Leveraging time-series electronic health records with large language models for chronic kidney disease diagnosis in primary care.
AI analyzing health records identifies early kidney injury with 89% sensitivity, catching disease before symptoms emerge.
OBJECTIVES: Chronic kidney disease (CKD) presents a growing public health challenge in China, exacerbated by low patient awareness and limited nephrology resources. RESULTS: 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%.
What the study was
- Study design
- Observational/unspecified
- Population
- patients
- Sample size
- 2300
- Category
- Early Detection
- Maturity
- Exploratory
- Journal
- BMJ health & care informatics
Why it surfaced
Score 6/10 [PROMISING_PRELIMINARY]: Observational/unspecified (Journal Article) matched 'Early cancer detection'. Components — novelty:1/3, relevance:2/3, design:1/2, population:2/2. Confidence: medium. Conservative scoring applied per v1.3 rubric.
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