A graph-based method for reconstructing entities from coordination ellipsis in medical text.

Clicks: 200
ID: 109594
2020
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Ranked #33 of 76 articles by views in Journal of the American Medical Informatics Association : JAMIA

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Abstract
Coordination ellipsis is a linguistic phenomenon abound in medical text and is challenging for concept normalization because of difficulty in recognizing elliptical expressions referencing 2 or more entities accurately. To resolve this bottleneck, we aim to contribute a generalizable method to reconstruct concepts from medical coordinated elliptical expressions in a variety of biomedical corpora.We proposed a graph-based representation model and built a pipeline to reconstruct concepts from coordinated elliptical expressions in medical text (RECEEM). There are 4 modules: (1) identify all possible candidate conjunct pairs from original coordinated elliptical expressions, (2) calculate coefficients for candidate conjuncts using the embedding model, (3) select the most appropriate decompositions by global optimization, and (4) rebuild concepts based on a pathfinding algorithm. We evaluated the pipeline's performance on 2658 coordinated elliptical expressions from 3 different medical corpora (ie, biomedical literature, clinical narratives, and eligibility criteria from clinical trials). Precision, recall, and F1 score were calculated.The F1 scores for biomedical publications, clinical narratives, and research eligibility criteria were 0.862, 0.721, and 0.870, respectively. RECEEM outperformed 2 previously released methods. By incorporating RECEEM into 2 existing NLP tools, the F1 scores increased from 0.248 to 0.460 and from 0.287 to 0.630 on concept mapping of 1125 coordination ellipses.RECEEM improves concept normalization for medical coordinated elliptical expressions in a variety of biomedical corpora. It outperformed existing methods and significantly enhanced the performance of 2 notable NLP systems for mapping coordination ellipses in the evaluation. The algorithm is open sourced online (https://github.com/chiyuan1126/RECEEM).
Reference Key
yuan2020ajournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yuan, Chi;Wang, Yongli;Shang, Ning;Li, Ziran;Zhao, Ruxin;Weng, Chunhua;
Journal Journal of the American Medical Informatics Association : JAMIA
Year 2020
DOI
ocaa109
URL
Keywords
natural language processing concept normalization coordination ellipsis

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