Evaluation of pooling operations in convolutional architectures for drug-drug interaction extraction

Background: Deep Neural Networks (DNN), in particular, Convolutional Neural Networks (CNN), has recently achieved state-of-art results for the task of Drug-Drug Interaction (DDI) extraction. Most CNN architectures incorporate a pooling layer to reduce the dimensionality of the convolution layer output, preserving relevant features and removing irrelevant details. All the previous CNN based systems for DDI extraction used max-pooling layers. Results: In this paper, we evaluate the performance of various pooling methods (in particular max-pooling, average-pooling and attentive pooling), as well as their combination, for the task of DDI extraction. Our experiments show that max-pooling exhibits a higher performance in F1-score (64.56%) than attentive pooling (59.92%) and than average-pooling (58.35%). Conclusions: Max-pooling outperforms the others alternatives because is the only one which is invariant to the special pad tokens that are appending to the shorter sentences known as padding. Actually, the combination of max-pooling and attentive pooling does not improve the performance as compared with the single max-pooling technique.Publication of this article was supported by the Research Program of the Ministry of Economy and Competitiveness - Government of Spain, (DeepEMR project TIN2017-87548-C2-1-R) and the TEAM project (Erasmus Mundus Action 2-Strand 2 Programme) funded by the European Commission

Recommendations for selecting drug-drug interactions for clinical decision support

To recommend principles for including drug-drug interactions (DDIs) in clinical decision support

Antidepressant-Warfarin Interaction and Associated Gastrointestinal Bleeding Risk in a Case-Control Study

Bleeding is the most common and worrisome adverse effect of warfarin therapy. One of the factors that might increase bleeding risk is initiation of interacting drugs that potentiate warfarin. We sought to evaluate whether initiation of an antidepressant increases the risk of hospitalization for gastrointestinal bleeding in warfarin users.Medicaid claims data (1999-2005) were used to perform an observational case-control study nested within person-time exposed to warfarin in those ≥18 years. In total, 430,455 warfarin users contributed 407,370 person-years of warfarin use. The incidence rate of hospitalization for GI bleeding among warfarin users was 4.48 per 100 person-years (95% CI, 4.42-4.55). Each gastrointestinal bleeding cases was matched to 50 controls based on index date and state. Warfarin users had an increased odds ratio of gastrointestinal bleeding upon initiation of citalopram (OR = 1.73 [95% CI, 1.25-2.38]), fluoxetine (OR = 1.63 [95% CI, 1.11-2.38]), paroxetine (OR = 1.64 [95% CI, 1.27-2.12]), amitriptyline (OR = 1.47 [95% CI, 1.02-2.11]). Also mirtazapine, which is not believed to interact with warfarin, increased the risk of GI bleeding (OR = 1.75 [95% CI, 1.30-2.35]).Warfarin users who initiated citalopram, fluoxetine, paroxetine, amitriptyline, or mirtazapine had an increased risk of hospitalization for gastrointestinal bleeding. However, the elevated risk with mirtazapine suggests that a drug-drug interaction may not have been responsible for all of the observed increased risk

A linguistic rule-based approach to extract drug-drug interactions from pharmacological documents

<p>Abstract</p> <p>Background</p> <p>A drug-drug interaction (DDI) occurs when one drug influences the level or activity of another drug. The increasing volume of the scientific literature overwhelms health care professionals trying to be kept up-to-date with all published studies on DDI.</p> <p>Methods</p> <p>This paper describes a hybrid linguistic approach to DDI extraction that combines shallow parsing and syntactic simplification with pattern matching. Appositions and coordinate structures are interpreted based on shallow syntactic parsing provided by the UMLS MetaMap tool (MMTx). Subsequently, complex and compound sentences are broken down into clauses from which simple sentences are generated by a set of simplification rules. A pharmacist defined a set of domain-specific lexical patterns to capture the most common expressions of DDI in texts. These lexical patterns are matched with the generated sentences in order to extract DDIs.</p> <p>Results</p> <p>We have performed different experiments to analyze the performance of the different processes. The lexical patterns achieve a reasonable precision (67.30%), but very low recall (14.07%). The inclusion of appositions and coordinate structures helps to improve the recall (25.70%), however, precision is lower (48.69%). The detection of clauses does not improve the performance.</p> <p>Conclusions</p> <p>Information Extraction (IE) techniques can provide an interesting way of reducing the time spent by health care professionals on reviewing the literature. Nevertheless, no approach has been carried out to extract DDI from texts. To the best of our knowledge, this work proposes the first integral solution for the automatic extraction of DDI from biomedical texts.</p