Use of machine learning techniques to identify risk factors for RV failure in LVAD patients

Nandini Nair

doi:10.3389/fcvm.2022.848789

Use of machine learning techniques to identify risk factors for RV failure in LVAD patients

Nandini Nair

Department of Medicine

Research output: Contribution to journal › Comment/debate › peer-review

2 Scopus citations

Original language	English (US)
Article number	848789
Journal	Frontiers in Cardiovascular Medicine
Volume	9
DOIs	https://doi.org/10.3389/fcvm.2022.848789
State	Published - Sep 14 2022

All Science Journal Classification (ASJC) codes

Cardiology and Cardiovascular Medicine

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10.3389/fcvm.2022.848789

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@article{492f79e76fe2422eadea7be450b8ace0,

title = "Use of machine learning techniques to identify risk factors for RV failure in LVAD patients",

author = "Nandini Nair",

note = "Funding Information: The authors wish to thank the MeitY Nano-Mission program, Grant no. SR/NM/NS-1109/2012(C), MeitY SWASTHA: Grant no. 5(1)/2022-NANO, ICMR Center for Excellence: Grant no. 5/3/8/20/2019-ITR, Government of India, for financial aids. We also thank the support from FIST facility, Department of Chemical Engineering, IIT Guwahati. ",

year = "2022",

month = sep,

day = "14",

doi = "10.3389/fcvm.2022.848789",

language = "English (US)",

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journal = "Frontiers in Cardiovascular Medicine",

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AU - Nair, Nandini

N1 - Funding Information: The authors wish to thank the MeitY Nano-Mission program, Grant no. SR/NM/NS-1109/2012(C), MeitY SWASTHA: Grant no. 5(1)/2022-NANO, ICMR Center for Excellence: Grant no. 5/3/8/20/2019-ITR, Government of India, for financial aids. We also thank the support from FIST facility, Department of Chemical Engineering, IIT Guwahati.

PY - 2022/9/14

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DO - 10.3389/fcvm.2022.848789

M3 - Comment/debate

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AN - SCOPUS:85138554595

SN - 2297-055X

VL - 9

JO - Frontiers in Cardiovascular Medicine

JF - Frontiers in Cardiovascular Medicine

M1 - 848789

ER -

Use of machine learning techniques to identify risk factors for RV failure in LVAD patients

All Science Journal Classification (ASJC) codes

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