Introduction to Supervised Machine Learning

被引：10

作者：

Biswas, Aditya ^{[1
]}

Saran, Ishan ^{[1
]}

Wilson, F. Perry ^{[1
]}

机构：

[1] Yale Sch Med, Dept Med, Clin & Translat Res Accelerator, New Haven, CT USA

来源：

KIDNEY360 | 2021年 / 2卷 / 05期

关键词：

clinical nephrology; algorithms; artificial intelligence; classification; machine learning; methods; prediction; supervised;

D O I：

10.34067/KID.0000182021

中图分类号：

R5 [内科学]; R69 [泌尿科学（泌尿生殖系疾病）];

学科分类号：

1002 ; 100201 ;

摘要：

The common conception and criticism of machine learning (ML) in medicine is that it centers around a "black box," an inscrutable series of mathematical calculations that take in data and spit out predictions, lacking the pathobiological explanatory rigor to which medical researchers are accustomed. Although this is oversimplified, it is not altogether untrue. It is also not really a problem. The true place for ML in medicine is not in explanation, but in prediction. © 2021 The Author(s).

引用

页码：878 / 880

页数：3

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