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<p>There are over 7000 rare diseases, some affecting 3500 or fewer patients in the United States. Due to clinicians’ limited experience with such diseases and the heterogeneity of clinical presentations, ~70% of individuals seeking a diagnosis remain undiagnosed. Deep learning has demonstrated success in aiding the diagnosis of common diseases. However, existing approaches require labeled datasets with thousands of diagnosed patients per disease. We present SHEPHERD, a few-shot learning approach for multi-faceted rare disease diagnosis. SHEPHERD performs deep learning over a knowledge graph enriched with rare disease information and is trained on a dataset of simulated rare disease patients. We demonstrate SHEPHERD's effectiveness across diverse diagnostic tasks, performing causal gene discovery, retrieving “patients-like-me”, and characterizing novel disease presentations, using real-world cohorts from the Undiagnosed Diseases Network <i>(N</i> = 465), MyGene2 (<i>N</i> = 146), and the Deciphering Developmental Disorders study (<i>N</i> = 1431). SHEPHERD demonstrates the potential of knowledge-grounded deep learning to accelerate rare disease diagnosis.</p>

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Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月20日

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  • Emily Alsentzer,
  • Michelle M. Li,
  • Shilpa N. Kobren,
  • Ayush Noori,
  • Isaac S. Kohane,
  • Marinka Zitnik

摘要

There are over 7000 rare diseases, some affecting 3500 or fewer patients in the United States. Due to clinicians’ limited experience with such diseases and the heterogeneity of clinical presentations, ~70% of individuals seeking a diagnosis remain undiagnosed. Deep learning has demonstrated success in aiding the diagnosis of common diseases. However, existing approaches require labeled datasets with thousands of diagnosed patients per disease. We present SHEPHERD, a few-shot learning approach for multi-faceted rare disease diagnosis. SHEPHERD performs deep learning over a knowledge graph enriched with rare disease information and is trained on a dataset of simulated rare disease patients. We demonstrate SHEPHERD's effectiveness across diverse diagnostic tasks, performing causal gene discovery, retrieving “patients-like-me”, and characterizing novel disease presentations, using real-world cohorts from the Undiagnosed Diseases Network (N = 465), MyGene2 (N = 146), and the Deciphering Developmental Disorders study (N = 1431). SHEPHERD demonstrates the potential of knowledge-grounded deep learning to accelerate rare disease diagnosis.