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

Journal: BMC medical research methodology
PMID: 39707270

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.

Authors

  • Junlong Ma
    Center of Clinical Pharmacology, Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
  • Heng Chen
    Medical College, Guizhou University, Jiaxiu Road, Huaxi Zone, Guiyang 550025, P. R. China.
  • Ji Sun
    Department of Pharmacy, The First Hospital of Changsha, Central South University, Changsha, Hunan, China.
  • Juanjuan Huang
    Department of Pharmacy, The First Hospital of Changsha, Central South University, Changsha, Hunan, China.
  • Gefei He
    Department of Pharmacy, The First Hospital of Changsha, Central South University, Changsha, Hunan, China. 326366726@qq.com.
  • Guoping Yang
    Center of Clinical Pharmacology, Third Xiangya Hospital, Central South University, Changsha, Hunan, China. ygp9880@126.com.

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