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.

BACKGROUND: Dealing with the high dimension of both neuroimaging data and genetic data is a difficult problem in the association of genetic data to neuroimaging. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of neural networks, our proposed solution uses neural networks to extract from neuroimaging data features that are relevant for predicting Alzheimer's Disease (AD) for subsequent relation to genetics. The neuroimaging-genetic pipeline we propose is comprised of image processing, neuroimaging feature extraction and genetic association steps. We present a neural network classifier for extracting neuroimaging features that are related with the disease. The proposed method is data-driven and requires no expert advice or a priori selection of regions of interest. We further propose a multivariate regression with priors specified in the Bayesian framework that allows for group sparsity at multiple levels including SNPs and genes. RESULTS: We find the features extracted with our proposed method are better predictors of AD than features used previously in the literature suggesting that single nucleotide polymorphisms (SNPs) related to the features extracted by our proposed method are also more relevant for AD. Our neuroimaging-genetic pipeline lead to the identification of some overlapping and more importantly some different SNPs when compared to those identified with previously used features. CONCLUSIONS: The pipeline we propose combines machine learning and statistical methods to benefit from the strong predictive performance of blackbox models to extract relevant features while preserving the interpretation provided by Bayesian models for genetic association. Finally, we argue in favour of using automatic feature extraction, such as the method we propose, in addition to ROI or voxelwise analysis to find potentially novel disease-relevant SNPs that may not be detected when using ROIs or voxels alone.

Massive data bring the big challenges of memory and computation for analysis. These challenges can be tackled by taking subsamples from the full data as a surrogate. For functional data, it is common to collect multiple measurements over their domains, which require even more memory and computation time when the sample size is large. The computation would be much more intensive when statistical inference is required through bootstrap samples. Motivated by analyzing large-scale kidney transplant data, we propose an optimal subsampling method based on the functional L-optimality criterion for functional generalized linear models. To the best of our knowledge, this is the first attempt to propose a subsampling method for functional data analysis. The asymptotic properties of the resultant estimators are also established. The analysis results from extensive simulation studies and from the kidney transplant data show that the functional L-optimality subsampling (FLoS) method is much better than the uniform subsampling approach and can well approximate the results based on the full data while dramatically reducing the computation time and memory.

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.

Scientists and others today often collect samples of curves and other functional data. The multivariate data classification methods cannot be directly used for functional data classification because the curse of dimensionality and difficulty in taking in account the correlation and order of functional data. We extend the kernel-induced random forest method for discriminating functional data by defining kernel functions of two curves. This method is demonstrated by classifying the temporal gene expression data. The simulation study and applications show that the kernel-induced random forest method increases the classification accuracy from the available methods. The kernel-induced random forest method is easy to implement by naive users. It is also appealing in its flexibility to allow users to choose different curve estimation methods and appropriate kernel functions.

MOTIVATION: Transcription regulation is a fundamental process in biology, and it is important to model the dynamic behavior of gene regulation networks. Many approaches have been proposed to specify the network structure. However, finding the network connectivity is not sufficient to understand the network dynamics. Instead, one needs to model the regulation reactions, usually with a set of ordinary differential equations (ODEs). Because some of the parameters involved in these ODEs are unknown, their values need to be inferred from the observed data. RESULTS: In this article, we introduce the generalized profiling method to estimate ODE parameters in a gene regulation network from microarray gene expression data which can be rather noisy. Because numerically solving ODEs is computationally expensive, we apply the penalized smoothing technique, a fast and stable computational method to approximate ODE solutions. The ODE solutions with our parameter estimates fit the data well. A goodness-of-fit test of dynamic models is developed to identify gene regulation networks.