Artificial intelligence-enabled safety monitoring in Alzheimer's disease clinical trials
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View abstract on PubMed
Summary
This summary is machine-generated.Artificial intelligence (AI) and natural language processing (NLP) significantly improve adverse event (AE) coding in Alzheimer's disease (AD) clinical trials. Automated methods enhance accuracy and reduce costs compared to traditional manual review by clinicians.
Area Of Science
- Clinical Informatics
- Biomedical Data Science
- Neuroscience Clinical Trials
Background
- Clinical trial participant safety relies on accurate adverse event (AE) data monitoring.
- Traditional AE data coding involves manual review by expert clinicians, which can be time-consuming and prone to variability.
Purpose Of The Study
- To explore the application of natural language processing (NLP) and artificial intelligence (AI) for streamlining AE data coding in Alzheimer's disease (AD) clinical trials.
- To assess the efficiency, accuracy, reliability, and consistency of automated coding versus traditional clinician coding.
Main Methods
- Developed a gold standard dataset for AD adverse events.
- Evaluated NLP-based models for AE classification using quantitative retrospective analysis.
- Compared automated coding performance against expert clinician coding using F1 score and predictive accuracy.
Main Results
- AI-based AE coding demonstrated a significant increase in accuracy (approximately 20%) compared to manual clinician coding.
- Automated coding resulted in substantial cost reductions (approximately 80%) and improved efficiency.
Conclusions
- Combining AI with human expertise enhances the efficiency and quality of AE coding.
- This integrated approach improves overall clinical trial safety monitoring in AD research.
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