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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Developing Crowdsourced Training Data Sets for Pharmacovigilance Intelligent Automation.

Alex Gartland1, Andrew Bate2, Jeffery L Painter3

  • 1College of Medicine, University of Central Florida, Orlando, FL, USA.

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|December 23, 2020
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Summary
This summary is machine-generated.

Crowdsourcing accurately annotates social media data for pharmacovigilance (PV) automation, achieving over 90% accuracy in 5% of the time. This method efficiently develops training datasets for machine learning in drug safety.

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Area of Science:

  • Pharmacovigilance and Machine Learning
  • Computational Social Science

Background:

  • The growing volume of individual case safety reports (ICSRs) in pharmacovigilance (PV) necessitates automated solutions.
  • Machine learning (ML) offers potential for automating ICSR analysis, but requires large, accurate training datasets.
  • Crowdsourcing presents a method for generating such datasets from unstructured data sources like social media.

Purpose of the Study:

  • To assess the accuracy and efficiency of using crowdsourcing for developing training datasets for PV automation.
  • To evaluate the feasibility of leveraging crowdsourced annotations of social media data for ML in drug safety.

Main Methods:

  • A reference dataset of 15,490 de-identified social media posts related to 15 drugs and 22 medical topics was curated by PV experts.
  • A subset of these posts was annotated by Amazon Turk users (Turkers).
  • Crowdsourced accuracy, cost-effectiveness (price elasticity), and time efficiency were evaluated against the expert-curated reference dataset.

Main Results:

  • Crowdsourced data curation achieved over 90% accuracy compared to the reference dataset.
  • The process was completed in approximately 5% of the time required for expert curation.
  • Increased pay improved time efficiency but not accuracy; multiple Turker reviews did not significantly enhance accuracy.

Conclusions:

  • Crowdsourcing is a viable and efficient method for generating accurate training datasets to support pharmacovigilance automation.
  • Further research is required to explore the full potential, limitations, and generalizability of crowdsourcing in this domain.