Matthew Herland

682 total citations
9 papers, 453 citations indexed

About

Matthew Herland is a scholar working on Artificial Intelligence, Health Information Management and Political Science and International Relations. According to data from OpenAlex, Matthew Herland has authored 9 papers receiving a total of 453 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 6 papers in Health Information Management and 2 papers in Political Science and International Relations. Recurrent topics in Matthew Herland's work include Imbalanced Data Classification Techniques (6 papers), Artificial Intelligence in Healthcare (3 papers) and Medical Coding and Health Information (3 papers). Matthew Herland is often cited by papers focused on Imbalanced Data Classification Techniques (6 papers), Artificial Intelligence in Healthcare (3 papers) and Medical Coding and Health Information (3 papers). Matthew Herland collaborates with scholars based in United States. Matthew Herland's co-authors include Taghi M. Khoshgoftaar, Richard A. Bauder, Randall Wald, Aaron N. Richter and Xingquan Zhu and has published in prestigious journals such as Journal Of Big Data and Health Care Management Science.

In The Last Decade

Matthew Herland

9 papers receiving 421 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Matthew Herland United States	7	281	172	62	46	45	9	453
Bikash Kanti Sarkar India	10	115 0.4×	87 0.5×	54 0.9×	17 0.4×	47 1.0×	33	293
Kudakwashe Dube New Zealand	9	206 0.7×	115 0.7×	41 0.7×	8 0.2×	16 0.4×	33	479
Milan Vukičević Serbia	12	170 0.6×	60 0.3×	83 1.3×	14 0.3×	16 0.4×	27	408
Muhammad Ayaz Malaysia	7	110 0.4×	86 0.5×	138 2.2×	13 0.3×	18 0.4×	12	432
Dennis Toddenroth Germany	11	160 0.6×	122 0.7×	79 1.3×	3 0.1×	29 0.6×	30	448
Felix Köpcke Germany	12	130 0.5×	174 1.0×	74 1.2×	3 0.1×	27 0.6×	12	500
Berry de Bruijn Canada	14	563 2.0×	27 0.2×	72 1.2×	39 0.8×	6 0.1×	26	986
Tsang-Hsiang Cheng Taiwan	13	211 0.8×	35 0.2×	116 1.9×	10 0.2×	15 0.3×	26	430
Bavani Arunasalam Australia	8	127 0.5×	43 0.3×	88 1.4×	9 0.2×	75 1.7×	9	304
Domenico M. Pisanelli Italy	14	297 1.1×	57 0.3×	118 1.9×	4 0.1×	39 0.9×	49	502

Countries citing papers authored by Matthew Herland

Since Specialization

Citations

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

Fields of papers citing papers by Matthew Herland

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthew Herland

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

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Bauder, Richard A., Matthew Herland, & Taghi M. Khoshgoftaar. (2019). Evaluating Model Predictive Performance: A Medicare Fraud Detection Case Study. 9–14. 5 indexed citations

2.

Herland, Matthew, Richard A. Bauder, & Taghi M. Khoshgoftaar. (2019). The effects of class rarity on the evaluation of supervised healthcare fraud detection models. Journal Of Big Data. 6(1). 34 indexed citations

3.

Herland, Matthew, Richard A. Bauder, & Taghi M. Khoshgoftaar. (2018). Approaches for identifying U.S. medicare fraud in provider claims data. Health Care Management Science. 23(1). 2–19. 12 indexed citations

4.

Herland, Matthew, Taghi M. Khoshgoftaar, & Richard A. Bauder. (2018). Big Data fraud detection using multiple medicare data sources. Journal Of Big Data. 5(1). 115 indexed citations

5.

Herland, Matthew, Richard A. Bauder, & Taghi M. Khoshgoftaar. (2017). Medical Provider Specialty Predictions for the Detection of Anomalous Medicare Insurance Claims. 579–588. 24 indexed citations

6.

Bauder, Richard A., Taghi M. Khoshgoftaar, Aaron N. Richter, & Matthew Herland. (2016). Predicting Medical Provider Specialties to Detect Anomalous Insurance Claims. 784–790. 45 indexed citations

7.

Herland, Matthew, Taghi M. Khoshgoftaar, & Randall Wald. (2014). A review of data mining using big data in health informatics. Journal Of Big Data. 1(1). 200 indexed citations

8.

Herland, Matthew, Taghi M. Khoshgoftaar, & Randall Wald. (2013). Survey of Clinical Data Mining Applications on Big Data in Health Informatics. 465–472. 16 indexed citations

9.

Herland, Matthew, et al.. (2013). An Empirical Study of Robustness of Network Centrality Scores in Various Networks and Conditions. 221–228. 2 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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