Smart Anomaly Prediction in Nonstationary CT Colonography Screening.

To enhance the quality of economically efficient healthcare, we propose a preventive planning service for next-generation screening based on a longitudinal prediction. This newly proposed framework may bring important advancements in prevention by identifying the early stages of cancer, which will help in further diagnoses and initial treatment planning. The preventive service may also solve the obstacles of cost and availability of scanners in screening. For nonstationary medical data, anomaly detection is the key problem in the prediction of cancer staging. To address anomaly detection in a huge stream of databases, we applied a composite kernel to the prediction of cancer staging for the first time. The proposed longitudinal analysis of composite kernels (LACK) is designed for the prediction of anomaly status and cancer stage for further diagnosis and the future likelihood of cancer stage progression. The prediction error of LACK is relatively small even if the prediction is made far ahead of time. The computation time for nonstationary learning is reduced by \text33\% compared with stationary learning. [ABSTRACT FROM PUBLISHER]