Abstract
The annotation of entities with concepts from standardized terminologies and ontologies is of high importance in the life sciences to enhance semantic interoperability, information retrieval and meta-analysis. Unfortunately, medical documents such as clinical forms or electronic health records are still rarely annotated despite the availability of some tools to automatically determine possible annotations. In this study, we comparatively evaluate the quality of two such tools, cTAKES and MetaMap, as well as of a recently proposed annotation approach from our group for annotating medical forms. We also investigate how to improve the match quality of the tools by post-filtering computed annotations as well as by combining several annotation approaches.
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Notes
- 1.
- 2.
- 3.
Clinical Text Analysis and Knowledge Extraction System http://ctakes.apache.org.
- 4.
Unstructured Information Management Architecture [16] https://uima.apache.org.
- 5.
Medical Dictionary for Regulatory Activities.
- 6.
Open-access and Collaborative (OAC) Consumer Health Vocabulary (CHV).
- 7.
US Extension to Systematized Nomenclature of Medicine-Clinical Terms.
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Acknowledgment
This work is funded by the German Research Foundation (DFG) (grant RA 497/22-1, “ELISA - Evolution of Semantic Annotations”), German Federal Ministry of Education and Research (BMBF) (grant 031L0026, “Leipzig Health Atlas”) and National Research Fund Luxembourg (FNR) (grant C13/IS/5809134).
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Lin, YC. et al. (2017). Evaluating and Improving Annotation Tools for Medical Forms. In: Da Silveira, M., Pruski, C., Schneider, R. (eds) Data Integration in the Life Sciences. DILS 2017. Lecture Notes in Computer Science(), vol 10649. Springer, Cham. https://doi.org/10.1007/978-3-319-69751-2_1
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