Enhancing Spectral Embedding through Robust and Flexible Knowledge Transfer in Electronic Health Records
arXiv:2606.11570v1 Announce Type: cross Abstract: We propose a spectral-based, unsupervised representation learning framework to derive low-dimensional embeddings for clinical concepts and patients in rare disease cohorts from electronic health records, where data are high-dimensional but sample sizes are limited. To overcome this challenge, we incorporate a knowledge matrix extracted from a broader population that shares a partially overlapping subspace with the rare-disease cohort. Our method departs from existing approaches by relaxing restrictive one-to-one signal-alignment assumptions bet
The increasing availability of electronic health records combined with advancements in AI methods makes this type of research timely.
This research offers a method to derive valuable insights from limited, high-dimensional healthcare data, particularly for rare diseases, which traditionally suffer from data scarcity.
The ability to more effectively leverage existing data, even in sparse datasets, for clinical concept and patient embeddings, improving diagnostic and treatment pathways for rare diseases.
- · Rare disease patients
- · Healthcare AI companies
- · Clinical researchers
- · Pharmaceutical companies
- · Traditional statistical methods for sparse data
Improved understanding and treatment pathways for rare diseases due to better data utilization.
Accelerated drug discovery and development for conditions with limited patient populations.
Potential for early detection and personalized medicine across a broader spectrum of complex diseases.
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Read at arXiv cs.LG