Article

Mining officially unrecognized side effects of drugs by combining web search and machine learning

Authors:

Carlo A. Curino,

Patricia M. West,

Clement YuAuthors Info & Claims

CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management

Pages 365 - 372

https://doi.org/10.1145/1099554.1099670

Published: 31 October 2005 Publication History

Abstract

We consider the problem of finding officially unrecognized side effects of drugs. By submitting queries to the Web involving a given drug name, it is possible to retrieve pages concerning the drug. However, many retrieved pages are irrelevant and some relevant pages are not retrieved. More relevant pages can be obtained by adding the active ingredient of the drug to the query. In order to eliminate irrelevant pages, we propose a machine learning process to filter out the undesirable pages. The process is shown experimentally to be very effective. Since obtaining training data for the machine learning process can be time consuming and expensive, we provide an automatic method to generate the training data. The method is also shown to be very accurate. The side effects of three drugs which are not recognized by FDA are validated by an expert. We believe that the same approach can be applied to many real life problems and will yield high precision. Thus, this could lead a new way to perform retrieval with high accuracy.

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Cited By

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https://doi.org/10.31083/j.rcm2204121
Audeh BBellet FBeyens MLillo-Le Louët ABousquet C(2020)Use of Social Media for Pharmacovigilance Activities: Key Findings and Recommendations from the Vigi4Med ProjectDrug Safety10.1007/s40264-020-00951-243:9(835-851)Online publication date: 16-Jun-2020
https://doi.org/10.1007/s40264-020-00951-2
Wu HFang HStanhope S(2018)Exploiting Online Discussions to Discover Unrecognized Drug Side EffectsMethods of Information in Medicine10.3414/ME12-02-000452:02(152-159)Online publication date: 20-Jan-2018
https://doi.org/10.3414/ME12-02-0004
Show More Cited By

Index Terms

Mining officially unrecognized side effects of drugs by combining web search and machine learning
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals

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Published In

cover image ACM Conferences

CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management

October 2005

854 pages

ISBN:1595931406

DOI:10.1145/1099554

General Chair:
Otthein Herzog
University of Bremen, Germany
,
Program Chairs:
Hans-Jörg Schek
University for Health Sciences, Medical Informatics and Technology, Austria
,
Norbert Fuhr
University of Duisburg-Essen, Germany
,
Abdur Chowdhury
America Online, USA
,
Wilfried Teiken
IBM T.J. Watson Research Center, USA

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 31 October 2005

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CIKM05: Conference on Information and Knowledge Management

October 31 - November 5, 2005

Bremen, Germany

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CIKM '05 Paper Acceptance Rate 77 of 425 submissions, 18%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Yasmin FShah SNaeem AShujauddin SJabeen AKazmi SSiddiqui SKumar PSalman SHassan SDasari CChoudhry AMustafa AChawla SLak H(2021)Artificial intelligence in the diagnosis and detection of heart failure: the past, present, and futureReviews in Cardiovascular Medicine10.31083/j.rcm220412122:4Online publication date: 22-Dec-2021
https://doi.org/10.31083/j.rcm2204121
Audeh BBellet FBeyens MLillo-Le Louët ABousquet C(2020)Use of Social Media for Pharmacovigilance Activities: Key Findings and Recommendations from the Vigi4Med ProjectDrug Safety10.1007/s40264-020-00951-243:9(835-851)Online publication date: 16-Jun-2020
https://doi.org/10.1007/s40264-020-00951-2
Wu HFang HStanhope S(2018)Exploiting Online Discussions to Discover Unrecognized Drug Side EffectsMethods of Information in Medicine10.3414/ME12-02-000452:02(152-159)Online publication date: 20-Jan-2018
https://doi.org/10.3414/ME12-02-0004
AKGÜN Z(2016)Veri Madenciliği İle Yazılım Hata TespitiEl-Cezeri Fen ve Mühendislik Dergisi10.31202/ecjse.2641973:2Online publication date: 31-May-2016
https://doi.org/10.31202/ecjse.264197
Gharehchopogh FKhalifelu Z(2011)Neural Network application in diagnosis of patient: A case studyInternational Conference on Computer Networks and Information Technology10.1109/ICCNIT.2011.6020937(245-249)Online publication date: Jul-2011
https://doi.org/10.1109/ICCNIT.2011.6020937

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