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Aggregating person-level data across multiple clinical study sites is often constrained by privacy regulations, necessitating the development of decentralized modeling approaches in biomedical research. To address this requirement, a federated nonlinear regression algorithm based on the Choquet integral has been introduced for outcome prediction. This approach avoids reliance on prior statistical assumptions about data distribution and captures feature interactions, reflecting the non-additive nature of biomedical data characteristics. This work represents the first theoretical application of Choquet integral regression to multisite longitudinal trial data within a federated learning framework. The Multiple Imputation Choquet Integral Regression with LASSO (MIChoquet-LASSO) algorithm is specifically designed to reduce overfitting and enable variable selection in federated learning settings. Its performance has been evaluated using synthetic datasets, publicly available biomedical datasets, and proprietary longitudinal randomized controlled trial data. Comparative evaluations were conducted against benchmark methods, including ordinary least squares (OLS) regression and Choquet-OLS regression, under various scenarios such as model misspecification and both linear and nonlinear data structures in non-federated and federated contexts. Mean squared error was used as the primary performance metric. Results indicate that MIChoquet-LASSO outperforms compared models in handling nonlinear longitudinal data with missing values, particularly in scenarios prone to overfitting. In federated settings, Choquet-OLS underperforms, whereas the federated variant of the model, FEDMIChoquet-LASSO, demonstrates consistently better performance. These findings suggest that FEDMIChoquet-LASSO offers a reliable solution for outcome prediction in multisite longitudinal trials, addressing challenges such as missing values, nonlinear relationships, and privacy constraints while maintaining strong performance within the federated learning framework. 

Digital Twin (DT) technology offers real-time monitoring, simulation, optimization, and precise forecasting. However, its theoretical framework and practical implementation, particularly in digital random controlled trials (RCT), remain underdeveloped, limiting its full potential. Therefore, we seek to develop an application of digital twins with the virtual reality using data from four longitudinal RCT in Massachusetts. As case studies, we create digital twins of two individuals from these RCT. This application of digital twins provides an avenue for a more personalized healthcare experience for patients, the enhancement of medical simulations, and a visualization for predictive analytics. In the future, we will move from virtual reality to extended reality with AI-generated digital twin models. 

Not AvailableThis work presents a digital twin framework based on smart rings. The compact design of smart rings with a composite sensor enables a digital twin as a viable option for mobile health monitoring, analysis and prediction of biomarkers over time. Built upon sensor data from a smart ring such as Photoplethysmogram (PPG), peripheral oxygen saturation (SpO2), physical motion, and others, a digital twin can provide continuous predictive insights. This framework enables the detection of anomalies in heart rate, monitoring of sleep patterns, evaluation of blood oxygen levels, and assessment of stress. Additionally, it integrates these findings into visual representations, enhancing the understanding of health outcomes. 

Obesity is a common, serious, and costly chronic disease in the United States, a risk factor for several major cancers, linked to higher rates of illness and death. It is thus a critical issue that needs attention from health care professionals and the public alike. We use a novel approach to target nonstandard variations to better understand the variables associated with weight loss. We introduce a new methodology using the Choquet Integral with fuzzy measure, an approach that accounts for interactions between measured features. The Choquet Integral has limited sourced applications to the biomedical field despite widespread use in theoretical mathematics and economics. Our technique applies it to health data to show a robust method to target and optimize weight loss parameters. We identify data versus noise, optimally choose a reduced version of the powerset for computability purposes, and identify the sub-additive cooperative learning bound of the Choquet Integral. We show that the proposed technique targets heretofore unknown variations in predictive weight loss studies with broad potential applications. 

Digital technology has huge potentials in transforming clinical trial research. One common issue in digital clinical trials for long-term behavioral treatments is incomplete longitudinal data, as subjects’ behavior changes over time. In this paper, we aim to improve the fuzzy clustering accuracy and stability of digital clinical trials by intelligently searching for the optimal fuzzifier, which is the key to identify the optimal number of overlapped clusters for incomplete longitudinal data. Our findings showed that integrating optimal fuzzifier searching with cluster validation can streamline the clustering process, thus enabling the intelligent fuzzy clustering procedure. 

Traditional implementations of federated learning for preserving data privacy are unsuitable for longitudinal health data. To remedy this, we develop a federated enhanced fuzzy c-means clustering (FeFCM) algorithm that can identify groups of patients based on complex behavioral intervention responses. FeFCM calculates a global cluster model by incorporating data from multiple healthcare institutions without requiring patient observations to be shared. We evaluate FeFCM on simulated clusters as well as empirical data from four different dietary health studies in Massachusetts. Results find that FeFCM converges rapidly and achieves desirable clustering performance. As a result, FeFCM can promote pattern recognition in longitudinal health studies across hundreds of collaborating healthcare institutions while ensuring patient privacy.