Transition-Based Digital Twin Modelling for Alzheimer's Disease under Sparse Longitudinal Data
arXiv:2606.09671v1 Announce Type: new Abstract: Alzheimer's disease (AD) progression is highly heterogeneous and is typically observed through sparse and irregular longitudinal data, posing challenges for prediction and personalised monitoring. Existing machine learning approaches have improved AD prediction using multimodal data, yet often focus on static classification or cohort-level risk estimation, providing limited support for subject-specific modelling and uncertainty-aware reasoning. To address these limitations, we present a personalised digital twin framework for AD prediction and sc
Advances in machine learning, particularly in handling sparse longitudinal data, are enabling more sophisticated applications in personalized medicine, pushing beyond traditional cohort-level analyses.
This development represents a significant step towards personalized healthcare, offering the potential for earlier and more precise interventions for complex diseases like Alzheimer's. It could improve quality of life for millions and reduce long-term healthcare burdens.
The ability to create personalized digital twins changes how chronic, complex diseases are monitored and treated, moving from reactive, generalized approaches to proactive, individualized precision medicine.
- · Healthcare providers
- · Patients with chronic diseases
- · AI-driven biotech companies
- · Medical device manufacturers
- · Traditional diagnostic companies
- · Healthcare systems unprepared for data integration
Individual patient outcomes and quality of life improve due to personalized disease management.
Healthcare costs shift from late-stage interventions to earlier, more customized preventative and management strategies.
The concept of 'digital health passports' or highly personalized health profiles gains traction, raising new ethical and data privacy concerns.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG