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• W2891469329 abstract "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.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 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.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." @default.
• W2891469329 created "2018-09-27" @default.
• W2891469329 creator A5060104570 @default.
• W2891469329 creator A5065389736 @default.
• W2891469329 date "2018-09-01" @default.
• W2891469329 modified "2023-10-01" @default.
• W2891469329 title "Comparison of MetaMap and cTAKES for entity extraction in clinical notes" @default.
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• W2891469329 doi "https://doi.org/10.1186/s12911-018-0654-2" @default.
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