Citations

Abstract Objectives To enable users with modest technical background to perform biomedical natural language processing (NLP). Materials and Methods We developed medspacyV using the Python graphical programming tkinter library, following the model-view-controller (MVC) design pattern. The interface wraps around a rule-based pipeline for sentence splitting, section segmentation, concept identification, and negation detection. Results The primary window allows the user to configure the project and NLP rules, execute the pipeline, and save the outputs into a table. A separate annotation viewer window can be launched to inspect the immediate or previous NLP outputs. Discussion We developed medspacyV with three rationales: controllability, explainability, and economy. The rule-based approach is sufficient for many NLP use cases. Conclusion The medspacyV program is publicly available at https://github.com/medspacy/medspacyV, targeting use by healthcare professionals and researchers in their NLP projects.