FHIR-Former: enhancing clinical predictions through Fast Healthcare Interoperability Resources and large language models.

ABSTRACT:

Objective

To address the challenges of data heterogeneity and manual feature engineering in clinical predictive modeling, we introduce FHIR-Former, an open-source framework integrating Fast Healthcare Interoperability Resources (FHIR) with large language models (LLMs) to automate and standardize clinical prediction tasks.

Materials and methods

FHIR-Former dynamically processes structured (eg, lab results, medications) and unstructured (eg, clinical notes) data from FHIR resources. The pipeline supports multiple classification tasks, including 30-day readmission, imaging study prediction, and ICD code classification. Leveraging open-source LLMs (GeBERTa), we trained models on 1.1 million data points across ten FHIR resources using retrospective inpatient data (2018-2024). Hyp

SUBMITTER: Engelke M 

PROVIDER: S-EPMC12646377 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature