Can reproducibility be improved in clinical natural language processing? A study of 7 clinical NLP suites.

ABSTRACT:

Background

The increasing complexity of data streams and computational processes in modern clinical health information systems makes reproducibility challenging. Clinical natural language processing (NLP) pipelines are routinely leveraged for the secondary use of data. Workflow management systems (WMS) have been widely used in bioinformatics to handle the reproducibility bottleneck.

Objective

To evaluate if WMS and other bioinformatics practices could impact the reproducibility of clinical NLP frameworks.

Materials and methods

Based on the literature across multiple researcho fields (NLP, bioinformatics and clinical informatics) we selected articles which (1) review reproducibility practices and (2) highlight a set of rules or guidelines to ensure tool or pipeline rep

SUBMITTER: Digan W 

PROVIDER: S-EPMC7936396 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature