Contextualized Large Language Model-Based Architecture for Outcome Measure Alignment in Clinical Trials

The inconsistency in reporting clinical trial outcome measures is a source of variability in clinical trial data which complicates comparability of results across studies. Automatic aggregation of semantically similar outcomes will greatly facilitate the harmonization, cross-trial comparison, and metadata analysis. Interpretation, comparison, and combination of results of clinical trials are reliant on the consistency of outcome reporting. This study aimed to align outcome measures by leveraging the significant capability of large language models. This study proposes a time-efficient, fully automated pipeline to find trials reporting semantically similar outcomes in order to facilitate cross-compatibility and metadata analysis.