# SHREC: A framework for advancing next-generation computational phenotyping with large language models

## TL;DR

This paper introduces SHREC, a framework that uses large language models to automate and speed up the process of identifying patient groups from electronic health records.

## Contribution

The novel contribution is SHREC, a framework integrating lightweight LLMs into end-to-end computational phenotyping pipelines.

## Key findings

- Lightweight LLMs like Mistral achieved high accuracy in concept classification with an AUROC of 0.896.
- LLMs demonstrated near-perfect specificity and strong performance in phenotyping with an average AUROC of 0.853 for single-therapy phenotypes.

## 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.

In our research, we explored how large language models like ChatGPT could help make the process of identifying patient groups from electronic health records faster and less labor-intensive. Traditionally, defining these patient groups requires careful manual review of large amounts of clinical data, which can be time-consuming and costly. We developed a framework called SHREC that integrates language models into these workflows, allowing the models to classify relevant clinical information and help create patient groups automatically. We tested several models on respiratory support therapies and found that even relatively small models were highly effective at accurately identifying concepts and patients. Our work shows that language models can complement human expertise, reducing the effort needed for routine tasks while still maintaining high accuracy. By demonstrating how these tools can fit into the larger research process, we hope to encourage further development of methods that make clinical data analysis faster, more efficient, and more accessible to researchers.