Co-Training and Ensemble Learning for Duplicate Detection in Adverse Drug Event Reporting Systems

碩士 === 國立高雄大學 === 資訊工程學系碩士班 === 101 === Adverse drug reactions detection is a very important topic in the public health as well as the development of modern pharmaceutical industry. Since the number of samples in clinical trials is not enough to identify potential adverse drug reactions before the d...

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Main Authors:	Chiao-Feng Lo, 羅喬楓
Other Authors:	Wen-Yang Lin
Format:	Others
Language:	en_US
Published:	2013
Online Access:	http://ndltd.ncl.edu.tw/handle/74533117604020370925

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spelling	ndltd-TW-101NUK053920092016-03-21T04:28:15Z http://ndltd.ncl.edu.tw/handle/74533117604020370925 Co-Training and Ensemble Learning for Duplicate Detection in Adverse Drug Event Reporting Systems 利用合作訓練與集成學習法檢測藥物不良反應事件通報系統中之重複記錄 Chiao-Feng Lo 羅喬楓碩士國立高雄大學資訊工程學系碩士班 101 Adverse drug reactions detection is a very important topic in the public health as well as the development of modern pharmaceutical industry. Since the number of samples in clinical trials is not enough to identify potential adverse drug reactions before the drugs are approved for marketing, many countries have established various spontaneous reporting systems (SRSs) to facilitate postmarketing surveillance of listed drugs and collect enough data for detecting unknown adverse drug reactions. Unfortunately, due to data in SRSs coming from different sources of reporters, there heralds the problem of duplicate reporting; even a small amount of duplicate records would bias the detection results. Although lots of works have been conducted on duplicate record detection, very few of them have been devoted to dataset about adverse drug reactions, and none of them have considered the existence of follow-up reports. Thus contemporary methods tailored to detecting duplicate ADR report are inept to discriminate real duplicate from follow-up linkage. In this study, we investigated the problem of identifying duplicate ADR reports in SRSs with the presence of follow-ups. We propose an ensemble and co-training based detection method that is capable of detecting for a given report not only its duplicates but also its initial or earlier linkage cases. Wen-Yang Lin 林文揚 2013 學位論文 ; thesis 59 en_US
collection	NDLTD
language	en_US
format	Others
sources	NDLTD
description	碩士 === 國立高雄大學 === 資訊工程學系碩士班 === 101 === Adverse drug reactions detection is a very important topic in the public health as well as the development of modern pharmaceutical industry. Since the number of samples in clinical trials is not enough to identify potential adverse drug reactions before the drugs are approved for marketing, many countries have established various spontaneous reporting systems (SRSs) to facilitate postmarketing surveillance of listed drugs and collect enough data for detecting unknown adverse drug reactions. Unfortunately, due to data in SRSs coming from different sources of reporters, there heralds the problem of duplicate reporting; even a small amount of duplicate records would bias the detection results. Although lots of works have been conducted on duplicate record detection, very few of them have been devoted to dataset about adverse drug reactions, and none of them have considered the existence of follow-up reports. Thus contemporary methods tailored to detecting duplicate ADR report are inept to discriminate real duplicate from follow-up linkage. In this study, we investigated the problem of identifying duplicate ADR reports in SRSs with the presence of follow-ups. We propose an ensemble and co-training based detection method that is capable of detecting for a given report not only its duplicates but also its initial or earlier linkage cases.
author2	Wen-Yang Lin
author_facet	Wen-Yang Lin Chiao-Feng Lo 羅喬楓
author	Chiao-Feng Lo 羅喬楓
spellingShingle	Chiao-Feng Lo 羅喬楓 Co-Training and Ensemble Learning for Duplicate Detection in Adverse Drug Event Reporting Systems
author_sort	Chiao-Feng Lo
title	Co-Training and Ensemble Learning for Duplicate Detection in Adverse Drug Event Reporting Systems
title_short	Co-Training and Ensemble Learning for Duplicate Detection in Adverse Drug Event Reporting Systems
title_full	Co-Training and Ensemble Learning for Duplicate Detection in Adverse Drug Event Reporting Systems
title_fullStr	Co-Training and Ensemble Learning for Duplicate Detection in Adverse Drug Event Reporting Systems
title_full_unstemmed	Co-Training and Ensemble Learning for Duplicate Detection in Adverse Drug Event Reporting Systems
title_sort	co-training and ensemble learning for duplicate detection in adverse drug event reporting systems
publishDate	2013
url	http://ndltd.ncl.edu.tw/handle/74533117604020370925
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Co-Training and Ensemble Learning for Duplicate Detection in Adverse Drug Event Reporting Systems

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