bio-nlp-umass/Synth-SBDH

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The declared licence (apache-2.0) is classified as permissive.

Declared licence: apache-2.0, from the tagEvidence: README.md at 8c66e1e
Licence files: No licence file at the top level of the repositoryEvidence: file list at 8c66e1e
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Citation text from the card

@inproceedings{mitra-etal-2025-synth,
    title = "Synth-{SBDH}: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text",
    author = "Mitra, Avijit  and
      Yang, Zhichao  and
      Druhl, Emily  and
      Goodwin, Raelene  and
      Yu, Hong",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.emnlp-main.1418/",
    doi = "10.18653/v1/2025.emnlp-main.1418",
    pages = "27899--27935",
    ISBN = "979-8-89176-332-6",
    abstract = "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 ver

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dataset_licence({ "id": "bio-nlp-umass/Synth-SBDH" })
signal: "permissive"
stated_licence: "apache-2.0"
sources: 0