Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-1152
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-20T06:54:07.569227Z
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 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%.
problem_title: (none)
trace_subject: (none)
gain_reading: 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%.
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
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

bb957f2ffda20aee4d75efef79e85e81d0d01dc18b57c685e1bc9a41cd859bd7

previous

0000000000000000000000000000000000000000000000000000000000000000