Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer | Zendy

Christopher R. Manz | Zendy; Jinbo Chen | Zendy; Yijie Liu | Zendy; Corey Chivers | Zendy; Susan Harkness Regli | Zendy; Jennifer Braun | Zendy; Michael Draugelis | Zendy; C. William Hanson | Zendy; Lawrence N. Shulman | Zendy; Lynn M. Schuchter | Zendy; Nina O’Connor | Zendy; Justin E. Bekelman | Zendy; Mitesh S. Patel | Zendy; Ravi B. Parikh | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer

Author(s) -

Christopher R. Manz,

Jinbo Chen,

Yijie Liu,

Corey Chivers,

Susan Harkness Regli,

Jennifer Braun,

Michael Draugelis,

C. William Hanson,

Lawrence N. Shulman,

Lynn M. Schuchter,

Nina O’Connor,

Justin E. Bekelman,

Mitesh S. Patel,

Ravi B. Parikh

Publication year - 2020

Publication title -

jama oncology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 8.846

H-Index - 99

eISSN - 2374-2445

pISSN - 2374-2437

DOI - 10.1001/jamaoncol.2020.4331

Subject(s) - medicine , interquartile range , prospective cohort study , receiver operating characteristic , palliative care , retrospective cohort study , gynecologic oncology , algorithm , cohort , oncology , emergency medicine , computer science , nursing

Machine learning (ML) algorithms can identify patients with cancer at risk of short-term mortality to inform treatment and advance care planning. However, no ML mortality risk prediction algorithm has been prospectively validated in oncology or compared with routinely used prognostic indices.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research