Phar-LSTM: a pharmacological representation-based LSTM network for drug-drug interaction extraction.

ABSTRACT: Pharmacological drug interactions are among the most common causes of medication errors. Many different methods have been proposed to extract drug-drug interactions from the literature to reduce medication errors over the last few years. However, the performance of these methods can be further improved. In this paper, we present a Pharmacological representation-based Long Short-Term Memory (LSTM) network named Phar-LSTM. In this method, a novel embedding strategy is proposed to extract pharmacological representations from the biomedical literature, and the information related to the target drug is considered. Then, an LSTM-based multi-task learning scheme is introduced to extract features from the different but related tasks according to their corresponding pharmacological representations.

SUBMITTER: Huang M 

PROVIDER: S-EPMC10725669 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature