Evaluating language model embeddings for Parkinson's disease cohort harmonization using a novel manually curated variable mapping schema.

ABSTRACT: Data Harmonization is an important yet time-consuming process. With the recent popularity of applications using Language Models (LMs) due to their high capabilities in text understanding, we investigated whether LMs could facilitate data harmonization for clinical use cases. To evaluate this, we created PASSIONATE, a novel Parkinson's disease (PD) variable mapping schema as a ground truth source for pairwise cohort harmonization using LLMs. Additionally, we extended our investigation using an existing Alzheimer's disease (AD) CDM. We computed text embeddings based on two language models to perform automated cohort harmonization for both AD and PD. We additionally compared the results to a baseline method using fuzzy string matching to determine the degree to which the semantic capabilities

SUBMITTER: Salimi Y 

PROVIDER: S-EPMC12181335 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature