Text mining for the Vaccine Adverse Event Reporting System: medical text classification using informative feature selection

Botsis, Taxiarchis; Nguyen, Michael; Woo, Emily Jane; Markatou, Marianthi; Ball, Robert

doi:10.1136/amiajnl-2010-000022

Cited by 97 publications

(78 citation statements)

References 39 publications

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“…Other textual corpora mined for pharmacovigilance or potential candidates thereof that we were unable to cover, include: narratives of spontaneous reports [93], regulatory documents such as new drug applications (NDA) to the FDA[94], European public assessment reports for medicines[95], regular safety summaries[96, 97], labeling information from drugs@FDA, and clinical trial report narratives[98]. …”

Section: Resultsmentioning

confidence: 99%

Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

et al. 2014

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Text mining is the computational process of extracting meaningful information from large amounts of unstructured text. Text mining is emerging as a tool to leverage underutilized data sources that can improve pharmacovigilance, including the objective of adverse drug event detection and assessment. This article provides an overview of recent advances in pharmacovigilance driven by the application of text mining, and discusses several data sources—such as biomedical literature, clinical narratives, product labeling, social media, and Web search logs—that are amenable to text-mining for pharmacovigilance. Given the state of the art, it appears text mining can be applied to extract useful ADE-related information from multiple textual sources. Nonetheless, further research is required to address remaining technical challenges associated with the text mining methodologies, and to conclusively determine the relative contribution of each textual source to improving pharmacovigilance.

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Section: Resultsmentioning

confidence: 99%

Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

et al. 2014

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“…For example, Lu et al [14] proposed an ontology-enhanced approach for classifying free-text chief complaints (CCs) from the emergency department. Botsis et al [15] employed a multi-level text mining approach for automated text classification of VAERS (Vaccine Adverse Event Reporting System) reports. In order to detect early indications of disease outbreaks from online news, researchers employed text classification in Internet-based biosurveillance projects [16,17].…”

Section: Text Mining In Syndromic Surveillancementioning

confidence: 99%

Filtering big data from social media – Building an early warning system for adverse drug reactions

Yang

Kiang

Shang

2015

Journal of Biomedical Informatics

136

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“…Different methods can be suggested to zoom in on the task-specific relevant information. Often, such mechanisms improve algorithm performance, particularly in situations where training data is limited [12, 13]. This motivates us to look for relevant medical factors, yielding a more concise document representation.…”

Section: Introductionmentioning

confidence: 99%

Counting trees in Random Forests: Predicting symptom severity in psychiatric intake reports

Scheurwegs

Sushil

Tulkens

et al. 2017

Journal of Biomedical Informatics

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The CEGS N-GRID 2016 Shared Task (Filannino, Stubbs, Uzuner (2017)) in Clinical Natural Language Processing introduces the assignment of a severity score to a psychiatric symptom, based on a psychiatric intake report. We present a method that employs the inherent interview-like structure of the report to extract relevant information from the report and generate a representation. The representation consists of a restricted set of psychiatric concepts (and the context they occur in), identified using medical concepts defined in UMLS that are directly related to the psychiatric diagnoses present in the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) ontology. Random Forests provides a generalization of the extracted, case-specific features in our representation. The best variant presented here scored an inverse mean absolute error (MAE) of 80.64%. A concise concept-based representation, paired with identification of concept certainty and scope (family, patient), shows a robust performance on the task.

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Text mining for the Vaccine Adverse Event Reporting System: medical text classification using informative feature selection

Abstract: Our validated results showed the possibility of developing effective medical text classifiers for VAERS reports by combining text mining with informative feature selection; this strategy has the potential to reduce reviewer workload considerably.

Cited by 97 publications

References 39 publications

Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

Filtering big data from social media – Building an early warning system for adverse drug reactions

Counting trees in Random Forests: Predicting symptom severity in psychiatric intake reports

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