KT-LLM: an evidence-grounded and sequence text framework for auditable kidney transplant modeling
- Published on 06/03/2026
- Reading time: 12 min.
Zheng Haofeng 1,2, Luo Zihuan 1,2, He Kaiming 1, Zhou Wangtianxu 1, Kong Zhiyi 1,3, Dong Jieyi 1, Dai Qingfu 1,2, Sun Qiquan 1,2,3,4
1 https://ror.org/01vjw4z39 Department of Renal Transplantation, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou China
2 https://ror.org/0432p8t34 Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou China
3 https://ror.org/02gxych78 Shantou University Medical College Shantou China
4 https://ror.org/0530pts50 School of Medicine South China University of Technology Guangzhou China
Abstract
We address a critical clinical gap in real-world kidney transplantation (KT), the long-standing disconnect between structured longitudinal follow-up and text-defined clinical rules, which often leads to inconsistent reporting, poor policy compliance, and non-reproducible outcomes across centers. To resolve this, we introduce KT-LLM, a verifiable orchestration layer that bridges sequence modeling with policy and terminology-aware reasoning, tailoring explicitly to KT clinical...
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