Automated information extraction model enhancing traditional Chinese medicine RCT evidence extraction (Evi-BERT): algorithm development and validation.

ABSTRACT:

Background

In the field of evidence-based medicine, randomized controlled trials (RCTs) are of critical importance for writing clinical guidelines and providing guidance to practicing physicians. Currently, RCTs rely heavily on manual extraction, but this method has data breadth limitations and is less efficient.

Objectives

To expand the breadth of data and improve the efficiency of obtaining clinical evidence, here, we introduce an automated information extraction model for traditional Chinese medicine (TCM) RCT evidence extraction.

Methods

We adopt the Evidence-Bidirectional Encoder Representation from Transformers (Evi-BERT) for automated information extraction, which is combined with rule extraction. Eleven disease types and 48,523 research articles from the China

SUBMITTER: Li Y 

PROVIDER: S-EPMC11358118 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature