SHREC: A Framework for Advancing Next-Generation Computational Phenotyping with Large Language Models

TL;DR

This paper introduces SHREC, a framework that leverages lightweight large language models to automate and improve computational phenotyping tasks, demonstrating high accuracy and adaptability in classifying phenotypes from electronic health records.

Contribution

The paper presents a novel framework integrating lightweight LLMs into phenotyping pipelines, showing their effectiveness in classifying concepts and phenotypes with high accuracy.

Findings

Mistral model achieved AUROC of 0.896 for concept classification.

Models demonstrated near-perfect specificity for phenotyping tasks.

Lightweight LLMs can adapt to new tasks with prompt engineering and incorporate raw EHR data.

Abstract

Computational phenotyping is a central informatics activity with resulting cohorts supporting a wide variety of applications. However, it is time-intensive because of manual data review and limited automation. Since LLMs have demonstrated promising capabilities for text classification, comprehension, and generation, we posit they will perform well at repetitive manual review tasks traditionally performed by human experts. To support next-generation computational phenotyping, we developed SHREC, a framework for integrating LLMs into end-to-end phenotyping pipelines. We applied and tested three lightweight LLMs (Gemma2 27 billion, Mistral Small 24 billion, and Phi-4 14 billion) to classify concepts and phenotype patients using phenotypes for ARF respiratory support therapies. All models performed well on concept classification, with the best (Mistral) achieving an AUROC of 0.896. For phenotyping, models demonstrated near-perfect specificity for all phenotypes with the top-performing model (Mistral) achieving an average AUROC of 0.853 for single-therapy phenotypes. In conclusion, lightweight LLMs can assist researchers with resource-intensive phenotyping tasks. Several advantages of LLMs included their ability to adapt to new tasks with prompt engineering alone and their ability to incorporate raw EHR data. Future steps include determining optimal strategies for integrating biomedical data and understanding reasoning errors