RDMA: Cost Effective Agent-Driven Rare Disease Discovery within Electronic Health Record Systems
This addresses the challenge for clinicians and rare disease patients in accessing crucial diagnostic information from EHRs while mitigating privacy risks.
The paper tackles the problem of identifying rare diseases in electronic health records, where standard coding systems fail, by developing RDMA, a framework that improves F1 performance by over 30% and reduces inference costs 10-fold.
Rare diseases affect 1 in 10 Americans, yet standard ICD coding systems fail to capture these conditions in electronic health records (EHR), leaving crucial information buried in clinical notes. Current approaches struggle with medical abbreviations, miss implicit disease mentions, raise privacy concerns with cloud processing, and lack clinical reasoning abilities. We present Rare Disease Mining Agents (RDMA), a framework that mirrors how medical experts identify rare disease patterns in EHR. RDMA connects scattered clinical observations that together suggest specific rare conditions. By handling clinical abbreviations, recognizing implicit disease patterns, and applying contextual reasoning locally on standard hardware, RDMA reduces privacy risks while improving F1 performance by upwards of 30\% and decreasing inferences costs 10-fold. This approach helps clinicians avoid the privacy risk of using cloud services while accessing key rare disease information from EHR systems, supporting earlier diagnosis for rare disease patients. Available at https://github.com/jhnwu3/RDMA.