Your browser doesn't support javascript.
loading
Using machine learning to predict individual patient toxicities from cancer treatments.
Cole, Katherine Marie; Clemons, Mark; McGee, Sharon; Alzahrani, Mashari; Larocque, Gail; MacDonald, Fiona; Liu, Michelle; Pond, Gregory R; Mosquera, Lucy; Vandermeer, Lisa; Hutton, Brian; Piper, Ardelle; Fernandes, Ricardo; Emam, Khaled El.
Afiliación
  • Cole KM; Department of Medicine, Division of Medical Oncology, The University of Ottawa, Ottawa, Canada.
  • Clemons M; Department of Medicine, Division of Medical Oncology, The University of Ottawa, Ottawa, Canada.
  • McGee S; Cancer Therapeutics Program, The Ottawa Hospital Research Institute, Ottawa, ON, Canada.
  • Alzahrani M; Department of Medicine, Division of Medical Oncology, The University of Ottawa, Ottawa, Canada.
  • Larocque G; Department of Medicine, Division of Medical Oncology, The University of Ottawa, Ottawa, Canada.
  • MacDonald F; The Ottawa Hospital Cancer Centre, Ottawa, Canada.
  • Liu M; The Ottawa Hospital Cancer Centre, Ottawa, Canada.
  • Pond GR; Cancer Therapeutics Program, The Ottawa Hospital Research Institute, Ottawa, ON, Canada.
  • Mosquera L; Department of Oncology, McMaster University, Hamilton, ON, Canada.
  • Vandermeer L; CHEO Research Institute, University of Ottawa, 401 Smyth Road, Ottawa, ON, K1H 8L1, Canada.
  • Hutton B; Cancer Therapeutics Program, The Ottawa Hospital Research Institute, Ottawa, ON, Canada.
  • Piper A; Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, ON, Canada.
  • Fernandes R; University of Ottawa Health Services, Ottawa, ON, Canada.
  • Emam KE; Division of Medical Oncology, Department of Oncology, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.
Support Care Cancer ; 30(9): 7397-7406, 2022 Sep.
Article en En | MEDLINE | ID: mdl-35614153
PURPOSE: Machine learning (ML) is a powerful tool for interrogating datasets and learning relationships between multiple variables. We utilized a ML model to identify those early breast cancer (EBC) patients at highest risk of developing severe vasomotor symptoms (VMS). METHODS: A gradient boosted decision model utilizing cross-sectional survey data from 360 EBC patients was created. Seventeen patient- and treatment-specific variables were considered in the model. The outcome variable was based on the Hot Flush Night Sweats (HFNS) Problem Rating Score, and individual scores were dichotomized around the median to indicate individuals with high and low problem scores. Model accuracy was assessed using the area under the receiver operating curve, and conditional partial dependence plots were constructed to illustrate relationships between variables and the outcome of interest. RESULTS: The model area under the ROC curve was 0.731 (SD 0.074). The most important variables in the model were as follows: the number of hot flashes per week, age, the prescription, or use of drug interventions to manage VMS, whether patients were asked about VMS in routine follow-up visits, and the presence or absence of changes to breast cancer treatments due to VMS. A threshold of 17 hot flashes per week was identified as being more predictive of severe VMS. Patients between the ages of 49 and 63 were more likely to report severe symptoms. CONCLUSION: Machine learning is a unique tool for predicting severe VMS. The use of ML to assess other treatment-related toxicities and their management requires further study.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Sofocos Tipo de estudio: Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Middle aged Idioma: En Revista: Support Care Cancer Asunto de la revista: NEOPLASIAS / SERVICOS DE SAUDE Año: 2022 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Alemania
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Sofocos Tipo de estudio: Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Middle aged Idioma: En Revista: Support Care Cancer Asunto de la revista: NEOPLASIAS / SERVICOS DE SAUDE Año: 2022 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Alemania