Kord M Kober1,2,3, Ritu Roy2, Anand Dhruva4, Yvette P Conley5, Raymond J Chan6,7, Bruce Cooper1, Adam Olshen2,8, Christine Miaskowski1,2. 1. School of Nursing, University of California, San Francisco, USA. 2. Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, USA. 3. Bakar Computational Health Sciences Institute, University of California, San Francisco, USA. 4. School of Medicine, University of California, San Francisco, USA. 5. School of Nursing, University of Pittsburgh, Pittsburgh, USA. 6. School of Nursing and Cancer and Palliative Care Outcomes Centre, Queensland University of Technology, Kelvin Grove, Australia. 7. Division of Cancer Services, Princess Alexandra Hospital, Metro South Hospital and Health Services, Woolloongabba, Australia. 8. Department of Epidemiology and Biostatistics, University of California, San Francisco, USA.
Abstract
BACKGROUND: Fatigue is the most common and debilitating symptom experienced by oncology patients undergoing chemotherapy. Little is known about patient characteristics that predict changes in fatigue severity over time. PURPOSE: To predict the severity of evening fatigue in the week following the administration of chemotherapy using machine learning approaches. METHODS: Outpatients with breast, gastrointestinal, gynecological, or lung cancer (N=1217) completed questionnaires one week prior to and one week following administration of chemotherapy. Evening fatigue was measured with the Lee Fatigue Scale (LFS). Separate prediction models for evening fatigue severity were created using clinical, symptom, and psychosocial adjustment characteristics and either evening fatigue scores or individual fatigue item scores. Prediction models were created using two regression and three machine learning approaches. RESULTS: Random forest (RF) models provided the best fit across all models. For the RF model using individual LFS item scores, two of the 13 individual LFS items (i.e., "worn out", "exhausted") were the strongest predictors. CONCLUSION: This study is the first to use machine learning techniques to predict evening fatigue severity in the week following chemotherapy from fatigue scores obtained in the week prior to chemotherapy. 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 evening fatigue severity.
BACKGROUND: Fatigue is the most common and debilitating symptom experienced by oncology patients undergoing chemotherapy. Little is known about patient characteristics that predict changes in fatigue severity over time. PURPOSE: To predict the severity of evening fatigue in the week following the administration of chemotherapy using machine learning approaches. METHODS: Outpatients with breast, gastrointestinal, gynecological, or lung cancer (N=1217) completed questionnaires one week prior to and one week following administration of chemotherapy. Evening fatigue was measured with the Lee Fatigue Scale (LFS). Separate prediction models for evening fatigue severity were created using clinical, symptom, and psychosocial adjustment characteristics and either evening fatigue scores or individual fatigue item scores. Prediction models were created using two regression and three machine learning approaches. RESULTS: Random forest (RF) models provided the best fit across all models. For the RF model using individual LFS item scores, two of the 13 individual LFS items (i.e., "worn out", "exhausted") were the strongest predictors. CONCLUSION: This study is the first to use machine learning techniques to predict evening fatigue severity in the week following chemotherapy from fatigue scores obtained in the week prior to chemotherapy. 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 evening fatigue severity.
Authors: Bradley E Aouizerat; Anand Dhruva; Steven M Paul; Bruce A Cooper; Kord M Kober; Christine Miaskowski Journal: J Pain Symptom Manage Date: 2015-05-29 Impact factor: 3.612
Authors: Fay Wright; Gail D'Eramo Melkus; Marilyn Hammer; Brian L Schmidt; M Tish Knobf; Steven M Paul; Frances Cartwright; Judy Mastick; Bruce A Cooper; Lee-May Chen; Michelle Melisko; Jon D Levine; Kord Kober; Bradley E Aouizerat; Christine Miaskowski Journal: J Pain Symptom Manage Date: 2015-03-28 Impact factor: 3.612
Authors: R K Portenoy; H T Thaler; A B Kornblith; J M Lepore; H Friedlander-Klar; E Kiyasu; K Sobel; N Coyle; N Kemeny; L Norton Journal: Eur J Cancer Date: 1994 Impact factor: 9.162