Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning

Abstract

BackgroundPharmacovigilance and drug-safety surveillance are crucial for monitoring adverse drug events (ADEs), but the main ADE-reporting systems such as Food and Drug Administration Adverse Event Reporting System face challenges such as underreporting. Therefore, as complementary surveillance, data on ADEs are extracted from electronic health record (EHR) notes via natural language processing (NLP). As NLP develops, many up-to-date machine-learning techniques are introduced in this field, such as deep learning and multi-task learning (MTL). However, only a few studies have focused on employing such techniques to extract ADEs.ObjectiveWe aimed to design a deep learning model for extracting ADEs and related information such as medications and indications. Since extraction of ADE-related information includes two steps—named entity recognition and relation extraction—our second objective was to improve the deep learning model using multi-task learning between the two steps.MethodsWe employed the dataset from the Medication, Indication and Adverse Drug Events (MADE) 1.0 challenge to train and test our models. This dataset consists of 1089 EHR notes of cancer patients and includes 9 entity types such as Medication, Indication, and ADE and 7 types of relations between these entities. To extract information from the dataset, we proposed a deep-learning model that uses a bidirectional long short-term memory (BiLSTM) conditional random field network to recognize entities and a BiLSTM-Attention network to extract relations. To further improve the deep-learning model, we employed three typical MTL methods, namely, hard parameter sharing, parameter regularization, and task relation learning, to build three MTL models, called HardMTL, RegMTL, and LearnMTL, respectively.ResultsSince extraction of ADE-related information is a two-step task, the result of the second step (ie, relation extraction) was used to compare all models. We used microaveraged precision, recall, and F1 as evaluation metrics. Our deep learning model achieved state-of-the-art results (F1=65.9%), which is significantly higher than that (F1=61.7%) of the best system in the MADE1.0 challenge. HardMTL further improved the F1 by 0.8%, boosting the F1 to 66.7%, whereas RegMTL and LearnMTL failed to boost the performance.ConclusionsDeep learning models can significantly improve the performance of ADE-related information extraction. MTL may be effective for named entity recognition and relation extraction, but it depends on the methods, data, and other factors. Our results can facilitate research on ADE detection, NLP, and machine learning.

Medical Information Extraction in the Age of Deep Learning

“…Wei et al [62] Gligic et al [63] Zeng et al [64] Unanue et al [65] Li et al [66] Wunnava et al [67] Dandala et al [70] Tao et al [71] Chapman et al [72] Unanue et al [65] NE type Word2vec embeddings self-trained on MIMIC-III [58] Word2vec embeddings self-trained on i2b2 2009 [57] word & character embeddings pre-trained on Wikipedia GloVe [53] pre-trained on CommonCrawl + GloVe self-trained on MIMIC-III [58] pre-trained [16] pre-trained on Wikipedia, EHR notes, and PubMed [68,69] pretrained Notes and consists of 1,092 de-identified EHR notes from 21 cancer patients. Each note was annotated with medication information (drug name, dosage, route, frequency, duration), ADEs, indication (symptom as reason for drug administration), other signs and symptoms, severity (of disease/symptom), and relations among those entities, resulting in 79,000 mention annotations.…”

“…Wei et al [62] Tao et al [71] Gligic et al [63] Li et al [66] Dandala et al [70] Chapman et al [72] Wunnava et al [67] Gligic et al [63] Wei et al [62] Tao et al [71] Wunnava et al [67] Chapman et al [72] Li et al [66] Dandala et al [70] Wei et al [62] Gligic et al [63] Tao et al [71] Wunnava et al [67] Li et al [66] Chapman et al [72] Dandala et al [70] Wei et al [62] Li et al [66] Wunnava et al [67] Dandala et al [70] Chapman et al [72] Gligic et al [63] Tao et al [71] Wunnava et al [67] Yang et al [73] Dandala et al [70] Li et al [66] Wei et al [62] Chapman et al [72] Word2vec embeddings self-trained on MIMIC-III [58] (as feature for CRF: GloVe [53] embeddings self-trained on MIMIC-III [58])…”

Detecting Hypoglycemia Incidents Reported in Patients’ Secure Messages: Using Cost-Sensitive Learning and Oversampling to Reduce Data Imbalance

“…There has been active research in using NLP to detect or facilitate manual review of adverse drug events in unstructured electronic health record notes [40,61-64]. The prior work identified adverse events at the entity (eg, medical terms representing side effects of a drug) or relation (eg, a pair of terms that represent a drug and its side effects) level.…”

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

“…Similarly, RNN methods have also been applied to solve problems relating to EHR to understand symptoms and achieve improved health care quality and personalized medication. A combination of bidirectional LSTM and CRF network has been implemented to recognize entities and extract relationship between entities in EHR [93]. To improve the model, multitask learning was included to handle hard parameter sharing, parameter regularization, and task relation learning.…”