Unknown

Dataset Information

0

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts.


ABSTRACT: 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.

SUBMITTER: Caufield JH 

PROVIDER: S-EPMC6235242 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

altmetric image

Publications

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts.

Caufield John Harry JH   Liem David A DA   Garlid Anders O AO   Zhou Yijiang Y   Watson Karol K   Bui Alex A T AAT   Wang Wei W   Ping Peipei P  

Journal of visualized experiments : JoVE 20180920 139


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 co  ...[more]

Similar Datasets

| S-EPMC6244181 | biostudies-literature
| S-EPMC11244985 | biostudies-literature
| S-EPMC9879259 | biostudies-literature
| S-EPMC8753304 | biostudies-literature
| S-EPMC5751806 | biostudies-literature
| S-EPMC8790684 | biostudies-literature
| S-EPMC10805696 | biostudies-literature
| S-EPMC3238286 | biostudies-literature
| S-EPMC2663782 | biostudies-literature
| S-EPMC5400040 | biostudies-literature