Francesca Ieva
- 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 è Associate Head dell’Health Data Science Centre di Human Technopole e docente di Statistica al Politecnico di Milano. Ha conseguito il dottorato di ricerca in Modelli e metodi matematici per l’ingegneria nel 2012. La sua ricerca si concentra sull’apprendimento statistico in ambito biomedico e sullo sviluppo di modelli avanzati per l’integrazione di dati clinici complessi, per informare le previsioni nel processo decisionale clinico e per supportare la medicina di precisione e le politiche di precisione.
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Pubblicazioni
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09/2022 - Clinical Epigenetics
A blood DNA methylation biomarker for predicting short-term risk of cardiovascular events
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 […]
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09/2022 - 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 […]
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08/2021 - Radiotherapy & Oncology
PH-0656 Prediction of toxicity after prostate cancer RT: the value of a SNP-interaction polygenic risk score
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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 […]
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