  Extracting social determinants of health (SDoH) from unstructured medical
notes depends heavily on labor-intensive annotations, which are typically
task-specific, hampering reusability and limiting sharing. In this study we
introduced SDoH-GPT, a simple and effective few-shot Large Language Model (LLM)
method leveraging contrastive examples and concise instructions to extract SDoH
without relying on extensive medical annotations or costly human intervention.
It achieved tenfold and twentyfold reductions in time and cost respectively,
and superior consistency with human annotators measured by Cohen's kappa of up
to 0.92. The innovative combination of SDoH-GPT and XGBoost leverages the
strengths of both, ensuring high accuracy and computational efficiency while
consistently maintaining 0.90+ AUROC scores. Testing across three distinct
datasets has confirmed its robustness and accuracy. This study highlights the
potential of leveraging LLMs to revolutionize medical note classification,
demonstrating their capability to achieve highly accurate classifications with
significantly reduced time and cost.
