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Despite being a unique source of information on patients' status and diseaseprogression, clinical notes are characterized by high levels of duplication andinformation redundancy. In general domain text, it has been shown thatdeduplication does not harm language model (LM) pretraining, thus helpingreduce the training cost. Although large LMs have proven to learn medicalknowledge, they still require specialized domain adaptation for improveddownstream clinical tasks. By leveraging large real-world clinical corpora, wefirst provided a fine-grained characterization of duplicates stemming fromcommon writing practices and clinical relevancy. Second, we demonstrated thatdeduplicating clinical text can help clinical LMs encode less redundantinformation in a more efficient manner and do not harm classification tasks viaprompt-based learning.

Clinical Text Deduplication Practices for Efficient Pretraining and Improved Clinical Tasks