Axis 2
Modelling and Numerical Methods
Focus: To create, validate and foster data modeling and adoption of trusted algorithms, including AI
Data Integrity – Digital Trust – Community Health
Axis 2:
Modelling and Numerical Methods
This axis is dedicated to advancing the field of modelling and numerical methods, focusing on developing and applying sophisticated algorithms and computational techniques that can improve healthcare decision-making and outcomes. Here’s a detailed exploration of this axis:
- Development of Advanced Models: Crafting cutting-edge models that simulate complex biological, behavioural, and clinical processes to predict outcomes and guide healthcare decisions.
- Validation and Standardization: Implementing rigorous testing and validation protocols to ensure that models are accurate, reliable, and generalizable across different populations and settings.
- Integration of AI and Machine Learning: Leveraging artificial intelligence and machine learning to enhance the predictive power of models, enabling more personalized and effective healthcare interventions.
- Numerical Methods for Healthcare Data: Employing numerical methods to solve problems in healthcare data analysis, optimizing the extraction of meaningful information from large datasets.
- Algorithm Transparency and Trust: Ensuring that algorithms are transparent and understandable to users, building trust in digital health technologies and their applications.
- Collaborative Modeling Projects: Fostering collaboration between mathematicians, data scientists, clinicians, and policymakers to co-create models that address real-world healthcare challenges.
Modelling and Numerical Methods
Modelling and numerical methods are fundamental to digital health research, offering powerful tools to simulate complex biological and healthcare processes, predict outcomes, and optimize treatments. These techniques allow for constructing detailed, data-driven models that mirror real-life scenarios, providing insights crucial for advancing medical knowledge and patient care. By integrating sophisticated mathematical and computational approaches, researchers can dissect large datasets, enhance the accuracy of predictions, and evaluate the effectiveness of different health interventions. Applying these methods in digital health accelerates innovation and improves the precision and effectiveness of healthcare solutions, making them indispensable in the quest to address today’s most pressing health challenges.
