How to Construct an Optimal Risk Model for Contrast-induced Nephropathy

Naoya Kataoka; Teruhiko Imamura

doi:10.2169/internalmedicine.3209-23

How to Construct an Optimal Risk Model for Contrast-induced Nephropathy

Naoya Kataoka, Teruhiko Imamura^*

^*Corresponding author for this work

Internal Medicine （2）

Research output: Contribution to journal › Letter › peer-review

Original language	English
Pages (from-to)	2717
Number of pages	1
Journal	Internal Medicine
Volume	63
Issue number	19
DOIs	https://doi.org/10.2169/internalmedicine.3209-23
State	Published - 2024

Keywords

acute kidney injury
machine learning
percutaneous coronary intervention

ASJC Scopus subject areas

Internal Medicine

Access to Document

10.2169/internalmedicine.3209-23

Cite this

@article{d492db61028e47889f79973d20a06dcb,

title = "How to Construct an Optimal Risk Model for Contrast-induced Nephropathy",

keywords = "acute kidney injury, machine learning, percutaneous coronary intervention",

author = "Naoya Kataoka and Teruhiko Imamura",

year = "2024",

doi = "10.2169/internalmedicine.3209-23",

language = "英語",

volume = "63",

pages = "2717",

journal = "Internal Medicine",

issn = "0918-2918",

publisher = "Japanese Society of Internal Medicine",

number = "19",

}

TY - JOUR

T1 - How to Construct an Optimal Risk Model for Contrast-induced Nephropathy

AU - Kataoka, Naoya

AU - Imamura, Teruhiko

PY - 2024

Y1 - 2024

KW - acute kidney injury

KW - machine learning

KW - percutaneous coronary intervention

UR - http://www.scopus.com/inward/record.url?scp=85205604545&partnerID=8YFLogxK

U2 - 10.2169/internalmedicine.3209-23

DO - 10.2169/internalmedicine.3209-23

M3 - Letter

C2 - 38296471

AN - SCOPUS:85205604545

SN - 0918-2918

VL - 63

SP - 2717

JO - Internal Medicine

JF - Internal Medicine

IS - 19

ER -