Literature DB >> 22195162

Pattern mining for extraction of mentions of Adverse Drug Reactions from user comments.

Azadeh Nikfarjam1, Graciela H Gonzalez.   

Abstract

Rapid growth of online health social networks has enabled patients to communicate more easily with each other. This way of exchange of opinions and experiences has provided a rich source of information about drugs and their effectiveness and more importantly, their possible adverse reactions. We developed a system to automatically extract mentions of Adverse Drug Reactions (ADRs) from user reviews about drugs in social network websites by mining a set of language patterns. The system applied association rule mining on a set of annotated comments to extract the underlying patterns of colloquial expressions about adverse effects. The patterns were tested on a set of unseen comments to evaluate their performance. We reached to precision of 70.01% and recall of 66.32% and F-measure of 67.96%.

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Mesh:

Year:  2011        PMID: 22195162      PMCID: PMC3243273     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  6 in total

1.  Extraction of adverse drug effects from clinical records.

Authors:  Eiji Aramaki; Yasuhide Miura; Masatsugu Tonoike; Tomoko Ohkuma; Hiroshi Masuichi; Kayo Waki; Kazuhiko Ohe
Journal:  Stud Health Technol Inform       Date:  2010

2.  Adverse drug reaction-related hospitalisations: a nationwide study in The Netherlands.

Authors:  Cornelis S van der Hooft; Miriam C J M Sturkenboom; Kees van Grootheest; Herre J Kingma; Bruno H Ch Stricker
Journal:  Drug Saf       Date:  2006       Impact factor: 5.606

3.  Drug-related deaths: an analysis of the Italian spontaneous reporting database.

Authors:  Roberto Leone; Laura Sottosanti; Maria Luisa Iorio; Carmela Santuccio; Anita Conforti; Vilma Sabatini; Ugo Moretti; Mauro Venegoni
Journal:  Drug Saf       Date:  2008       Impact factor: 5.606

4.  Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study.

Authors:  Xiaoyan Wang; George Hripcsak; Marianthi Markatou; Carol Friedman
Journal:  J Am Med Inform Assoc       Date:  2009-03-04       Impact factor: 4.497

5.  Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies.

Authors:  J Lazarou; B H Pomeranz; P N Corey
Journal:  JAMA       Date:  1998-04-15       Impact factor: 56.272

Review 6.  Hospital admissions associated with adverse drug reactions: a systematic review of prospective observational studies.

Authors:  Chuenjid Kongkaew; Peter R Noyce; Darren M Ashcroft
Journal:  Ann Pharmacother       Date:  2008-07-01       Impact factor: 3.154

  6 in total
  29 in total

1.  Mining Social Media Data for Biomedical Signals and Health-Related Behavior.

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Journal:  Annu Rev Biomed Data Sci       Date:  2020-05-04

2.  Text mining for adverse drug events: the promise, challenges, and state of the art.

Authors:  Rave Harpaz; Alison Callahan; Suzanne Tamang; Yen Low; David Odgers; Sam Finlayson; Kenneth Jung; Paea LePendu; Nigam H Shah
Journal:  Drug Saf       Date:  2014-10       Impact factor: 5.606

Review 3.  Utilizing social media data for pharmacovigilance: A review.

Authors:  Abeed Sarker; Rachel Ginn; Azadeh Nikfarjam; Karen O'Connor; Karen Smith; Swetha Jayaraman; Tejaswi Upadhaya; Graciela Gonzalez
Journal:  J Biomed Inform       Date:  2015-02-23       Impact factor: 6.317

4.  Portable automatic text classification for adverse drug reaction detection via multi-corpus training.

Authors:  Abeed Sarker; Graciela Gonzalez
Journal:  J Biomed Inform       Date:  2014-11-08       Impact factor: 6.317

5.  Semantic processing to identify adverse drug event information from black box warnings.

Authors:  Adam Culbertson; Marcelo Fiszman; Dongwook Shin; Thomas C Rindflesch
Journal:  AMIA Annu Symp Proc       Date:  2014-11-14

6.  HARNESSING SOCIAL MEDIA FOR HEALTH INFORMATION MANAGEMENT.

Authors:  Lina Zhou; Dongsong Zhang; Chris Yang; Yu Wang
Journal:  Electron Commer Res Appl       Date:  2017-12-29       Impact factor: 6.014

7.  Reducing free-text communication orders placed by providers using association rule mining.

Authors:  Zahra Hajihashemi; Paul Pancoast
Journal:  AMIA Annu Symp Proc       Date:  2012-11-03

8.  Towards generating a patient's timeline: extracting temporal relationships from clinical notes.

Authors:  Azadeh Nikfarjam; Ehsan Emadzadeh; Graciela Gonzalez
Journal:  J Biomed Inform       Date:  2013-11-07       Impact factor: 6.317

9.  Pharmacovigilance on twitter? Mining tweets for adverse drug reactions.

Authors:  Karen O'Connor; Pranoti Pimpalkhute; Azadeh Nikfarjam; Rachel Ginn; Karen L Smith; Graciela Gonzalez
Journal:  AMIA Annu Symp Proc       Date:  2014-11-14

10.  RedMed: Extending drug lexicons for social media applications.

Authors:  Adam Lavertu; Russ B Altman
Journal:  J Biomed Inform       Date:  2019-10-15       Impact factor: 6.317

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