Computational analysis of medical and health datasets using statistical and machine learning methods.
Work can include exploratory analysis, prediction, classification, feature engineering, model evaluation, and explainability.
Applications include research datasets, observational studies, medical imaging data, and computational health research.
Implementation is selected according to the available data, technical constraints, required level of automation, and the existing software or research environment. The solution can be implemented as a standalone component or integrated into a larger system.
The resulting system is intended to provide a clear computational workflow that can be evaluated, maintained, and extended as the project develops. Model choice, data processing, interfaces, and deployment can therefore be adapted to the requirements of the specific project.