Development of an automated assessment tool for MedWatch reports in the FDA adverse event reporting system
| Autor: | Lichy Han, Carol A. Pamer, Russ B. Altman, Robert Ball, Scott Proestel |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Support Vector Machine
Drug-Related Side Effects and Adverse Reactions Computer science Health Informatics Machine learning computer.software_genre Logistic regression Research and Applications 030226 pharmacology & pharmacy Machine Learning 03 medical and health sciences Adverse Event Reporting System 0302 clinical medicine Adverse Drug Reaction Reporting Systems Data Mining 030212 general & internal medicine Adverse effect Natural Language Processing MedWatch business.industry United States Food and Drug Administration Workload Models Theoretical Causality United States Random forest Logistic Models Ranking ROC Curve Artificial intelligence Data mining business computer |
| Zdroj: | J Am Med Inform Assoc |
| Popis: | Objective: As the US Food and Drug Administration (FDA) receives over a million adverse event reports associated with medication use every year, a system is needed to aid FDA safety evaluators in identifying reports most likely to demonstrate causal relationships to the suspect medications. We combined text mining with machine learning to construct and evaluate such a system to identify medication-related adverse event reports. Methods: FDA safety evaluators assessed 326 reports for medication-related causality. We engineered features from these reports and constructed random forest, L1 regularized logistic regression, and support vector machine models. We evaluated model accuracy and further assessed utility by generating report rankings that represented a prioritized report review process. Results: Our random forest model showed the best performance in report ranking and accuracy, with an area under the receiver operating characteristic curve of 0.66. The generated report ordering assigns reports with a higher probability of medication-related causality a higher rank and is significantly correlated to a perfect report ordering, with a Kendall’s tau of 0.24 (P = .002). Conclusion: Our models produced prioritized report orderings that enable FDA safety evaluators to focus on reports that are more likely to contain valuable medication-related adverse event information. Applying our models to all FDA adverse event reports has the potential to streamline the manual review process and greatly reduce reviewer workload. |
| Databáze: | OpenAIRE |
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