2 research outputs found
Two Step Joint Model for Drug Drug Interaction Extraction
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.Comment: 8 pages, 6 figure
Attention-Gated Graph Convolutions for Extracting Drug Interaction Information from Drug Labels
Preventable adverse events as a result of medical errors present a growing
concern in the healthcare system. As drug-drug interactions (DDIs) may lead to
preventable adverse events, being able to extract DDIs from drug labels into a
machine-processable form is an important step toward effective dissemination of
drug safety information. In this study, we tackle the problem of jointly
extracting drugs and their interactions, including interaction outcome, from
drug labels. Our deep learning approach entails composing various intermediate
representations including sequence and graph based context, where the latter is
derived using graph convolutions (GC) with a novel attention-based gating
mechanism (holistically called GCA). These representations are then composed in
meaningful ways to handle all subtasks jointly. To overcome scarcity in
training data, we additionally propose transfer learning by pre-training on
related DDI data. Our model is trained and evaluated on the 2018 TAC DDI
corpus. Our GCA model in conjunction with transfer learning performs at 39.20%
F1 and 26.09% F1 on entity recognition (ER) and relation extraction (RE)
respectively on the first official test set and at 45.30% F1 and 27.87% F1 on
ER and RE respectively on the second official test set corresponding to an
improvement over our prior best results by up to 6 absolute F1 points. After
controlling for available training data, our model exhibits state-of-the-art
performance by improving over the next comparable best outcome by roughly three
F1 points in ER and 1.5 F1 points in RE evaluation across two official test
sets