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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
Published on: December 11, 2016
A new algorithm for reducing the workload of experts in performing systematic reviews.
Stan Matwin1, Alexandre Kouznetsov, Diana Inkpen
1School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada.
Summary
A new factorized complement naïve Bayes (FCNB) classifier with weight engineering (WE) significantly reduces expert workload in systematic reviews for drug efficacy. This machine learning approach automates article screening, improving efficiency for disease treatment research.
Area of Science:
- Biomedical Informatics
- Machine Learning
- Evidence-Based Medicine
Background:
- Systematic reviews are crucial for assessing drug efficacy but are time-consuming.
- Automating article screening can improve the efficiency of systematic reviews.
- Previous automated citation classification systems have limitations in workload reduction.
Purpose of the Study:
- To evaluate a factorized complement naïve Bayes (FCNB) classifier for reducing expert review time in systematic reviews.
- To assess the effectiveness of FCNB with weight engineering (WE) for classifying high-quality, drug class-specific evidence.
Main Methods:
- Developed and evaluated a FCNB classifier on a test collection from 15 systematic drug class reviews.
- Incorporated weight engineering (WE) to enhance feature representation (MeSH, PubType).
- Used cross-validation for parameter tuning and performance evaluation, with work saved over sampling (WSS) at >=95% recall as the primary metric.
Main Results:
- The FCNB/WE classifier achieved an average workload reduction of 33.5% across 15 topics, with a maximum reduction of 62.2%.
- This represents a 15.0% improvement in average workload reduction compared to a voting perceptron-based system.
- The classifier effectively identifies articles with high-quality, drug class-specific evidence.
Conclusions:
- The FCNB/WE classifier offers a simple and implementable solution for automating systematic reviews.
- It significantly outperforms previous methods in reducing expert workload for drug efficacy reviews.
- The algorithm is a valuable tool for machine-learning-based automation in evidence-based medicine.
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