− EDUCATION To develop a machine learning (ML)-based predictive model for assessing the risk of zero-dose children using…+ HEALTH Chronic kidney disease (CKD) presents a growing public health challenge in China, exacerbated by low patient…+ POLICY Digital transformation in pharmaceutical regulatory affairs is accelerating as global submissions grow in com…− HEALTH The objective of this study is to develop and validate a Picture Archiving and Communication System-integrate…− HEALTH Artificial intelligence (AI) is increasingly used in cerebral small vessel disease (CSVD) imaging, but the ex…− HEALTH Background Large language models (LLMs) are increasingly accessed by patients for gastrointestinal health inf…− HEALTH Artificial intelligence in ophthalmology encounters a continual challenge: Systems proficient in picture clas…− HEALTH The FDA received a malfunction report involving Inogen one g5 oxygen concentrator, made by Inogen, Inc.. Rece…
TruaceTracing the truth around AISunday, September 20, 2026
Subscribe
Health·G Space·Evidence-backed gain·Published 2026-09-20

Leveraging time-series electronic health records with large language models for chronic kidney disease diagnosis in primary care

Abstract: 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…

TRV-2026-1152Peer-reviewedPermanent record — cite & verify
Leveraging time-series electronic health records with large language models for chronic kidney disease diagnosis in primary care

"Bill on dialysis" by fireflythegreat is licensed under CC BY 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/2.0/.

The 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.

Main 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.
Gain

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%.

The rundown

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

Reader signal

How should this claim be treated?

The debate