Reliable prognoses for progressive illnesses often require analysis of a series of medical images rather than a single snapshot. Classical joint models and many recent deep-learning approaches either discard censored observations, ignore temporal dependencies between adjacent scans or offer limited insight into the image regions that drive risk. We introduce SurLonMamba, a joint survival model that couples a computationally efficient selective state-space model for sequential image processing and survival prediction. Its vision encoder extracts spatial features from each image scan; the sequence encoder propagates these representations through time, capturing long-range progression patterns at linear computational complexity; and the survival module converts the resulting sequence summary into a hazard score trained by minimizing the negative log partial likelihood, thereby leveraging all censored data. The architecture naturally supports participant-level interpretation through occlusion sensitivity and survival risk prediction. Extensive simulations and experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort show that SurLonMamba exhibits strong predictive performance, while highlighting brain regions consistently linked to conversion to dementia.

Fraudulent activities within the U.S. healthcare system cost billions of dollars each year and harm the wellbeing of many qualifying beneficiaries. The implementation of an effective fraud detection method has become imperative to secure the welfare of the general public. In this article, we focus on the problem of fraud detection using the current year's Medicare claims data from the perspective of utilizing temporal information from the previous years. We group the data into temporal trajectories of the key covariates and base our feature engineering around these trajectories. For effective feature engineering on the temporal data, we propose to use the functional principal component analysis (FPCA) method for analyzing the temporal covariates’ trajectory as well as the distributional FPCA for extracting features from the empirical probability density curve of the covariates. Moreover, we introduce the framework of cost-sensitive learning for analyzing the Medicare database to allow for asymmetrical losses in the confusion matrix, such that the classification rule reflects the realistic tradeoff between the fixed cost and the fraud cost. The issue of class imbalance in the database is tackled through the random undersampling scheme. Our results confirm that the trained classifier has a reasonably good prediction performance and a significant percentage of cost savings can be achieved by taking into account the financial cost.