Comparative Analysis of Rule-Based and Large Language Model-Based Approaches in Addressing Variability in Clinical Outcome Reporting

In clinical trials, varied terminologies and definitions often obscure the clarity and consistency needed to interpret results effectively. The ability to standardize clinical outcome reports and align semantically similar outcomes is crucial in healthcare and research, as inconsistencies can impede the comparability of trial results, complicating metaanalyses and informed decision-making. This research focuses on minimizing variability in the reporting of outcome measures through a comparative analysis of rule-based and advanced language modeling techniques. The rule-based method employs established ontologies, while the language model-based approach utilizes large language models. Findings indicate a low linkage of outcomes to traditional rule-based ontology, particularly for three-word outcomes, and underscore large language models’ efficacy in recognizing semantically similar outcomes across varying word counts. This supports the critical role of large language models in harmonizing outcome data, reducing redundancies, and improving data interoperability in clinical research contexts.