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

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

Comparative performance of two drug interaction screening programmes analysing a cross-sectional prescription dataset of 84,625 psychiatric inpatients

BACKGROUND: Clinical decision support software (CDSS) solutions can automatically identify drug interactions and thereby aim to improve drug safety. However, data on the comparative performance of different CDSS to detect and appropriately classify interactions in real-life prescription datasets is limited. OBJECTIVE: The aim of this study was to compare the results from two different CDSS analysing the pharmacotherapy of a large population of psychiatric inpatients for drug interactions. METHODS: We performed mass analyses of cross-sectional patient-level prescriptions from 84,625 psychiatric inpatients using two CDSS - MediQ and ID PHARMA CHECK(®). Interactions with the highest risk ratings and the most frequent ratings were reclassified according to the Zurich Interaction System (ZHIAS), a multidimensional classification that incorporates the OpeRational ClassificAtion of Drug Interactions (ORCA) and served as a reference standard. RESULTS: MediQ reported 6,133 unique interacting combinations responsible for 270,617 alerts affecting 63,454 patients. ID PHARMA CHECK(®) issued 5,400 interactions and 157,489 alerts in 48,302 patients. Only 2,154 unique interactions were identified by both programmes, but overlap increased with higher risk rating. MediQ reported high-risk interactions in 2.5 % of all patients, compared with 5 % according to ID PHARMA CHECK(®). The positive predictive value for unique major alerts to be (provisionally) contraindicated according to ORCA was higher for MediQ (0.63) than for either of the two ID PHARMA CHECK(®) components (0.42 for hospINDEX and 0.30 for ID MACS). MediQ reported more interactions, and ID PHARMA CHECK(®) tended to classify interactions into a higher risk class, but overall both programmes identified a similar number of (provisionally) contraindicated interactions according to ORCA criteria. Both programmes identified arrhythmia as the most frequent specific risk associated with interactions in psychiatric patients. CONCLUSIONS: CDSS can be used for mass-analysis of prescription data and thereby support quality management. However, in clinical practice CDSS impose an overwhelming alert burden on the prescriber, and prediction of clinical relevance remains a major challenge. Only a small subset of yet to be determined alerts appears suitable for automated display in clinical routine