Prediction of morning fatigue severity in outpatients receiving chemotherapy: less may still be more.
Kord M KoberRitu RoyYvette P ConleyAnand A DhruvaMarilyn J HammerJon D LevineAdam B OlshenChristine A MiaskowskiPublished in: Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer (2023)
This study is the first to use machine learning techniques to accurately predict the severity of morning fatigue from prior to through the week following the administration of CTX using total and individual item scores from the Lee Fatigue Scale (LFS). Our findings suggest that the language used to assess clinical fatigue in oncology patients is important and that two simple questions may be used to predict morning fatigue severity.
Keyphrases
- sleep quality
- machine learning
- end stage renal disease
- ejection fraction
- newly diagnosed
- autism spectrum disorder
- prognostic factors
- artificial intelligence
- palliative care
- escherichia coli
- clinical trial
- squamous cell carcinoma
- depressive symptoms
- study protocol
- peritoneal dialysis
- randomized controlled trial
- patient reported outcomes
- multidrug resistant