A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
| Autor: | John Caufield, Wei Wang, Anders O. Garlid, David A. Liem, Karol E. Watson, Yijiang Zhou, Peipei Ping, Alex A. T. Bui |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Computer science General Chemical Engineering text mining Semantics Health informatics General Biochemistry Genetics and Molecular Biology 03 medical and health sciences Annotation Issue 139 medical informatics Humans clinical case reports Protocol (object-oriented programming) Metadata Information retrieval General Immunology and Microbiology business.industry General Neuroscience Unified Medical Language System This Month in JoVE curation 3. Good health 030104 developmental biology Structured text Workflow annotation data science business |
| Zdroj: | Journal of Visualized Experiments : JoVE |
| ISSN: | 1940-087X |
| DOI: | 10.3791/58392-v |
| Popis: | Clinical case reports (CCRs) are a valuable means of sharing observations and insights in medicine. The form of these documents varies, and their content includes descriptions of numerous, novel disease presentations and treatments. Thus far, the text data within CCRs is largely unstructured, requiring significant human and computational effort to render these data useful for in-depth analysis. In this protocol, we describe methods for identifying metadata corresponding to specific biomedical concepts frequently observed within CCRs. We provide a metadata template as a guide for document annotation, recognizing that imposing structure on CCRs may be pursued by combinations of manual and automated effort. The approach presented here is appropriate for organization of concept-related text from a large literature corpus (e.g., thousands of CCRs) but may be easily adapted to facilitate more focused tasks or small sets of reports. The resulting structured text data includes sufficient semantic context to support a variety of subsequent text analysis workflows: meta-analyses to determine how to maximize CCR detail, epidemiological studies of rare diseases, and the development of models of medical language may all be made more realizable and manageable through the use of structured text data. |
| Databáze: | OpenAIRE |
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