Melissa Castine

466 total citations
10 papers, 333 citations indexed

About

Melissa Castine is a scholar working on Artificial Intelligence, Molecular Biology and Family Practice. According to data from OpenAlex, Melissa Castine has authored 10 papers receiving a total of 333 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 5 papers in Molecular Biology and 5 papers in Family Practice. Recurrent topics in Melissa Castine's work include Biomedical Text Mining and Ontologies (5 papers), Clinical Reasoning and Diagnostic Skills (5 papers) and Innovations in Medical Education (2 papers). Melissa Castine is often cited by papers focused on Biomedical Text Mining and Ontologies (5 papers), Clinical Reasoning and Diagnostic Skills (5 papers) and Innovations in Medical Education (2 papers). Melissa Castine collaborates with scholars based in United States, Ukraine and United Kingdom. Melissa Castine's co-authors include Rebecca S. Crowley, Eugene Tseytlin, Olga Medvedeva, Elizabeth Legowski, D.M. Jukic, Girish Chavan, Harry Hochheiser, Rebecca S. Jacobson, Guergana Savova and Michael D. Feldman and has published in prestigious journals such as Cancer Research, Journal of the American Medical Informatics Association and Archives of Pathology & Laboratory Medicine.

In The Last Decade

Melissa Castine

10 papers receiving 318 citations

Peers

Melissa Castine

AI

MB

FP

PHEOH

RNMI

Elizabeth Legowski United States

Olga Medvedeva United States

Amin Beigzadeh Iran

Ethan Goh United States

Martin Komenda Czechia

J. Mark Tuthill United States

Carl Preiksaitis United States

Daniel Poon United Kingdom

Jan Clusmann Germany

Michaela Unger Germany

Elizabeth Legowski United States

Melissa Castine

202 ×1.2

AI

110 ×0.9

MB

86 ×1.3

FP

77 ×1.2

PHEOH

56 ×1.0

RNMI

Citations per year, relative to Melissa Castine Melissa Castine (= 1×) peers Elizabeth Legowski

Countries citing papers authored by Melissa Castine

Since Specialization

Citations

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

Fields of papers citing papers by Melissa Castine

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Melissa Castine

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

All Works

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10 of 10 papers shown

1.

Savova, Guergana, Eugene Tseytlin, Sean Finan, et al.. (2017). DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records. Cancer Research. 77(21). e115–e118. 65 indexed citations

2.

Savova, Guergana, Eugene Tseytlin, Sean Finan, et al.. (2017). DeepPhe - A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records.. AMIA. 1 indexed citations

3.

Hochheiser, Harry, Melissa Castine, David J. Harris, Guergana Savova, & Rebecca S. Jacobson. (2016). An information model for computable cancer phenotypes. BMC Medical Informatics and Decision Making. 16(1). 121–121. 28 indexed citations

4.

Conway, Mike, et al.. (2016). Developing a web-based SKOS editor. Journal of Biomedical Semantics. 7(1). 1 indexed citations

5.

Crowley, Rebecca S., Elizabeth Legowski, Olga Medvedeva, et al.. (2012). Automated detection of heuristics and biases among pathologists in a computer-based system. Advances in Health Sciences Education. 18(3). 343–363. 41 indexed citations

6.

Mello‐Thoms, Claudia, C.A.B. Mello, Olga Medvedeva, et al.. (2012). Perceptual Analysis of the Reading of Dermatopathology Virtual Slides by Pathology Residents. Archives of Pathology & Laboratory Medicine. 136(5). 551–562. 24 indexed citations

7.

Crowley, Rebecca S., et al.. (2010). caTIES: a grid based system for coding and retrieval of surgical pathology reports and tissue specimens in support of translational research. Journal of the American Medical Informatics Association. 17(3). 253–264. 73 indexed citations

8.

Payne, Velma L., Olga Medvedeva, Elizabeth Legowski, et al.. (2009). Effect of a limited-enforcement intelligent tutoring system in dermatopathology on student errors, goals and solution paths. Artificial Intelligence in Medicine. 47(3). 175–197. 25 indexed citations

9.

Azevedo, Roger, Melissa Castine, Velma L. Payne, et al.. (2009). Factors affecting feeling-of-knowing in a medical intelligent tutoring system: the role of immediate feedback as a metacognitive scaffold. Advances in Health Sciences Education. 15(1). 9–30. 39 indexed citations

10.

Tseytlin, Eugene, Elizabeth Legowski, D.M. Jukic, et al.. (2007). A natural language intelligent tutoring system for training pathologists: implementation and evaluation. Advances in Health Sciences Education. 13(5). 709–722. 36 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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