Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases
https://doi.org/10.1101/2022.12.07.22283238
Abstract
There are more than 7,000 rare diseases, some affecting 3,500 or fewer patients in the US. Due to clinicians’ limited experience with such diseases and the heterogeneity of clinical presentations, approximately 70% of individuals seeking a diagnosis today 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. Here, we present SHEPHERD, a few shot learning approach for multi-faceted rare disease diagnosis. SHEPHERD performs deep learning over a biomedical knowledge graph enriched with rare disease information to perform phenotype-driven diagnosis. Once trained, we show that SHEPHERD can provide clinical insights about real-world patients. We evaluate SHEPHERD on a cohort of N = 465 patients representing 299 diseases (79% of genes and 83% of diseases are represented in only a single patient) in the Undiagnosed Diseases Network. SHEPHERD excels at several diagnostic facets: performing causal gene discovery (causal genes are predicted at rank = 3.56 on average), retrieving “patients-like-me” with the same causal gene or disease, and providing interpretable characterizations of novel disease presentations. We additionally examine SHEPHERD on two other real-world cohorts, MyGene2 (N = 146) and Deciphering Developmental Disorders Study (N = 1,431). SHEPHERD demonstrates the potential of deep learning to accelerate rare disease diagnosis and has implications for using deep learning on medical datasets with very few labels.
Interpretable personalized surgical recommendation with joint consideration of multiple decisional dimensions
https://doi.org/10.1038/s41746-025-01509-1
Abstract
Surgical planning can be highly complicated and personalized, where a surgeon needs to balance multiple decisional dimensions including surgical effectiveness, risk, cost, and patient’s conditions and preferences. Turning to artificial intelligence is a great appeal. This study filled in this gap with Multi-Dimensional Recommendation (MUDI), an interpretable data-driven intelligent system that supported personalized surgical recommendations on both the patient’s and the surgeon’s side with joint consideration of multiple decisional dimensions. Applied to Pelvic Organ Prolapse, a common female disease with significant impacts on life quality, MUDI stood out from a crowd of competing methods and achieved excellent performance that was comparable to top urogynecologists, with a transparent process that made communications between surgeons and patients easier. Users showed a willingness to accept the recommendations and achieved higher accuracy with the aid of MUDI. Such a success indicated that MUDI had the potential to solve similar challenges in other situations.