Clinical time series data from electronic health records and medical registries offer unprecedented opportunities to understand patient trajectories and inform medical decision-making. However, leveraging such data presents significant challenges due to irregular sampling, complex latent physiology, and inherent uncertainties in both measurements and disease progression. To address these challenges, we propose a generative modeling framework based on latent neural stochastic differential equations (SDEs) that views clinical time series as discrete-time partial observations of an underlying controlled stochastic dynamical system. This formulation naturally handles irregularly sampled observations, learns complex non-linear interactions, and captures the stochasticity of disease progression and measurement noise within a unified scalable probabilistic framework. Results show that our framework outperforms ordinary differential equation and long short-term memory baseline models in accuracy and uncertainty estimation. These results highlight its potential for enabling precise, uncertainty-aware predictions to support clinical decision-making. Introduction Predicting patient trajectories is critical for enabling timely interventions, better understanding of disease progression, and developing personalized medicine [1]. For instance, early detection of sepsis has been shown to significantly reduce the risk of organ failure and mortality [2]. This potential is increasingly becoming feasible due to the rapid growth of available healthcare data like electronic health records (EHRs) [3]. A defining feature of healthcare data are their temporal nature, reflecting the dynamic evolution of patient conditions over time. These temporal patterns highlight the need for time series models specifically tailored to the complexities of clinical data. However, healthcare time series have unique characteristics such as missing values, irregular sampling, aleatoric uncertainty, and patient-specific variability, that make modeling them particularly challenging [5, 6]. Traditional time series models, such as autoregressive moving average (ARIMA) models, have been applied to healthcare data but often struggle with its complexity and irregularity [7].

Early detection of Alzheimer's disease (AD) is crucial because its neurodegenerative effects are irreversible, and neuropathologic and social-behavioral risk factors accumulate years before diagnosis. Identifying higher-risk individuals earlier enables prevention, timely care, and equitable resource allocation. We predict cognitive performance from social determinants of health (SDOH) using the NIH NIA-supported PREPARE Challenge Phase 2 dataset derived from the nationally representative Mex-Cog cohort of the 2003 and 2012 Mexican Health and Aging Study (MHAS). Data: The target is a validated composite cognitive score across seven domains-orientation, memory, attention, language, constructional praxis, and executive function-derived from the 2016 and 2021 MHAS waves. Predictors span demographic, socioeconomic, health, lifestyle, psychosocial, and healthcare access factors. Methodology: Missingness was addressed with a singular value decomposition (SVD)-based imputation pipeline treating continuous and categorical variables separately. This approach leverages latent feature correlations to recover missing values while balancing reliability and scalability. After evaluating multiple methods, XGBoost was chosen for its superior predictive performance. Results and Discussion: The framework outperformed existing methods and the data challenge leaderboard, demonstrating high accuracy, robustness, and interpretability. SHAP-based post hoc analysis identified top contributing SDOH factors and age-specific feature patterns. Notably, flooring material emerged as a strong predictor, reflecting socioeconomic and environmental disparities. Other influential factors, age, SES, lifestyle, social interaction, sleep, stress, and BMI, underscore the multifactorial nature of cognitive aging and the value of interpretable, data-driven SDOH modeling.

The utilization of robotic technology has gained traction in healthcare facilities due to progress in the field that enables time and cost savings, minimizes waste, and improves patient care. Digital healthcare technologies that leverage automation, such as robotics and artificial intelligence, have the potential to enhance the sustainability and profitability of healthcare systems in the long run. However, the recent COVID-19 pandemic has amplified the need for cyber-physical robots to automate check-ups and medication administration. A robot nurse is controlled by the Internet of Things (IoT) and can serve as an automated medical assistant while also allowing supervisory control based on custom commands. This system helps reduce infection risk and improves outcomes in pandemic settings. This research presents a test case with a nurse robot that can assess a patient's health status and take action accordingly. We also evaluate the system's performance in medication administration, health-status monitoring, and life-cycle considerations.

Outdoor health monitoring is essential to detect early abnormal health status for safeguarding human health and safety. Conventional outdoor monitoring relies on static multimodal deep learning frameworks, which requires extensive data training from scratch and fails to capture subtle health status changes. Multimodal large language models (MLLMs) emerge as a promising alternative, utilizing only small datasets to fine-tune pre-trained information-rich models for enabling powerful health status monitoring. Unfortunately, MLLM-based outdoor health monitoring also faces significant challenges: I) sensor data contains input noise stemming from sensor data acquisition and fluctuation noise caused by sudden changes in physiological signals due to dynamic outdoor environments, thus degrading the training performance; ii) current transformer based MLLMs struggle to achieve robust multimodal fusion, as they lack a design for fusing the noisy modality; iii) modalities with varying noise levels hinder accurate recovery of missing data from fluctuating distributions. To combat these challenges, we propose an uncertainty-aware multimodal fusion framework, named DUAL-Health, for outdoor health monitoring in dynamic and noisy environments. First, to assess the impact of noise, we accurately quantify modality uncertainty caused by input and fluctuation noise with current and temporal features. Second, to empower efficient muitimodal fusion with low-quality modalities,we customize the fusion weight for each modality based on quantified and calibrated uncertainty. Third, to enhance data recovery from fluctuating noisy modalities, we align modality distributions within a common semantic space. Extensive experiments demonstrate that our DUAL-Health outperforms state-of-the-art baselines in detection accuracy and robustness.

Aiming at the critical role of SSD health state prediction in data reliability assurance, this study proposes a hybrid BiGRU-MHA model that incorporates a multi-head attention mechanism to enhance the accuracy and stability of storage device health classification. The model innovatively integrates temporal feature extraction and key information focusing capabilities. Specifically, it leverages the bidirectional timing modeling advantages of the BiGRU network to capture both forward and backward dependencies of SSD degradation features. Simultaneously, the multi-head attention mechanism dynamically assigns feature weights, improving the model's sensitivity to critical health indicators. Experimental results show that the proposed model achieves classification accuracies of 92.70% on the training set and 92.44% on the test set, with a minimal performance gap of only 0.26%, demonstrating excellent generalization ability. Further analysis using the receiver operating characteristic (ROC) curve shows an area under the curve (AUC) of 0.94 on the test set, confirming the model's robust binary classification performance. This work not only presents a new technical approach for SSD health prediction but also addresses the generalization bottleneck of traditional models, offering a verifiable method with practical value for preventive maintenance of industrial-grade storage systems. The results show the model can significantly reduce data loss risks by providing early failure warnings and help optimize maintenance costs, supporting intelligent decision-making in building reliable storage systems for cloud computing data centers and edge storage environments.

Dynamic microsimulation has long been recognized as a powerful tool for policy analysis, but in fact most major health policy simulations lack path dependency, a critical feature for evaluating policies that depend on accumulated outcomes such as retirement savings, wealth, or debt. We propose the Longitudinal Health, Income and Employment Model (LHIEM), a path-dependent discrete-time microsimulation that predicts annual health care expenditures, family income, and health status for the U.S. population over a multi-year period. LHIEM advances the population from year to year as a Markov chain with modules capturing the particular dynamics of each predictive attribute. LHIEM was designed to assess a health care financing proposal that would allow individuals to borrow from the U.S. government to cover health care costs, requiring careful tracking of medical expenditures and medical debt over time. However, LHIEM is flexible enough to be used for a range of modeling needs related to predicting health care spending and income over time. In this paper, we present the details of the model and all dynamic modules, and include a case study to demonstrate how LHIEM can be used to evaluate proposed policy changes.

Racial disparities in healthcare expenditures are well-documented, yet the underlying drivers remain complex and require further investigation. This study employs causal and counterfactual path-specific effects to quantify how various factors, including socioeconomic status, insurance access, health behaviors, and health status, mediate these disparities. Using data from the Medical Expenditures Panel Survey, we estimate how expenditures would differ under counterfactual scenarios in which the values of specific mediators were aligned across racial groups along selected causal pathways. A key challenge in this analysis is ensuring robustness against model misspecification while addressing the zero-inflation and right-skewness of healthcare expenditures. For reliable inference, we derive asymptotically linear estimators by integrating influence function-based techniques with flexible machine learning methods, including super learners and a two-part model tailored to the zero-inflated, right-skewed nature of healthcare expenditures.

Participants: This study employed a combination of Vector Autoregression (VAR) model and Graph Neural Networks (GNN) to systematically construct dynamic causal inference. Multiple classic classification algorithms were compared, including Random Forest, Logistic Regression, XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gradient Boosting, and Multi Layer Perceptron (MLP). The SMOTE algorithm was used to undersample a small number of samples and employed Stratified K-fold Cross Validation. Results: This study included a total of 11,789 participants, including 6,334 females (53.73%) and 5,455 males (46.27%), with an average age of 65 years. Introduction of dynamic causal inference features has significantly improved the performance of almost all models. The area under the ROC curve of each model ranged from 0.78 to 0.83, indicating significant difference (P < 0.01). Among all the models, the Gradient Boosting model demonstrated the highest performance and stability. Model explanation and feature importance analysis generated model interpretation that illustrated significant contributors associated with risks of stroke. Conclusions and Relevance: This study proposes a stroke risk prediction method that combines dynamic causal inference with machine learning models, significantly improving prediction accuracy and revealing key health factors that affect stroke. The research results indicate that dynamic causal inference features have important value in predicting stroke risk, especially in capturing the impact of changes in health status over time on stroke risk. By further optimizing the model and introducing more variables, this study provides theoretical basis and practical guidance for future stroke prevention and intervention strategies.

Research has increasingly explored the application of artificial intelligence (AI) and machine learning (ML) within the mental health domain to enhance both patient care and healthcare provider efficiency. Given that mental health challenges frequently emerge during early adolescence -- the critical years of high school and college -- investigating AI/ML-driven mental health solutions within the education domain is of paramount importance. Nevertheless, conventional AI/ML techniques follow a centralized model training architecture, which poses privacy risks due to the need for transferring students' sensitive data from institutions, universities, and clinics to central servers. Federated learning (FL) has emerged as a solution to address these risks by enabling distributed model training while maintaining data privacy. Despite its potential, research on applying FL to analyze students' mental health remains limited. In this paper, we aim to address this limitation by proposing a roadmap for integrating FL into mental health data analysis within educational settings. We begin by providing an overview of mental health issues among students and reviewing existing studies where ML has been applied to address these challenges. Next, we examine broader applications of FL in the mental health domain to emphasize the lack of focus on educational contexts. Finally, we propose promising research directions focused on using FL to address mental health issues in the education sector, which entails discussing the synergies between the proposed directions with broader human-centered domains. By categorizing the proposed research directions into short- and long-term strategies and highlighting the unique challenges at each stage, we aim to encourage the development of privacy-conscious AI/ML-driven mental health solutions.

With the rapid development of artificial intelligence technology, its application in the optimization of complex computer systems is becoming more and more extensive. Edge computing is an efficient distributed computing architecture, and the health status of its nodes directly affects the performance and reliability of the entire system. In view of the lack of accuracy and interpretability of traditional methods in node health status judgment, this paper proposes a health status judgment method based on XGBoost and combines the SHAP method to analyze the interpretability of the model. Through experiments, it is verified that XGBoost has superior performance in processing complex features and nonlinear data of edge computing nodes, especially in capturing the impact of key features (such as response time and power consumption) on node status. SHAP value analysis further reveals the global and local importance of features, so that the model not only has high precision discrimination ability but also can provide intuitive explanations, providing data support for system optimization. Research shows that the combination of AI technology and computer system optimization can not only realize the intelligent monitoring of the health status of edge computing nodes but also provide a scientific basis for dynamic optimization scheduling, resource management and anomaly detection. In the future, with the in-depth development of AI technology, model dynamics, cross-node collaborative optimization and multimodal data fusion will become the focus of research, providing important support for the intelligent evolution of edge computing systems.