Development of an Adverse Drug Reaction Corpus from Consumer Health Posts

Maryam Zolnoori; Timothy B Patrick; Kin Wah Fung; Anthony Faiola; Yi Shuan Shirley Wu; Jiaxi Zhu; Christina Eldredge

Development of an Adverse Drug Reaction Corpus from Consumer Health Posts

Maryam Zolnoori, Timothy B Patrick, Kin Wah Fung, Anthony Faiola, Yi Shuan Shirley Wu, Jiaxi Zhu, Christina Eldredge

University of South Florida

Research output: Contribution to conference › Presentation

Abstract

UWM-Adverse Drug Events Corpus (UWM-ADEC) is an annotated corpus that has been developed from consumer drug review posts in social media. In this corpus, we identified four types of Adverse Drug Reactions (ADRs) including physiological, psychological, cognitive, and functional problems. Additionally, we mapped the ADRs to corresponding concepts in Unified medical language Systems (UMLS). The quality of the corpus was measured using well-defined guidelines, double coding, high inter-annotator agreement, and final reviews by pharmacists and clinical terminologists. This corpus is a valuable source for research in the area of text mining and machine learning for ADRs identifications from consumer health posts, specifically for psychiatric medications

Original language	American English
State	Published - Nov 1 2017
Event	SMM4H 2017 Social Media Mining for Health Research and Applications - Duration: Nov 1 2017 → …

Conference

Conference	SMM4H 2017 Social Media Mining for Health Research and Applications
Period	11/1/17 → …

Disciplines

Medicine and Health Sciences

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Development of an Adverse Drug Reaction Corpus from Consumer HealFinal published version, 654 KBLicense: CC BY

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title = "Development of an Adverse Drug Reaction Corpus from Consumer Health Posts",

abstract = " UWM-Adverse Drug Events Corpus (UWM-ADEC) is an annotated corpus that has been developed from consumer drug review posts in social media. In this corpus, we identified four types of Adverse Drug Reactions (ADRs) including physiological, psychological, cognitive, and functional problems. Additionally, we mapped the ADRs to corresponding concepts in Unified medical language Systems (UMLS). The quality of the corpus was measured using well-defined guidelines, double coding, high inter-annotator agreement, and final reviews by pharmacists and clinical terminologists. This corpus is a valuable source for research in the area of text mining and machine learning for ADRs identifications from consumer health posts, specifically for psychiatric medications",

author = "Maryam Zolnoori and Patrick, {Timothy B} and Fung, {Kin Wah} and Anthony Faiola and Wu, {Yi Shuan Shirley} and Jiaxi Zhu and Christina Eldredge",

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day = "1",

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TY - CONF

T1 - Development of an Adverse Drug Reaction Corpus from Consumer Health Posts

AU - Zolnoori, Maryam

AU - Patrick, Timothy B

AU - Fung, Kin Wah

AU - Faiola, Anthony

AU - Wu, Yi Shuan Shirley

AU - Zhu, Jiaxi

AU - Eldredge, Christina

PY - 2017/11/1

Y1 - 2017/11/1

N2 - UWM-Adverse Drug Events Corpus (UWM-ADEC) is an annotated corpus that has been developed from consumer drug review posts in social media. In this corpus, we identified four types of Adverse Drug Reactions (ADRs) including physiological, psychological, cognitive, and functional problems. Additionally, we mapped the ADRs to corresponding concepts in Unified medical language Systems (UMLS). The quality of the corpus was measured using well-defined guidelines, double coding, high inter-annotator agreement, and final reviews by pharmacists and clinical terminologists. This corpus is a valuable source for research in the area of text mining and machine learning for ADRs identifications from consumer health posts, specifically for psychiatric medications

AB - UWM-Adverse Drug Events Corpus (UWM-ADEC) is an annotated corpus that has been developed from consumer drug review posts in social media. In this corpus, we identified four types of Adverse Drug Reactions (ADRs) including physiological, psychological, cognitive, and functional problems. Additionally, we mapped the ADRs to corresponding concepts in Unified medical language Systems (UMLS). The quality of the corpus was measured using well-defined guidelines, double coding, high inter-annotator agreement, and final reviews by pharmacists and clinical terminologists. This corpus is a valuable source for research in the area of text mining and machine learning for ADRs identifications from consumer health posts, specifically for psychiatric medications

UR - https://digitalcommons.usf.edu/si_facpub/444

M3 - Presentation

T2 - SMM4H 2017 Social Media Mining for Health Research and Applications

Y2 - 1 November 2017

ER -

Development of an Adverse Drug Reaction Corpus from Consumer Health Posts

Abstract

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