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TRUVACE RECORD VERSION
record: TRV-2026-1152
version: 2
kind: revised
reason: Editorial pass: sharper headline; gain/problem restated as concrete outcomes.
timestamp: 2026-11-04T09:50:24.978897Z
status: published
lens: g_space
sector: health
headline: Leveraging time-series electronic health records with large language models for chronic kidney disease diagnosis in primary care
dek: 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…
gain_title: Using one month of health records from 2300 sampled adults in Weinan, China, an AI chatbot diagnosed chronic kidney disease with 87.0% accuracy, 89.1% sensitivity and 86.8% specificity.
problem_title: (none)
trace_subject: (none)
gain_reading: Using one month of health records from 2300 sampled adults in Weinan, China, an AI chatbot diagnosed chronic kidney disease with 87.0% accuracy, 89.1% sensitivity and 86.8% specificity.
gain_evidence: (none)
problem_reading: (none)
problem_evidence: (none)
quick_read: 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.

Using the DeepSeek LLM with prompt engineering, we generated binary CKD diagnoses and probability scores based on EHR data. Diagnostic performance was evaluated using accuracy, sensitivity, specificity, F1 score, area under the curve (AUC) and the detection rate for early kidney injury and compared with traditional machine learning (ML) models.
limitation: 
tag: Evidence-backed gain
key_points: 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.
rundown: 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.
sources:
- peer_reviewed | BMJ Health & Care Informatics | https://doi.org/10.1136/bmjhci-2025-101830 | 2026-09-18
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