Abstract：
BACKGROUND:Clinical notes such as discharge summaries have a semi- or unstructured format. These documents contain information about diseases, treatments, drugs, etc. Extracting meaningful information from them becomes challenging due to their narrative format. In this context, we aimed to compare the automatic extraction capacity of medical entities using two tools: MetaMap and cTAKES. METHODS:We worked with i2b2 (Informatics for Integrating Biology to the Bedside) Obesity Challenge data. Two experiments were constructed. In the first one, only one UMLS concept related with the diseases annotated was extracted. In the second, some UMLS concepts were aggregated. RESULTS:Results were evaluated with manually annotated medical entities. With the aggregation process the result shows a better improvement. MetaMap had an average of 0.88 in recall, 0.89 in precision, and 0.88 in F-score. With cTAKES, the average of recall, precision and F-score were 0.91, 0.89, and 0.89, respectively. CONCLUSIONS:The aggregation of concepts (with similar and different semantic types) was shown to be a good strategy for improving the extraction of medical entities, and automatic aggregation could be considered in future works.
journal_name
BMC Med Inform Decis Makjournal_title
BMC medical informatics and decision makingauthors
Reátegui R,Ratté Sdoi
10.1186/s12911-018-0654-2subject
Has Abstractpub_date
2018-09-14 00:00:00pages
74issue
Suppl 3issn
1472-6947pii
10.1186/s12911-018-0654-2journal_volume
18pub_type
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