Efficient analysis of drug interactions in liver injury: a retrospective study leveraging natural language processing and machine learning
Our NLP and machine learning approach effectively identifies isoniazid-related DDIs that increase the risk of liver injury, identifying 20 signal drugs, mainly antibacterials. Further research is required to validate these DDI signals.
Authors
- Ji Sun (ORCID: https://orcid.org/0000-0001-6536-6773)
- Junlong Ma (ORCID: https://orcid.org/0000-0003-1949-1201)
- Guoping Yang (ORCID: https://orcid.org/0000-0001-5930-586X)
- Heng Chen (ORCID: https://orcid.org/0000-0002-1781-9050)
- Juanjuan Huang (ORCID: https://orcid.org/0000-0003-0385-3263)
- Gefei He (ORCID: https://orcid.org/0000-0001-6071-5399)
Institutions
- Central South University (CN)
- Changsha Central Hospital (CN)
- The First Hospital of Changsha (CN)
- Third Xiangya Hospital (CN)
Publication Details
- Journal
- BMC Medical Research Methodology
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1186/s12874-024-02443-8
- Citations
- 7
- Primary Topic
- Drug-Induced Hepatotoxicity and Protection
- Type
- article
- Field-Weighted Citation Impact
- 3.27
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Hunan Province