- Associate Head of Research centre, Health Data Science
- Associate Professor of Statistics at MOX - Modeling and Scientific Computing laboratory, Department of Mathematics, Politecnico di Milano, Health Data Science
- Group Leader, Di Angelantonio & Ieva Group
Francesca Ieva is an associate professor of Statistics at Politecnico di Milano. She got the PhD in Mathematical Models and Methods for Engineering at Politecnico in 2012, then she was hosted by the MRC Biostatistic Unit in Cambridge before becoming a junior researcher at Università statale di Milano (Department of Mathematics) in 2013 and a tenure track professor at MOX – Modelling and Scientific Computing lab, within the Department of Mathematics of Politecnico di Milano, in 2016.
Her research activity has been always focused on statistical learning in biomedical context, both from a methodological and applied point of view. In particular, she deals with health analytics for complex data in medicine.
Her main contributions regard the application of Functional Data Analysis, Survival models, Artificial Intelligence and Nonparametric Statistics to administrative data, electronic health records, medical imaging and genomic data, with the aim of defining suitable patient representation to inform predictive models for personalized medicine.
- 09/2022 - Clinical Epigenetics
Background Recent evidence highlights the epidemiological value of blood DNA methylation (DNAm) as surrogate biomarker for exposure to risk factors for non-communicable diseases (NCD). DNAm surrogate of exposures predicts diseases and longevity better than self-reported or measured exposures in many cases. Consequently, disease prediction models based on blood DNAm surrogates may outperform current state-of-the-art prediction […]
- 02/2022 - PloS CompBio
Deep Survival EWAS approach estimating risk profile based on pre-diagnostic DNA methylation: an application to Breast Cancer time to diagnosis
Previous studies for cancer biomarker discovery based on pre-diagnostic blood DNA methylation profiles, either ignore the explicit modeling of the time to diagnosis (TTD) as in a survival analysis setting, or provide inconsistent results. This lack of consistency is likely due to the limitations of standard EWAS approaches, that model the effect of DNAm at […]
- 08/2021 - Radiotherapy & Oncology
- 08/2020 - Biometrical Journal
Dynamic monitoring of the effects of adherence to medication on survival in heart failure patients: A joint modeling approach exploiting time-varying covariates
Adherence to medication is the process by which patients take their drugs as prescribed, and represents an issue in pharmacoepidemiological studies. Poor adherence is often associated with adverse health conditions and outcomes, especially in case of chronic diseases such as heart failure (HF). This turns out in an increased request for health care services, and […]