Integration of Large Language Models and Traditional Deep Learning for Social Determinants of Health Prediction
This work addresses the need for efficient and accurate SDoH prediction from clinical text to aid physicians in diagnosing diseases and decision-making for at-risk patients, presenting an incremental advancement over existing methods.
The paper tackled the problem of automatically extracting Social Determinants of Health (SDoH) from clinical text by integrating Large Language Models (LLMs) and traditional deep learning, resulting in a 10-point improvement in multilabel classification and a 12X speedup in execution time.
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.