Impact of Machine Learning Assistance on the Quality of Life Prediction for Breast Cancer Patients

Proper and well-timed interventions may improve breast cancer patient adaptation, resilience and quality of life (QoL) during treatment process and time after disease. The challenge is to identify those patients who would benefit most from a particular intervention. The aim of this study was to measure whether the machine learning prediction incorporated in the clinical decision support system (CDSS) improves clinicians' performance to predict patients' QoL during treatment process. We conducted an experimental setup in which six clinicians used CDSS and predicted QoL for 60 breast cancer patients. Each patient was evaluated both with and without the aid of machine learning prediction. The clinicians were also open-ended interviewed to investigate the usage and perceived benefits of CDSS with the machine learning prediction aid. Clinicians' performance to evaluate the patients' QoL was higher with the aid of machine learning predictions than without the aid. AUROC of clinicians was .777 (95% CI .691 - .857) with the aid and .755 (95% CI .664 - .840) without the aid. When the machine learning model's prediction was correct, the average accuracy (ACC) of the clinicians was .788 (95% CI .739 - .838) with the aid and .717 (95% CI .636 - .798) without the aid.