mlsanityData to 7 Oct 2026

Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text

arXiv 2406.06056 ↗First posted 10 Jun 2024cs.CLcited by 3

On the map: Synthetic data and self-improvement for LLMs → Medical synthetic data and agents

Abstract (arXiv)

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 63.75% macro-F improvements. Additionally, Synth-SBDH proves effective for rare SBDH categories and under-resource constraints while being substantially cheaper than expert-annotated real-world data. Human evaluation reveals a 71.06% Human-LLM alignment and uncovers areas for future refinements.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Medical synthetic data and agents, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 34 of its 97 references that are in the map. First posted on arXiv 10 Jun 2024.

Builds on

34 of 97 references.

References · 34

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22 more
  1. 2022Large Language Models are Better Reasoners with Self-Verificationcited by 304
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 2.

3 papers.

  1. 2024Disease Entity Recognition and Normalization is Improved with Large Language Model Derived Synthetic Normalized Mentionscited by 3
  2. 2026Systematic Evaluation of the Quality of Synthetic Clinical Notes Rephrased by LLMs at Million-Note Scalecited by 0
  3. 2026Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studiescited by 0

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