Interpretable machine learning model for early complication prediction after split liver transplantation

IV PL during right tri-segment SLT appears to reduce the risk of EPC and enhance postoperative liver function recovery. Together with LnSII, blood loss, and MELD score, these factors offer a reliable foundation for individualized perioperative risk stratification and management.

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Publication Details

Journal
World Journal of Gastroenterology
Published
2025-12-19
DOI
https://doi.org/10.3748/wjg.v31.i47.114370
Primary Topic
Organ Transplantation Techniques and Outcomes
Type
article
Field-Weighted Citation Impact
0.00
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article

Interpretable machine learning model for early complication prediction after split liver transplantation

World Journal of Gastroenterology
Organ Transplantation Techniques and Outcomes
article

Interpretable machine learning model for early complication prediction after split liver transplantation

article en

Abstract

IV PL during right tri-segment SLT appears to reduce the risk of EPC and enhance postoperative liver function recovery. Together with LnSII, blood loss, and MELD score, these factors offer a reliable foundation for individualized perioperative risk stratification and management.

World Journal of GastroenterologyVol. 31(47)
Nankai University (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 28%
Organ Transplantation Techniques and Outcomes
DOIOpen Access PDFOpenAlex
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Interpretable machine learning model for early complication prediction after split liver transplantation · World Journal of Gastroenterology (2025) | TGRS Research Map | TGRS