aceLeveraging time-series electronic health records with large language models for chronic kidney disease diagnosis in primary care
Chronic kidney disease (CKD) presents a growing public health challenge in China, exacerbated by low patient awareness and limited nephrology resources. This study evaluated the potential of large language models (LLMs) to support CKD diagnosis in primary care using time-series electronic health record (EHR) data. Longitudinal data were extracted from the EHR system of Weinan, China. Among 29 963 adults meeting inclusion criteria, 2300 participants were randomly sampled to generate clinical vignettes. CKD status…
Using 1-month EHR data, the LLM achieved an accuracy of 87.0%, AUC of 0.921, F1 score of 0.579, sensitivity of 89.1%, specificity of 86.8%, and detection rate for early kidney injury of 42.2%.
Evidence
- Peer-reviewedBMJ Health & Care Informatics2026-09-18
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Truvace Impact Record TRV-2026-1152, v1: “Leveraging time-series electronic health records with large language models for chronic kidney disease diagnosis in primary care.” Truvace, 2026-09-20. /record/TRV-2026-1152 (accessed at citation time). sha256 bb957f2ffda20aee…
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