Integration of Large Language Models and Traditional Deep Learning for Social Determinants of Health Prediction

TL;DR

This paper combines traditional deep learning and Large Language Models to improve the automatic extraction of Social Determinants of Health from clinical texts, achieving higher accuracy and faster processing.

Contribution

It introduces a hybrid approach that leverages LLMs for precision and traditional models for efficiency, significantly speeding up classification without sacrificing accuracy.

Findings

Models outperform previous benchmarks by 10 points in multilabel classification.

Achieves 12X faster classification by removing expensive LLM processing.

Traditional deep learning models outperform LLMs on augmented datasets.

Abstract

Social Determinants of Health (SDoH) are economic, social and personal circumstances that affect or influence an individual's health status. SDoHs have shown to be correlated to wellness outcomes, and therefore, are useful to physicians in diagnosing diseases and in decision-making. In this work, we automatically extract SDoHs from clinical text using traditional deep learning and Large Language Models (LLMs) to find the advantages and disadvantages of each on an existing publicly available dataset. Our models outperform a previous reference point on a multilabel SDoH classification by 10 points, and we present a method and model to drastically speed up classification (12X execution time) by eliminating expensive LLM processing. The method we present combines a more nimble and efficient solution that leverages the power of the LLM for precision and traditional deep learning methods for efficiency. We also show highly performant results on a dataset supplemented with synthetic data and several traditional deep learning models that outperform LLMs. Our models and methods offer the next iteration of automatic prediction of SDoHs that impact at-risk patients.