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Exploring machine learning techniques using patient interactions in online health forums to classify drug safety

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Title: Exploring machine learning techniques using patient interactions in online health forums to classify drug safety
Author(s): Chee, Brant
Advisor(s): Schatz, Bruce
Contributor(s): Schatz, Bruce; Karahalios, Karrie; Blake, Catherine
Department / Program: Library & Information Science
Discipline: Library & Information Science
Degree Granting Institution: University of Illinois at Urbana-Champaign
Degree: Ph.D.
Genre: Doctoral
Subject(s): Machine Learning Natural Language Processing Adverse Drug Events Pharmacovigilance
Abstract: This dissertation explores the use of personal health messages collected from online message forums to predict drug safety using natural language processing and machine learning techniques. Drug safety is defined as any drug with an active safety alert from the US Food and Drug Administration (FDA). It is believed that this is the first exploration of patient derived data of this type for pharmacovigilance – the study of drugs once released to market for safety. It is believed that this is the first application of machine learning and natural language processing techniques to be used for pharmicovigilance on patient derived data. We present results demonstrating the identification of drugs withdrawn from market as well as predictions of other potential safety alert drugs. One example includes Meridia, a weight loss drug linked with death for those with cardiovascular disease. The drug is identified based on data presented two years before FDA and European Union (EU) advisory panels were formed and the subsequent withdrawal of the drug from market within the EU and United States.
Issue Date: 2012-02-06
Genre: thesis
URI: http://hdl.handle.net/2142/29787
Rights Information: Copyright 2011 Brant Chee
Date Available in IDEALS: 2012-02-06
Date Deposited: 2011-12
 

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