Machine learning-enabled systematic review on coded healthcare data in heart failure research.

ABSTRACT:

Aims

Coded healthcare data are now commonly used in clinical research. This study aimed to assess the transparency of reporting within heart failure studies and employ machine learning to facilitate larger-scale evaluation.

Methods & results

A systematic search of EMBASE and MEDLINE (2015-2020) identified 4279 heart failure studies with accessible Extensible Markup Language published in the top 25 journals by impact factor. Manual extraction in a random sample of 170 studies by independent human reviewers characterized 40 studies (23.5%) that used coded healthcare data, with 34 of these (85%) reporting doing so and only 19 (47.5%) providing clear descriptions of dataset construction and linkage. Another 420 studies underwent manual annotation to further train a Natural Langu

SUBMITTER: Champsi A 

PROVIDER: S-EPMC12821059 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature