Josef Ingenerf

949 total citations
67 papers, 525 citations indexed

About

Josef Ingenerf is a scholar working on Molecular Biology, Artificial Intelligence and Health Information Management. According to data from OpenAlex, Josef Ingenerf has authored 67 papers receiving a total of 525 indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Molecular Biology, 27 papers in Artificial Intelligence and 12 papers in Health Information Management. Recurrent topics in Josef Ingenerf's work include Biomedical Text Mining and Ontologies (35 papers), Semantic Web and Ontologies (24 papers) and linguistics and terminology studies (11 papers). Josef Ingenerf is often cited by papers focused on Biomedical Text Mining and Ontologies (35 papers), Semantic Web and Ontologies (24 papers) and linguistics and terminology studies (11 papers). Josef Ingenerf collaborates with scholars based in Germany, United States and Switzerland. Josef Ingenerf's co-authors include Jürgen Stausberg, Hannes Ulrich, Michael Betzler, Martin Lablans, Jens K. Habermann, Rolf Engelbrecht, Raphael W. Majeed, Frank Golatowski, Stefan Franke and Thorsten Langer and has published in prestigious journals such as Journal of Medical Internet Research, Journal of the American Medical Informatics Association and Applied Sciences.

In The Last Decade

Josef Ingenerf

61 papers receiving 491 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Josef Ingenerf Germany 13 188 181 130 62 61 67 525
Charles N. Mead United States 10 271 1.4× 264 1.5× 141 1.1× 56 0.9× 39 0.6× 24 528
Philipp Daumke Germany 9 331 1.8× 126 0.7× 377 2.9× 45 0.7× 49 0.8× 29 696
Ram Chilukuri United States 7 177 0.9× 100 0.6× 135 1.0× 38 0.6× 54 0.9× 8 558
Georges De Moor Belgium 15 248 1.3× 309 1.7× 279 2.1× 82 1.3× 48 0.8× 51 927
Catalina Martínez-Costa Spain 12 274 1.5× 212 1.2× 256 2.0× 104 1.7× 57 0.9× 39 453
Éric Zapletal France 14 142 0.8× 163 0.9× 144 1.1× 55 0.9× 43 0.7× 34 639
Daniel J. Vreeman United States 17 305 1.6× 274 1.5× 172 1.3× 105 1.7× 19 0.3× 47 684
Omar Bouhaddou United States 14 198 1.1× 306 1.7× 168 1.3× 49 0.8× 28 0.5× 41 591
Hans Åhlfeldt Sweden 15 212 1.1× 198 1.1× 162 1.2× 51 0.8× 47 0.8× 64 976
Beatriz H. Rocha United States 13 120 0.6× 241 1.3× 70 0.5× 43 0.7× 19 0.3× 22 427

Countries citing papers authored by Josef Ingenerf

Since Specialization
Citations

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

Fields of papers citing papers by Josef Ingenerf

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Josef Ingenerf

This figure shows the co-authorship network connecting the top 25 collaborators of Josef Ingenerf. A scholar is included among the top collaborators of Josef Ingenerf 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 Josef Ingenerf. Josef Ingenerf 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.
Ingenerf, Josef, et al.. (2024). PCEtoFHIR: Decomposition of Postcoordinated SNOMED CT Expressions for Storage as HL7 FHIR Resources. JMIR Medical Informatics. 12. e57853–e57853. 2 indexed citations
2.
3.
Ingenerf, Josef, et al.. (2023). Mettertron – Bridging Metadata Repositories and Terminology Servers. Studies in health technology and informatics. 307. 243–248. 1 indexed citations
4.
Rinaldi, Eugenia, Friederike D. von Loewenich, Claas Baier, et al.. (2023). Towards interoperability in infection control: a standard data model for microbiology. Scientific Data. 10(1). 654–654. 5 indexed citations
5.
Majeed, Raphael W., et al.. (2022). Generation of a Fast Healthcare Interoperability Resources (FHIR)-based Ontology for Federated Feasibility Queries in the Context of COVID-19: Feasibility Study. JMIR Medical Informatics. 10(4). e35789–e35789. 15 indexed citations
6.
Ulrich, Hannes, Martin Lablans, Raphael W. Majeed, et al.. (2021). Understanding the Nature of Metadata: Systematic Review. Journal of Medical Internet Research. 24(1). e25440–e25440. 35 indexed citations
7.
Ulrich, Hannes, et al.. (2020). A Smart Mapping Editor for Standardised Data Transformation. Studies in health technology and informatics. 270. 1185–1186. 4 indexed citations
8.
Ingenerf, Josef, et al.. (2020). The LOINC Content Model and Its Limitations of Usage in the Laboratory Domain. Studies in health technology and informatics. 270. 437–442. 9 indexed citations
9.
Franke, Stefan, et al.. (2019). Enabling artificial intelligence in high acuity medical environments. Minimally Invasive Therapy & Allied Technologies. 28(2). 120–126. 11 indexed citations
10.
Ingenerf, Josef, et al.. (2019). MDRCupid: A Configurable Metadata Matching Toolbox. Studies in health technology and informatics. 264. 88–92. 3 indexed citations
11.
Ingenerf, Josef, et al.. (2019). Aggregation and Visualization of Laboratory Data by Using Ontological Tools Based on LOINC and SNOMED CT. Studies in health technology and informatics. 264. 108–112. 4 indexed citations
12.
Bruland, Philipp, et al.. (2019). Scientific Challenge in eHealth: MAPPATHON, a Metadata Mapping Challenge. Studies in health technology and informatics. 264. 1516–1517. 1 indexed citations
13.
Ingenerf, Josef, et al.. (2019). Service-Oriented Device Connectivity: Device Specialisations for Interoperability. Studies in health technology and informatics. 264. 509–511. 5 indexed citations
14.
Ulrich, Hannes, et al.. (2018). A Method to Use Metadata in Legacy Web Applications: The Samply.MDR.Injector. Studies in health technology and informatics. 253. 45–49. 1 indexed citations
15.
Schulz, Stefan, Josef Ingenerf, Sylvia Thun, & Philipp Daumke. (2013). German-Language Content in Biomedical Vocabularies.. CLEF (Working Notes). 5 indexed citations
16.
Ingenerf, Josef, et al.. (2012). Standardizing Intensive Care Device Data to Enable Secondary Usages. Studies in health technology and informatics. 180. 619–23. 2 indexed citations
17.
Ingenerf, Josef. (2007). Terminologien oder Klassifikationen: Was bringt die Zukunft?. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz. 50(8). 1070–1083. 2 indexed citations
18.
Ingenerf, Josef. (2007). Terminologien oder Klassifikationen. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz. 50(8). 1070–1083. 5 indexed citations
19.
Ingenerf, Josef. (1999). Telemedicine and terminology: different needs of context information. IEEE Transactions on Information Technology in Biomedicine. 3(2). 92–100. 23 indexed citations
20.
Ingenerf, Josef, et al.. (1997). Notwendigkeit und Funktionalität eines Terminologieservers in der Medizin.. Künstliche Intell.. 11. 6–14. 3 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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