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Scaling drug indication curation through crowdsourcing

Motivated by the high cost of human curation of biological databases, there is an increasing interest in using computational approaches to assist human curators and accelerate the manual curation process. Towards the goal of cataloging drug indications from FDA drug labels, we recently developed Lab...

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Autores principales: Khare, Ritu, Burger, John D., Aberdeen, John S., Tresner-Kirsch, David W., Corrales, Theodore J., Hirchman, Lynette, Lu, Zhiyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4369375/
https://www.ncbi.nlm.nih.gov/pubmed/25797061
http://dx.doi.org/10.1093/database/bav016
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author Khare, Ritu
Burger, John D.
Aberdeen, John S.
Tresner-Kirsch, David W.
Corrales, Theodore J.
Hirchman, Lynette
Lu, Zhiyong
author_facet Khare, Ritu
Burger, John D.
Aberdeen, John S.
Tresner-Kirsch, David W.
Corrales, Theodore J.
Hirchman, Lynette
Lu, Zhiyong
author_sort Khare, Ritu
collection PubMed
description Motivated by the high cost of human curation of biological databases, there is an increasing interest in using computational approaches to assist human curators and accelerate the manual curation process. Towards the goal of cataloging drug indications from FDA drug labels, we recently developed LabeledIn, a human-curated drug indication resource for 250 clinical drugs. Its development required over 40 h of human effort across 20 weeks, despite using well-defined annotation guidelines. In this study, we aim to investigate the feasibility of scaling drug indication annotation through a crowdsourcing technique where an unknown network of workers can be recruited through the technical environment of Amazon Mechanical Turk (MTurk). To translate the expert-curation task of cataloging indications into human intelligence tasks (HITs) suitable for the average workers on MTurk, we first simplify the complex task such that each HIT only involves a worker making a binary judgment of whether a highlighted disease, in context of a given drug label, is an indication. In addition, this study is novel in the crowdsourcing interface design where the annotation guidelines are encoded into user options. For evaluation, we assess the ability of our proposed method to achieve high-quality annotations in a time-efficient and cost-effective manner. We posted over 3000 HITs drawn from 706 drug labels on MTurk. Within 8 h of posting, we collected 18 775 judgments from 74 workers, and achieved an aggregated accuracy of 96% on 450 control HITs (where gold-standard answers are known), at a cost of $1.75 per drug label. On the basis of these results, we conclude that our crowdsourcing approach not only results in significant cost and time saving, but also leads to accuracy comparable to that of domain experts. Database URL: ftp://ftp.ncbi.nlm.nih.gov/pub/lu/LabeledIn/Crowdsourcing/.
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spelling pubmed-43693752015-04-17 Scaling drug indication curation through crowdsourcing Khare, Ritu Burger, John D. Aberdeen, John S. Tresner-Kirsch, David W. Corrales, Theodore J. Hirchman, Lynette Lu, Zhiyong Database (Oxford) Original Article Motivated by the high cost of human curation of biological databases, there is an increasing interest in using computational approaches to assist human curators and accelerate the manual curation process. Towards the goal of cataloging drug indications from FDA drug labels, we recently developed LabeledIn, a human-curated drug indication resource for 250 clinical drugs. Its development required over 40 h of human effort across 20 weeks, despite using well-defined annotation guidelines. In this study, we aim to investigate the feasibility of scaling drug indication annotation through a crowdsourcing technique where an unknown network of workers can be recruited through the technical environment of Amazon Mechanical Turk (MTurk). To translate the expert-curation task of cataloging indications into human intelligence tasks (HITs) suitable for the average workers on MTurk, we first simplify the complex task such that each HIT only involves a worker making a binary judgment of whether a highlighted disease, in context of a given drug label, is an indication. In addition, this study is novel in the crowdsourcing interface design where the annotation guidelines are encoded into user options. For evaluation, we assess the ability of our proposed method to achieve high-quality annotations in a time-efficient and cost-effective manner. We posted over 3000 HITs drawn from 706 drug labels on MTurk. Within 8 h of posting, we collected 18 775 judgments from 74 workers, and achieved an aggregated accuracy of 96% on 450 control HITs (where gold-standard answers are known), at a cost of $1.75 per drug label. On the basis of these results, we conclude that our crowdsourcing approach not only results in significant cost and time saving, but also leads to accuracy comparable to that of domain experts. Database URL: ftp://ftp.ncbi.nlm.nih.gov/pub/lu/LabeledIn/Crowdsourcing/. Oxford University Press 2015-03-22 /pmc/articles/PMC4369375/ /pubmed/25797061 http://dx.doi.org/10.1093/database/bav016 Text en Published by Oxford University Press 2015. This work is written by US Government employees and is in the public domain in the US.
spellingShingle Original Article
Khare, Ritu
Burger, John D.
Aberdeen, John S.
Tresner-Kirsch, David W.
Corrales, Theodore J.
Hirchman, Lynette
Lu, Zhiyong
Scaling drug indication curation through crowdsourcing
title Scaling drug indication curation through crowdsourcing
title_full Scaling drug indication curation through crowdsourcing
title_fullStr Scaling drug indication curation through crowdsourcing
title_full_unstemmed Scaling drug indication curation through crowdsourcing
title_short Scaling drug indication curation through crowdsourcing
title_sort scaling drug indication curation through crowdsourcing
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4369375/
https://www.ncbi.nlm.nih.gov/pubmed/25797061
http://dx.doi.org/10.1093/database/bav016
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