An AdaBoost-Inspired Ensemble Method for ADR Signal Detection

Spontaneous Reporting System (SRS) is the major mechanism employed for monitoring Adverse Drug Reaction (ADR), also the main repository for detecting suspect ADR signals. Most research organizations rely on a single ADR detection method to make decision. Although some organizations such as the Briti...

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Bibliographic Details
Published in2020 IEEE International Conference on Healthcare Informatics (ICHI) pp. 1 - 2
Main Authors Lin, Wen-Yang, Dai, I
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2020
Subjects
AdaBoost
ADR Signal Detection
Adverse Drug Reaction
Drugs
Ensemble Learning
Medical services
Monitoring
Organizations
Random forests
Signal detection
SRS
Stacking
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Summary:Spontaneous Reporting System (SRS) is the major mechanism employed for monitoring Adverse Drug Reaction (ADR), also the main repository for detecting suspect ADR signals. Most research organizations rely on a single ADR detection method to make decision. Although some organizations such as the British MHRA and EU have used the aggregative indicator method to combine several different rules based on the rule of thumb, or like some China scholars suggested using a simple voting ensemble method, there is no research comprehensively and in-depth investigating how to synergize different methods and showing any experimental results. In this paper, we propose an ensemble of ADR signal detectors that adopts the operating principle of AdaBoost in ensemble learning. The proposed method integrates the advantages from different ADR detection methods and automatically adjust the weight of each ADR detection method to improve the overall detection performance. Experiments conducted using FAERS datasets showed that our method significantly outperforms simple voting as well as random forest ensemble.
ISSN:2575-2634
DOI:10.1109/ICHI48887.2020.9374305