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

arXiv: https://arxiv.org/abs/2505.04655 · HTML: https://mlsanity.com/paper/2505.04655/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 6 May 2025.

## Builds on (most cited first, 10 of 10)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning](https://mlsanity.com/paper/2012.13255/) (2020)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit](https://mlsanity.com/paper/2010.01165/) (2020)
- [Large Language Models to Identify Social Determinants of Health in Electronic Health Records](https://mlsanity.com/paper/2308.06354/) (2023)
- [Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data](https://mlsanity.com/paper/1804.01486/) (2018)

## Built on by (most cited first, 3 of 3)

- [EPPCMinerBen: A Novel Benchmark for Evaluating Large Language Models on Electronic Patient-Provider Communication via the Patient Portal](https://mlsanity.com/paper/2603.00028/) (2026)
- [PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning](https://mlsanity.com/paper/2601.16414/) (2026)
- [Social Determinants of Health Prediction for ICD-9 Code with Reasoning Models](https://mlsanity.com/paper/2601.09709/) (2025)

## Abstract (arXiv)

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.