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While we pay attention to the latest advances in clinical natural languageprocessing (NLP), we can notice some resistance in the clinical andtranslational research community to adopt NLP models due to limitedtransparency, interpretability, and usability. In this study, we proposed anopen natural language processing development framework. We evaluated it throughthe implementation of NLP algorithms for the National COVID CohortCollaborative (N3C). Based on the interests in information extraction fromCOVID-19 related clinical notes, our work includes 1) an open data annotationprocess using COVID-19 signs and symptoms as the use case, 2) acommunity-driven ruleset composing platform, and 3) a synthetic text datageneration workflow to generate texts for information extraction tasks withoutinvolving human subjects. The corpora were derived from texts from threedifferent institutions (Mayo Clinic, University of Kentucky, University ofMinnesota). The gold standard annotations were tested with a singleinstitution's (Mayo) ruleset. This resulted in performances of 0.876, 0.706,and 0.694 in F-scores for Mayo, Minnesota, and Kentucky test datasets,respectively. The study as a consortium effort of the N3C NLP subgroupdemonstrates the feasibility of creating a federated NLP algorithm developmentand benchmarking platform to enhance multi-institution clinical NLP study andadoption. Although we use COVID-19 as a use case in this effort, our frameworkis general enough to be applied to other domains of interest in clinical NLP.

An Open Natural Language Processing Development Framework for EHR-based  Clinical Research: A case demonstration using the National COVID Cohort  Collaborative (N3C)