A dataset of 200 structured product labels annotated for adverse drug reactions

Abstract

Adverse drug reactions (ADRs), unintended and sometimes dangerous effects that a drug may have, are one of the leading causes of morbidity and mortality during medical care. To date, there is no structured machine-readable authoritative source of known ADRs. The United States Food and Drug Administration (FDA) partnered with the National Library of Medicine to create a pilot dataset containing standardised information about known adverse reactions for 200 FDA-approved drugs. The Structured Product Labels (SPLs), the documents FDA uses to exchange information about drugs and other products, were manually annotated for adverse reactions at the mention level to facilitate development and evaluation of text mining tools for extraction of ADRs from all SPLs. The ADRs were then normalised to the Unified Medical Language System (UMLS) and to the Medical Dictionary for Regulatory Activities (MedDRA). We present the curation process and the structure of the publicly available database SPL-ADR-200db containing 5,098 distinct ADRs. The database is available at https://bionlp.nlm.nih.gov/tac2017adversereactions/; the code for preparing and validating the data is available at https://github.com/lhncbc/fda-ars.

CRF-LSTM Text Mining Method Unveiling the Pharmacological Mechanism of Off-target Side Effect of Anti-Multiple Myeloma Drugs

“…Sequence labeling of biomedical entities, e.g., side effects or phenotypes, was a long-term task in BioNLP and MedNLP communities. Thanks to effects made among these communities, adverse reaction NER has developed dramatically in recent years (Demner-Fushman et al, 2018). As an illuminative application, to achieve knowledge discovery via the combination of the text mining result and bioinformatics idea shed lights on the pharmacological mechanism research.…”

“…In this research, we proposed a novel pharmacological knowledge discovery strategy which integrated both Biomedical natural language processing (BioNLP) and medical informatics. The adverse reactions (ADRs) were trained by newly released ADR training data (Demner-Fushman et al, 2018), and were extracted on-line with large-scale of text mining upon 16 anti-MM drugs by using conditioned random field (CRF) and long short term memory (LSTM) neural networks. Subsequently, Human Phenotype Ontology (HPO) (Sebastian et al, 2017) and Ligand Similarity prediction were used to calculate the target phenotypes.…”

A comprehensive review of computational techniques for the prediction of drug side effects

“…Adverse drug reactions extracted data set (Demner-Fushman et al, 2018) The US Food and Drug Administration (FDA) and the National Library of Medicine have collectively aimed to create a structured source of drug side effects that will be available in a machine-readable form. The data set contains standardized data regarding the side effects of 200 FDA approved drugs.…”

“…This requires data about the drug compounds, protein targets, and the side effects to predict the possible drug reactions. The important data sets containing drug side effect information and drug/ protein properties that have been used or may be used in the process of side effect prediction have been mentioned in Table 1 (Kuhn et al, 2015;Zitniket al, 2018;Tatonetti et al, 2012;Demner-Fushman et al, 2018;Dua, & Graff, 2017).…”

Extraction of Lactation Frames from Drug Labels and LactMed

“…This includes frame for molecular biology information (Dolbey et al, 2006;Dolbey, 2009;Tan, 2014), cancer information from EHRs Datta et al, 2017), and general medical information for Swedish (Kokkinakis, 2013). Many other works have implicitly used representations that are similar to frames, including the TAC ADR task data on drug labels Demner-Fushman et al, 2018b).…”