Pharmacovigilance in Psychiatry: PsyTAR: a Corpus of Patient-reported Adverse Events - download

A systematic approach for developing a corpus of patient reported adverse drug events: A case study for SSRI and SNRI medications

Corpus download (free): Download PsyTAR Data Set (excel format, zipped) Includes original sentences, mapping, and documentation. Please cite this corpus as:

Zolnoori, M., Fung, K. W., Patrick, T. B., Fontelo, P., Kharrazi, H., Faiola, A., . . . Hamideh, M. (in press). A systematic approach for developing a corpus of patient reported adverse drug events: A case study for SSRI and SNRI medications, Journal of Biomedical Informatics. "
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Zolnoori, M., Fung, K. W., Patrick, T. B., Fontelo, P., Kharrazi, H., Faiola, A., . . . Hamideh, M. (submitted). The PsyTAR dataset: From Social Media Posts to a Corpus of Adverse Drug Events and Effectiveness of Psychiatric Medications, Journal of Data in Brief. "

This corpus is under the CC BY 4.0 Data license. This license allows user to use the data with appropriate attribution to its origin.  Separate terms of use for AskaPatient postings on the "license" tab.

Research Highlights:

We introduce PsyTAR, a corpus of patient expressions of effectiveness and adverse drug events (ADEs) associated with psychiatric medications.

We show that following a systematic approach can significantly improve inter-annotator agreement.

We show that psychological ADEs, cognitive ADEs, and functional problems have higher semantic variability than physiological ADEs and drug indications.

We show that PsyTAR has important implications in improving the performance of text mining and machine learning algorithms in identifying ADEs.

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 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.

Article download: Download PDF (from Science Direct)