Using machine learning to predict individual patient toxicities from cancer treatments.

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.

Vasomotor symptoms in early breast cancer-a "real world" exploration of the patient experience.

Background: Despite the frequency of vasomotor symptoms (VMS) in patients with early breast cancer (EBC), their optimal management remains unknown. A patient survey was performed to determine perspectives on this important clinical challenge.

Methods: Patients with EBC experiencing VMS participated in an anonymous survey.

Developing patient-centred strategies to optimize the management of vasomotor symptoms in breast cancer patients: a survey of health care providers.

Purpose: Vasomotor symptoms (VMS) such as hot flashes and night sweats are common in breast cancer patients and can affect both quality of life and treatment adherence. However, there is limited practical data to guide clinicians in the optimal selection of therapeutic strategies. A survey of health care providers was performed to better understand perspectives and prescribing practices for managing this problem.