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J Biomed Inform. 2019 Jan 3. pii: S1532-0464(19)30001-2. doi: 10.1016/j.jbi.2018.12.005. [Epub ahead of print]

A Systematic Approach for Developing a Corpus of Patient Reported Adverse Drug Events: A Case Study for SSRI and SNRI Medications.

Author information

1
Department of Health Sciences, University of Wisconsin Milwaukee, Milwaukee, WI; National Library of Medicine, Bethesda, MD.
2
National Library of Medicine, Bethesda, MD.
3
Department of Health Sciences, University of Wisconsin Milwaukee, Milwaukee, WI.
4
Department of Health Policy and Management, Johns Hopkins University, Baltimore, Maryland.
5
Department of Biomedical and Health Information Sciences, University of Illinois at Chicago, Chicago, IL.
6
School of Pharmacy, University of Pittsburgh, Pittsburgh, PA.
7
School of Information, University of South Florida, Tampa, FL.
8
Department of Biomedical Informatics, Utah University, Salt Lake City, UT.
9
Emmes Corporation, Rockville, MD.
10
Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland.
11
School of Computing and Engineering, University of Missouri-Kansas, Kansas City, MO.

Abstract

"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 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 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. (submitted).

KEYWORDS:

Adverse Drug Events; Annotated Corpus; Drug Effectiveness; Drug Safety; Healthcare Forum; Information Extraction; Machine Learning; Patients Narratives; Psychiatric Medications; SNOMED CT; SNRIs; SSRIs; Semantic Mapping; Social Media; Text Mining; UMLS

PMID:
30611893
DOI:
10.1016/j.jbi.2018.12.005

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