Using machine learning to identify older adults at high risk for hospitalization and mortality via the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE).

Huiwen Xu,Supriya G. Mohile,Marie Flannery,Luke J. Peppone,Mostafa Mohamed,Erika Ramsdale,Amita Patil,Sahithi Jonnalagadda,Leah Jamieson,Victor G. Vogel,Khalil Katato,Bianca Hall,Karen M. Mustian,Eva Culakova

Using machine learning to identify older adults at high risk for hospitalization and mortality via the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE).

2020

Huiwen Xu
Supriya G. Mohile
Marie Flannery
Luke J. Peppone
Mostafa Mohamed
Erika Ramsdale
Amita Patil
Sahithi Jonnalagadda
Leah Jamieson
Victor G. Vogel
Khalil Katato
Bianca Hall
Karen M. Mustian
Eva Culakova

169Background: PRO-CTCAE captures symptomatic adverse events (e.g. pain, fatigue) and may indicate poor treatment tolerability in older patients (pts) with advanced cancer. Using unsupervised machi...

Keywords:

Intensive care medicine
advanced cancer
Common Terminology Criteria for Adverse Events
Adverse effect
older patients
pro ctcae
Medicine
Tolerability

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