EvidenceTriangulator: A Large Language Model Approach to Synthesizing Causal Evidence across Study Designs

Abstract

AbstractIn managing chronic diseases, the role of social determinants like lifestyle and diet is crucial. A comprehensive strategy combining biomedical and lifestyle changes is necessary for optimal health. However, the complexity of evidence from varied study designs on lifestyle interventions poses a challenge to decision-making. To tackle this challenge, our work focused on leveraging large language model to construct a dataset primed for evidence triangulation. This approach automates the process of gathering and preparing evidence for analysis, thereby simplifying the integration of reliable insights and reducing the dependency on labor-intensive manual curation. Our approach, validated by expert evaluations, demonstrates significant utility, especially illustrated through a case study on reduced salt intake and its effect on blood pressure. This highlights the potential of leveraging large language models to enhance evidence-based decision-making in health care.