Detection of Anomalous Patterns in Cancer Patient Undergoing Radiotherapy Using Wearable Sensors: A Proof of Principle Machine Learning Analysis

Radiation Therapy (RT) may cause side effects, many of which are subjectively reported by patients. Previous studies showed the possibility to relate subjective symptoms such as fatigue, with data acquired by wearable sensors such as fitness activity trackers (FT) in a breast cancer patients group. Heart Rate (HR) and Activity Level (AL) emerged as good factors to measure fatigue in an objective fashion. On such a basis, our hypothesis is creating a synergy between a machine learning (ML) approach on the data collected via FT and the related Patient Reported Outcome (PRO), it is possible to better define the patient global status of performance during RT. To this intent, the intra-patient HR and AL patterns were analyzed with ML approach, to label them as “regular” or “anomalous.”