Integration of machine learning and large language models for screening and identifying key risk factors of acute kidney injury after cardiac surgery

Abstract

The proposed risk prediction approach for postoperative AKI following cardiac surgery, based on the collaborative analysis of machine learning and large language models (LLMs), effectively identified and validated key clinical risk factors. By simulating expert clinical reasoning, the LLMs significantly enhanced the medical relevance of feature selection and improved the clinical interpretability of the model. This approach provides a solid theoretical and practical foundation for the precise early identification and clinical intervention of postoperative AKI in cardiac surgery patients.

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