Efficient analysis of drug interactions in liver injury: a retrospective study leveraging natural language processing and machine learning.

ABSTRACT:

Background

Liver injury from drug-drug interactions (DDIs), notably with anti-tuberculosis drugs such as isoniazid, poses a significant safety concern. Electronic medical records contain comprehensive clinical information and have gained increasing attention as a potential resource for DDI detection. However, a substantial portion of adverse drug reaction (ADR) information is hidden in unstructured narrative text, which has yet to be efficiently harnessed, thereby introducing bias into the research. There is a significant need for an efficient framework for the DDI assessment.

Methods

Using a Chinese natural language processing (NLP) model, we extracted 25,130 adverse drug reaction (ADR) records, dividing them into sets for training an automated normalization model. The trai

SUBMITTER: Ma J 

PROVIDER: S-EPMC11660714 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature