Two Step Joint Model for Drug Drug Interaction Extraction

TL;DR

This paper presents a novel two-step joint model for extracting drug-drug interactions from drug labels, improving detection accuracy by jointly identifying mentions and relations, and achieving top results in the TAC 2018 challenge.

Contribution

The paper introduces a joint sequence tagging approach that combines mention detection and relation extraction in a unified model, enhancing DDI extraction performance.

Findings

Achieved F-measure of 0.46 in task1

Achieved F-measure of 0.40 in task2

Outperformed existing methods in TAC 2018 challenge

Abstract

When patients need to take medicine, particularly taking more than one kind of drug simultaneously, they should be alarmed that there possibly exists drug-drug interaction. Interaction between drugs may have a negative impact on patients or even cause death. Generally, drugs that conflict with a specific drug (or label drug) are usually described in its drug label or package insert. Since more and more new drug products come into the market, it is difficult to collect such information by manual. We take part in the Drug-Drug Interaction (DDI) Extraction from Drug Labels challenge of Text Analysis Conference (TAC) 2018, choosing task1 and task2 to automatically extract DDI related mentions and DDI relations respectively. Instead of regarding task1 as named entity recognition (NER) task and regarding task2 as relation extraction (RE) task then solving it in a pipeline, we propose a two step joint model to detect DDI and it's related mentions jointly. A sequence tagging system (CNN-GRU encoder-decoder) finds precipitants first and search its fine-grained Trigger and determine the DDI for each precipitant in the second step. Moreover, a rule based model is built to determine the sub-type for pharmacokinetic interation. Our system achieved best result in both task1 and task2. F-measure reaches 0.46 in task1 and 0.40 in task2.