Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text
<|reference_start|>Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text: Social and behavioral determinants of health (SBDH) play a crucial role in health outcomes and are frequently documented in clinical text. Automatically extracting SBDH information from clinical text relies on publicly available good-quality datasets. However, existing SBDH datasets exhibit substantial limitations in their availability and coverage. In this study, we introduce Synth-SBDH, a novel synthetic dataset with detailed SBDH annotations, encompassing status, temporal information, and rationale across 15 SBDH categories. We showcase the utility of Synth-SBDH on three tasks using real-world clinical datasets from two distinct hospital settings, highlighting its versatility, generalizability, and distillation capabilities. Models trained on Synth-SBDH consistently outperform counterparts with no Synth-SBDH training, achieving up to 62.5% macro-F improvements. Additionally, Synth-SBDH proves effective for rare SBDH categories and under-resource constraints. Human evaluation demonstrates a Human-LLM alignment of 71.06% and uncovers areas for future refinements.<|reference_end|>
@article{mitra2024synth-sbdh:,
    title={Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of
  Health for Clinical Text},
    author={Avijit Mitra, Emily Druhl, Raelene Goodwin, Hong Yu},
    journal={arXiv preprint arXiv:2406.06056},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06056},
    primaryClass={cs.CL}
}