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Original Research | Open access
Digital Twin Framework Integrating Patient-Specific Computational Models and Real-Time Wearable Data for Personalized Management of Chronic Obstructive Pulmonary Disease Exacerbations
Digital twin, Wearable sensors, Chronic obstructive pulmonary disease, Exacerbation prediction, Patient-specific modelling, Data assimilation
Chronic obstructive pulmonary disease (COPD) is a leading cause of death, with exacerbations worsening functional decline, reducing quality of life, and increasing healthcare use. Current management remains reactive, with treatment often initiated only after symptoms worsen. Existing monitoring approaches struggle to distinguish between clinically significant deterioration and normal variability, leading to delayed intervention. This article proposes a digital twin framework combining patient-specific respiratory models with real-time wearable data to predict and manage COPD exacerbations proactively. The framework includes a mechanistic lung model, continuous data ingestion, a data assimilation module, an exacerbation prediction layer, and an alert system, enabling early detection of physiological deviations before severe symptoms arise. By supporting pre-emptive telehealth, medication adjustments, and patient self-management with clinician oversight, this approach could shift COPD care from reactive to personalized, proactive management, pending robust modeling, reliable sensing, and real-world validation.
Published: 20 July 2026
Original Research | Open access
Deep Reinforcement Learning with Inverse Reinforcement Learning for Learning Optimal Personalized Rehabilitation Exercise Prescriptions from Physical Therapist Demonstrations
Deep reinforcement learning, Inverse reinforcement learning, Personalized rehabilitation, Physical therapist demonstrations, Exergaming, Reward learning
Personalized rehabilitation exercise prescriptions are essential for recovery after neurological injury, orthopedic surgery, and chronic decline. While physical therapists have valuable expertise, translating it into scalable computational systems is challenging. Standard deep reinforcement learning relies on manually defined reward functions, but in rehabilitation, clinically significant goals like movement quality, fatigue, pain, safety, motivation, and adherence are difficult to quantify. This paper introduces a framework combining inverse reinforcement learning (IRL) and deep reinforcement learning (DRL) to learn personalized rehabilitation prescriptions from therapist demonstrations. IRL would derive expert-aligned rewards, and DRL would use these to create adaptive exercise plans. The framework encompasses therapist demonstration collection, movement trajectory representation, reward inference, policy learning, safety constraints, and clinical oversight. Demonstrations would include exercise selection, progression decisions, and therapist responses to patient fatigue, pain, or adherence issues. IRL could capture implicit clinical priorities, while DRL would adjust prescriptions based on patient conditions such as fatigue, progress, and engagement. The framework aims to create scalable, personalized rehabilitation prescriptions, offering a conceptual model for future rehabilitation robotics, exergaming, and home-based digital rehabilitation systems.
Published: 20 July 2026
Original Research | Open access
Reinforcement Learning Framework for Dynamic Optimization of Extracorporeal Membrane Oxygenation Settings Using Real-Time Blood Gas, Hemodynamic, and Pump Flow Measurements
Reinforcement learning, Extracorporeal membrane oxygenation, Offline reinforcement learning, Blood gas monitoring, Hemodynamic monitoring, Pump flow
Extracorporeal membrane oxygenation (ECMO) is used to support patients with severe cardiac or respiratory failure, requiring constant manual adjustments of pump flow, sweep gas flow, and oxygen fraction. However, current ECMO management lacks a real-time optimization system tailored to individual patient needs. This manuscript proposes an offline reinforcement learning framework for dynamic ECMO optimization, utilizing real-time measurements of blood gases, hemodynamics, and pump flow. The framework includes a state encoder for various patient data, an action space for adjustments to ECMO settings, and a reward function that balances oxygenation, hemodynamic support, and complication avoidance. A safety shield filters unsafe recommendations before clinician review. The system aims to provide personalized, proactive, and safety-constrained ECMO management, with the goal of guiding future research validation rather than claiming experimental results.
Published: 20 July 2026
Original Research | Open access
Federated Learning with Differential Privacy and Secure Multi-Party Computation for Training Rare Disease Detection Models Across 50 International Hospitals without Centralizing Data
Federated learning, Rare disease detection, Differential privacy, Secure multi-party computation, Privacy-preserving artificial intelligence, Cross-hospital machine learning
Detecting rare diseases often requires data from multiple institutions due to the scarcity of cases at individual hospitals. Centralizing data is not feasible due to privacy, consent, and jurisdictional issues. Federated learning enables model training across hospitals without transferring raw data, but it lacks formal privacy guarantees. Model updates can still leak information, and aggregation servers may compromise privacy if they handle unprotected data. This article presents a conceptual framework combining federated learning, differential privacy, and secure multi-party computation for rare disease detection across 50+ international hospitals. The system addresses data scarcity, regulatory fragmentation, and network heterogeneity. Each hospital trains a local model, applies differential privacy to updates, and shares encrypted updates via an aggregation protocol. Non-colluding servers compute global updates without accessing plaintext data. Differential privacy reduces the impact of individual patient data, while secure multi-party computation ensures privacy at the aggregation layer. These methods enable a privacy-preserving approach to federated learning for rare disease collaboration. The proposed framework enables multi-continental rare disease detection without centralizing patient data, offering a privacy-preserving model for future consortia.
Published: 20 July 2026
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