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A systematic approach for developing a corpus of patient reported adverse drug events: A case study for SSRI and SNRI medications. J Biomed Inform 2019 02;90:103091



Pubmed ID




Scopus ID

2-s2.0-85060100705   10 Citations


"Psychiatric Treatment Adverse Reactions" (PsyTAR) corpus is an annotated corpus that has been developed using patients narrative data for psychiatric medications, particularly SSRIs (Selective Serotonin Reuptake Inhibitor) and SNRIs (Serotonin Norepinephrine Reuptake Inhibitor) medications. This corpus consists of three main components: sentence classification, entity identification, and entity normalization. We split the review posts into sentences and labeled them for presence of adverse drug reactions (ADRs) (2168 sentences), withdrawal symptoms (WDs) (438 sentences), sign/symptoms/illness (SSIs) (789 sentences), drug indications (517), drug effectiveness (EF) (1087 sentences), and drug infectiveness (INF) (337 sentences). In the entity identification phase, we identified and extracted ADRs (4813 mentions), WDs (590 mentions), SSIs (1219 mentions), and DIs (792). In the entity normalization phase, we mapped the identified entities to the corresponding concepts in both UMLS (918 unique concepts) and SNOMED CT (755 unique concepts). Four annotators double coded the sentences and the span of identified entities by strictly following guidelines rules developed for this study. We used the PsyTAR sentence classification component to automatically train a range of supervised machine learning classifiers to identifying text segments with the mentions of ADRs, WDs, DIs, SSIs, EF, and INF. SVMs classifiers had the highest performance with F-Score 0.90. We also measured performance of the cTAKES (clinical Text Analysis and Knowledge Extraction System) in identifying patients' expressions of ADRs and WDs with and without adding PsyTAR dictionary to the core dictionary of cTAKES. Augmenting cTAKES dictionary with PsyTAR improved the F-score cTAKES by 25%. The findings imply that PsyTAR has significant implications for text mining algorithms aimed to identify information about adverse drug events and drug effectiveness from patients' narratives data, by linking the patients' expressions of adverse drug events to medical standard vocabularies. The corpus is publicly available at Zolnoori et al. [30].

Author List

Zolnoori M, Fung KW, Patrick TB, Fontelo P, Kharrazi H, Faiola A, Wu YSS, Eldredge CE, Luo J, Conway M, Zhu J, Park SK, Xu K, Moayyed H, Goudarzvand S


Jake Luo Ph.D. Associate Professor; Director, Center for Biomedical Data and Language Processing (BioDLP) in the Health Informatics & Administration department at University of Wisconsin - Milwaukee

MESH terms used to index this publication - Major topics in bold

Adverse Drug Reaction Reporting Systems
Data Collection
Data Mining
Serotonin Uptake Inhibitors
Serotonin and Noradrenaline Reuptake Inhibitors
Systematized Nomenclature of Medicine
Unified Medical Language System