Biomedical Data Science Lab

A flexible and powerful Python library for dataset shift analysis and characterization, providing supervised and unsupervised evaluation of temporal and multi-source data shifts, visualization tools, and statistical insights for data integrity and model performance monitoring

Examining Associations Between Passive Smartphone Sensing and Daily Self-Perceived Health in Older Adults in Rural Spain: An Exploratory Longitudinal Observational Study

Numerical data imputation methods for extremely missing data contexts

Code and data to detect and mitigate overrepresentation bias in blood-brain barrier permeability (BBBP) prediction models, as presented in the paper "Overrepresentation bias leads to performance overestimation in blood-brain barrier permeability prediction models: characterization and mitigation".

A framework for developing trustworthy health AI with tutorial notebooks, including technical solutions for the different trustworthy AI requirements through the AI lifecycle

Epione microsimulation APP

Deep continual learning pipelines for out-of-hospital medical emergencies text classification under the presence of dataset shifts.

Code run to analyze the Data Quality (DQ) gathered within the CANCERLESS project

AI Passport proposal for MIE 2024

To describe age-gender unbiased COVID-19 subphenotypes regarding severity patterns through a two-stage clustering approach using patient phenotypes and demographic features. Additional source and temporal variability assessments are included as part of data quality analyses.