Don't Mention It? Analyzing User-Generated Content Signals for Early Adverse Drug Event Warnings

Proceedings of the Workshop on Information Technologies and Systems, Dallas 2015

16 Pages Posted: 7 Dec 2016

See all articles by Ahmed Abbasi

Ahmed Abbasi

University of Notre Dame - Mendoza College of Business - IT, Analytics, and Operations Department

Jingjing Li

University of Virginia - McIntire School of Commerce

Sohaib Abbasi

Virginia Commonwealth University (VCU)

Donald Adjeroh

West Virginia University

Marie Abate

West Virginia University

Wanhong Zheng

West Virginia University

Date Written: 2015

Abstract

Post-marketing surveillance entails monitoring, assessing, and detecting adverse events once drugs are approved and released into the market. User-generated content channels such as social media and search query logs are increasingly being leveraged as complementary data sources to traditional databases for post-marketing drug surveillance. However, the existing body of knowledge has leveraged diverse sets of channels, adverse event types, and modeling methods, resulting in varying results and diverging conclusions regarding the viability and efficacy of various online user-generated channels and accompanying modeling methods. The objective of this study is to examine the efficacy and impact of different online user-generated content channels, event characteristics, and event modeling strategies on early detection of adverse drug events. We incorporate a large test bed encompassing millions of tweets, forums postings, and search query logs pertaining to 143 adverse events. We also propose a novel heuristic-based event modeling method capable of improving precision, recall, and timeliness of alerts. Preliminary results shed light on the interplay between user-generated channels and event types, as well as the potential for more robust event modeling methods that go beyond basic mention models. Several current and future research directions are also discussed. The preliminary results reported have important implications for various stakeholder groups, including regulatory agencies, post-marketing monitoring teams, healthcare hedge fund managers, and third-party consumer advocacy groups.

Keywords: Signal Detection, Social Media, Data Mining, Smart Health, Predictive Analytics

Suggested Citation

Abbasi, Ahmed and Li, Jingjing and Abbasi, Sohaib and Adjeroh, Donald and Abate, Marie and Zheng, Wanhong, Don't Mention It? Analyzing User-Generated Content Signals for Early Adverse Drug Event Warnings (2015). Proceedings of the Workshop on Information Technologies and Systems, Dallas 2015 , Available at SSRN: https://ssrn.com/abstract=2880774 or http://dx.doi.org/10.2139/ssrn.2880774

Ahmed Abbasi (Contact Author)

University of Notre Dame - Mendoza College of Business - IT, Analytics, and Operations Department

Notre Dame, IN 46556
United States

Jingjing Li

University of Virginia - McIntire School of Commerce ( email )

P.O. Box 400173
Charlottesville, VA 22904-4173
United States

Sohaib Abbasi

Virginia Commonwealth University (VCU) ( email )

1015 Floyd Avenue
Richmond, VA 23284
United States

Donald Adjeroh

West Virginia University ( email )

PO Box 6025
Morgantown, WV 26506
United States

Marie Abate

West Virginia University ( email )

PO Box 6025
Morgantown, WV 26506
United States

Wanhong Zheng

West Virginia University ( email )

PO Box 6025
Morgantown, WV 26506
United States

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