Citations per field
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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
#WorkIndexed citations
1 25
2 22
3 13
4 18
5
Supervisory Control System for Ship Damage Control: Volume 4 - Intelligent Reasoning
1
6 7
7
Automated instructor assistant for ship damage control
21
8 38
9 53
10
Bayesian network models for generation of crisis management training scenarios
15
11 3
12 2
13
Recognition and critiquing of erroneous agent actions
1
14
Varicose vein surgery: patient satisfaction.
32
15
Exploiting the ordering of observed problem-solving steps for knowledge base refinement: an apprenticeship approach
1
16 4
17
Vascular infection: another hazard of listeriosis.
6
18
Establishing the coherence of an explanation to improve refinement of an incomplete knowledge base
8
19
Apprenticeship Learning Techniques for Knowledge Based Systems.
12
20 22

About David C. Wilkins

David C. Wilkins is a scholar working on Artificial Intelligence, Anesthesiology and Pain Medicine and Management Information Systems, having authored 49 papers that have together received 651 indexed citations. Recurring topics across this work include AI-based Problem Solving and Planning (19 papers), Bayesian Modeling and Causal Inference (14 papers) and Machine Learning and Algorithms (6 papers). The work is most often cited by research in Internal Medicine (75 citations), Anesthesiology and Pain Medicine (65 citations) and Surgery (322 citations). David C. Wilkins has collaborated with scholars based in United States, United Kingdom and Canada. Frequent 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. Their work appears in journals such as British journal of surgery, Artificial Intelligence and IEEE Transactions on Knowledge and Data Engineering.

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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