Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury | Zendy

Kumardeep Chaudhary | Zendy; Akhil Vaid | Zendy; Áine Duffy | Zendy; Ishan Paranjpe | Zendy; Suraj K. Jaladanki | Zendy; Manish Paranjpe | Zendy; Kipp W. Johnson | Zendy; Avantee V. Gokhale | Zendy; Pattharawin Pattharanitima | Zendy; Kinsuk Chauhan | Zendy; Ross O’Hagan | Zendy; Tielman Van Vleck | Zendy; Steven G. Coca | Zendy; Richard Cooper | Zendy; Benjamin S. Glicksberg | Zendy; Erwin P. Böttinger | Zendy; Lili Chan | Zendy; Girish N. Nadkarni | Zendy

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Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury

Author(s) -

Kumardeep Chaudhary,

Akhil Vaid,

Áine Duffy,

Ishan Paranjpe,

Suraj K. Jaladanki,

Manish Paranjpe,

Kipp W. Johnson,

Avantee V. Gokhale,

Pattharawin Pattharanitima,

Kinsuk Chauhan,

Ross O’Hagan,

Tielman Van Vleck,

Steven G. Coca,

Richard Cooper,

Benjamin S. Glicksberg,

Erwin P. Böttinger,

Lili Chan,

Girish N. Nadkarni

Publication year - 2020

Publication title -

clinical journal of the american society of nephrology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.755

H-Index - 151

eISSN - 1555-905X

pISSN - 1555-9041

DOI - 10.2215/cjn.09330819

Subject(s) - acute kidney injury , sepsis , medicine , dialysis , emergency department , intensive care unit , intensive care medicine , emergency medicine , psychiatry

Sepsis-associated AKI is a heterogeneous clinical entity. We aimed to agnostically identify sepsis-associated AKI subphenotypes using deep learning on routinely collected data in electronic health records.

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