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BENTO: A Visual Platform for Building Clinical NLP Pipelines Based on CodaLab.

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BENTO simplifies clinical natural language processing (NLP) pipeline creation with a graphical interface. This platform enhances CodaLab by providing pre-trained NLP tools and enabling custom tool integration for reproducible research.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Clinical informatics

Background:

  • CodaLab facilitates collaborative research but has limited support for reusable tools and pipeline composition.
  • Clinical natural language processing (NLP) tasks often involve scattered, multi-step tools, hindering reproducibility and ease of use for researchers.

Purpose of the Study:

  • To introduce BENTO, a graphical user interface (GUI) workflow management platform built on CodaLab.
  • To streamline the creation and application of clinical NLP pipelines for researchers with varying computational expertise.

Main Methods:

  • Developed BENTO as a GUI platform integrating with CodaLab.
  • Incorporated pre-trained clinical NLP tools for common tasks (e.g., tokenization, named entity recognition).
  • Enabled users to create, manage, and compose custom NLP tools and pipelines in a reproducible manner.

Main Results:

  • BENTO provides a user-friendly, WYSIWYG interface for building and deploying clinical NLP pipelines.
  • Researchers can leverage pre-trained tools or integrate their own custom models.
  • The platform supports reproducible research by managing tools and pipelines effectively.

Conclusions:

  • BENTO significantly improves the accessibility and usability of clinical NLP pipeline development.
  • The flexible architecture of BENTO can be adapted for NLP applications in other scientific domains.
  • Facilitates reproducible computational research through an intuitive graphical interface.