Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods

Abstract

Objective Identification of drugs, associated medication entities, and interactions among them are crucial to prevent unwanted effects of drug therapy, known as adverse drug events. This article describes our participation to the n2c2 shared-task in extracting relations between medication-related entities in electronic health records. Materials and Methods We proposed an ensemble approach for relation extraction and classification between drugs and medication-related entities. We incorporated state-of-the-art named-entity recognition (NER) models based on bidirectional long short-term memory (BiLSTM) networks and conditional random fields (CRF) for end-to-end extraction. We additionally developed separate models for intra- and inter-sentence relation extraction and combined them using an ensemble method. The intra-sentence models rely on bidirectional long short-term memory networks and attention mechanisms and are able to capture dependencies between multiple related pairs in the same sentence. For the inter-sentence relations, we adopted a neural architecture that utilizes the Transformer network to improve performance in longer sequences. Results Our team ranked third with a micro-averaged F1 score of 94.72% and 87.65% for relation and end-to-end relation extraction, respectively (Tracks 2 and 3). Our ensemble effectively takes advantages from our proposed models. Analysis of the reported results indicated that our proposed approach is more generalizable than the top-performing system, which employs additional training data- and corpus-driven processing techniques. Conclusions We proposed a relation extraction system to identify relations between drugs and medication-related entities. The proposed approach is independent of external syntactic tools. Analysis showed that by using latent Drug-Drug interactions we were able to significantly improve the performance of non–Drug-Drug pairs in EHRs.

Medical Information Extraction in the Age of Deep Learning

“…Wei et al [62] Christopoulou et al [74] Chapman et al [72] Dandala et al [70] Wei et al [62] Christopoulou et al [74] Dandala et al [70] Chapman et al [72] Wei et al [62] Christopoulou et al [74] Chapman et al [72] Dandala et al [70] Wei et al [62] Chapman et al [72] Christopoulou et al [74] Dandala et al [70] Wei et al [62] Christopoulou et al [74] Chapman et al [72] Dandala et al [70] Wei et al [62] Christopoulou et al [74] Dandala et al [70] Yang et al [73] Relation type (thus diverging from the most successful architectures for medication NER; see Tables 3 and 4) and rule-based post-processing that outperformed a simple CNN-RNN. Summarizing, the CNN-RNN approach seems more favorable than an (attention-based) BiLSTM, with preferences for self-trained in-domain embeddings.…”

Women’s health inThe BMJ: a data science history

“…More sophisticated text mining tools might also allow the content of articles to be explored in a more nuanced way. 64 65 We strongly encourage all journals to require all data be disaggregated by sex and aim to maintain a balance in topics with attention for sex-specific differences. Finally, we note that we have made our code and data public ( https://github.com/Armand1/Women-in-the-BMJ-public ).…”

Hybrid Deep Learning for Medication-Related Information Extraction From Clinical Texts in French: MedExt Algorithm Development Study

“…This data set contained 16,225 drug mentions in the training set and a total of 50,951 entity annotations again in the training set. Among the best-performing algorithms, bidirectional long short term memory and bidirectional long short term memory with conditional random field architectures were popular [24][25][26][27]. Some systems combined attention mechanisms [28] or convolutional neural networks [27].…”

“…Others combined classic entity extraction systems such as cTakes with classifiers such as support vector machines [29]. Ensemble approaches, combining multiple classifiers were also proposed [24][25][26]30].…”