Journal
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Volume 24, Issue 1, Pages 57-68Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2019.2932740
Keywords
Drugs; Machine learning algorithms; Machine learning; Semisupervised learning; Prediction algorithms; Feature extraction; Support vector machines; Semi-supervised learning; stacked autoencoder; classification; drug-drug interactions; clinical decision support; adverse event reports
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Funding
- Allen E. Pearce and Allen M. Pearce Professorship
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Identifying drug-drug interactions (DDIs) is a critical enabler for reducing adverse drug events and improving patient safety. Generating proper DDI alerts during prescribing workflow has the potential to prevent DDI-related adverse events. However, the implementation of DDI alerting system remains a challenge as users are experiencing alert overload which causes alert fatigue. One strategy to optimize the current system is to establish a list of high-priority DDIs for alerting purposes, though it is a resource-intensive task. In this study, we propose a machine learning framework to extract useful features from the FDA adverse event reports and then identify potential high-priority DDIs using an autoencoder-based semi-supervised learning algorithm. The experimental results demonstrate the effectiveness of using adverse event feature representations in differentiating high- and low-priority DDIs. Additionally, the proposed algorithm utilizes stacked autoencoders and weighted support vector machine for boosting classification performance, which outperforms other competing methods in terms of F-measure and AUC score. This framework integrates multiple information sources, leverages domain knowledge and clinical evidence, and provides a practical approach for pre-screening high-priority DDI candidates for medication alerts.
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