Automatic Extraction of Comprehensive Drug Safety Information from Adverse Drug Event Narratives in the Korea Adverse Event Reporting System Using Natural Language Processing Techniques

Abstract

Introduction: Concerns have been raised over the quality of drug safety information, particularly data completeness, collected through spontaneous reporting systems (SRS), although regulatory agencies routinely use SRS data to guide their pharmacovigilance programs. We expected that collecting additional drug safety information from adverse event (ADE) narratives and incorporating it into the SRS database would improve data completeness.

Objective: The aims of this study were to define the extraction of comprehensive drug safety information from ADE narratives reported through the Korea Adverse Event Reporting System (KAERS) as natural language processing (NLP) tasks and to provide baseline models for the defined tasks.

Methods: This study used ADE narratives and structured drug safety information from individual case safety reports (ICSRs) reported through KAERS between 1 January 2015 and 31 December 2019. We developed the annotation guideline for the extraction of comprehensive drug safety information from ADE narratives based on the International Conference on Harmonisation (ICH) E2B(R3) guideline and manually annotated 3723 ADE narratives. Then, we developed a domain-specific Korean Bidirectional Encoder Representations from Transformers (KAERS-BERT) model using 1.2 million ADE narratives in KAERS and provided baseline models for the task we defined. In addition, we performed an ablation experiment to investigate whether named entity recognition (NER) models were improved when a training dataset contained more diverse ADE narratives.

Results: We defined 21 types of word entities, six types of entity labels, and 49 types of relations to formulate the extraction of comprehensive drug safety information as NLP tasks. We obtained a total of 86,750 entities, 81,828 entity labels, and 45,107 relations from manually annotated ADE narratives. The KAERS-BERT model achieved F1-scores of 83.81 and 76.62% on the NER and sentence extraction tasks, respectively, while outperforming other baseline models on all the NLP tasks we defined except the sentence extraction task. Finally, utilizing the NER model for extracting drug safety information from ADE narratives resulted in an average increase of 3.24% in data completeness for KAERS structured data fields.

Conclusions: We formulated the extraction of comprehensive drug safety information from ADE narratives as NLP tasks and developed the annotated corpus and strong baseline models for the tasks. The annotated corpus and models for extracting comprehensive drug safety information can improve the data quality of an SRS database.

Fig. 1

Illustration of a developing of natural language processing (NLP) models for extracting comprehensive drug safety information from adverse drug event (ADE) narratives reported through the Korea Adverse Event Reporting System (KAERS), and b an example of extracting comprehensive drug safety information from ADE narratives

Fig. 2

The Medical Dictionary for Regulatory Activities (MedDRA) system organ classes (SOCs) distribution of normalized ADE entities in annotated ADE narratives and adverse drug events (ADEs) normalized by reporters in Korea Institute of Drug Safety and Risk Management (KIDS) Korea Adverse Event Reporting System (KAERS) Database (KIDS-KD)

Fig. 3

Comparison of completeness in a mandatory and b non-mandatory data fields structured in the Korea Institute of Drug Safety and Risk Management (KIDS) Korea Adverse Event Reporting System (KAERS) Database (KIDS-KD) before and after extracting drug safety information from adverse drug event (ADE) narratives using natural language processing (NLP) models

Fig. 4

Named entity recognition (NER) performances of the KAERS (Korea Adverse Event Reporting System)-BERT (bidirectional encoder representations from transformers) model on total entities (a) and adverse drug event (ADE) entities (b) by the composition of training dataset. A random only dataset denotes a training dataset consisting of only (340 + M) randomly selected ADE narratives, while ADE + random, indication + random, drug compound + random, and drug product + random datasets represent training datasets consisting of 340 ADE narratives reported with the least reported ADE, indication, drug compound, drug product items plus M randomly selected ADE narratives, respectively