AI Article Synopsis
- The main challenge for clinical Named Entity Recognition (NER) is the lack of annotated training data due to technical and legal issues with electronic health records (EHRs).
- This study explores how generating pseudo-data using gazetteering with a neural network model affects clinical NER performance.
- Results indicate that using gazetteers with a higher number of relevant terms leads to better inclusion of proper terms while filtering out unnecessary words like determiners and pronouns.
Article Abstract
One of the primary challenges for clinical Named Entity Recognition (NER) is the availability of annotated training data. Technical and legal hurdles prevent the creation and release of corpora related to electronic health records (EHRs). In this work, we look at the impact of pseudo-data generation on clinical NER using gazetteering utilizing a neural network model. We report that gazetteers can result in the inclusion of proper terms with the exclusion of determiners and pronouns in preceding and middle positions. Gazetteers that had higher numbers of terms inclusive to the original dataset had a higher impact.
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