Automatically Extracting Numerical Results from Randomized Controlled Trials with Large Language Models
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View abstract on PubMed
Summary
This summary is machine-generated.Large language models (LLMs) show promise for automating meta-analyses by extracting data from randomized controlled trials (RCTs). While effective for simple outcomes, LLMs struggle with complex data requiring inference.
Area Of Science
- Medical Informatics
- Natural Language Processing
- Clinical Trials
Background
- Meta-analyses are crucial for robust treatment effectiveness estimates, synthesizing findings from multiple randomized controlled trials (RCTs).
- Current meta-analysis requires laborious manual data extraction from individual trial reports, limiting efficiency and scalability.
- Automating this data extraction using language technologies could enable on-demand meta-analyses.
Purpose Of The Study
- To evaluate the capability of modern large language models (LLMs) in reliably extracting numerical findings from clinical trial reports for meta-analysis.
- To assess LLM performance in zero-shot conditional extraction of numerical results linked to interventions, comparators, and outcomes.
Main Methods
- Development and release of a granular evaluation dataset of clinical trial reports with annotated numerical findings.
- Evaluation of seven large language models (LLMs) using a zero-shot approach on the annotated dataset.
- Focus on extracting numerical results for interventions, comparators, and outcomes from trial reports.
Main Results
- Massive LLMs demonstrate near-capability for fully automatic meta-analysis, particularly for dichotomous outcomes like mortality.
- LLMs exhibit poor performance when outcome measures are complex and require inferential reasoning to tally results.
- Performance limitations persist even for LLMs trained on biomedical texts.
Conclusions
- Large language models (LLMs) are approaching the goal of fully automatic meta-analysis of randomized controlled trials (RCTs).
- Current LLMs face significant limitations in extracting and synthesizing complex numerical data from trial reports.
- Further advancements in LLMs are needed to overcome challenges in inferential data processing for comprehensive meta-analysis.
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