Machine learning in liver surgery: Benefits and pitfalls

被引：0

作者：

Calleja, Rafael ^{[1
]}

Duran, Manuel ^{[1
]}

Ayllon, Maria Dolores ^{[1
]}

Ciria, Ruben ^{[1
]}

Briceno, Javier ^{[1
]}

机构：

[1] Hosp Univ Reina Sofia, Maimonides Biomed Res Inst Cordoba, Hepatobiliary Surg & Liver Transplantat Unit, Ave Menendez Pidal S-N, Cordoba 14004, Spain

来源：

WORLD JOURNAL OF CLINICAL CASES | 2024年 / 12卷 / 12期

关键词：

Machine learning; Liver surgery; Artificial intelligence; Random forest; Prediction model; ACUTE KIDNEY INJURY; ARTIFICIAL-INTELLIGENCE; RISK-FACTORS;

D O I：

10.12998/wjcc.v12.i12.2134

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

The application of machine learning (ML) algorithms in various fields of hepatology is an issue of interest. However, we must be cautious with the results. In this letter, based on a published ML prediction model for acute kidney injury after liver surgery, we discuss some limitations of ML models and how they may be addressed in the future. Although the future faces significant challenges, it also holds a great potential.

引用

页数：5

共 15 条

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