Semi-Supervised Frameworks for Predicting Fear of Cancer Recurrence Using Reimbursement Data

Fear of cancer recurrence (FCR) is a significant psychological concern for cancer survivors and is associated with quality of life. While traditional methods for assessing FCR often rely on self-reported questionnaires, these are not always available in clinical settings, and there is no effective measure to reliably identify individuals at risk. This paper proposes two semi-supervised learning (SSL) frameworks to predict FCR using healthcare reimbursement data, which is routinely collected but often underutilized for psychological outcomes such as FCR. Our approach leverages both labeled cases of FCR (patients classified with respect to FCR) and unlabeled cases through a sequential neural network pipeline combined with an autoencoder architecture. The preliminary results demonstrate that SSL can significantly enhance prediction performance, addressing the scarcity of labeled data while offering a scalable framework for real-world healthcare applications, and suggest promising directions for leveraging SSL techniques in predicting high-risk cases based on healthcare reimbursement data. This ongoing work focuses on improving the early identification of high-risk patients, aiming to enable timely interventions and personalized care.