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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07/2023 - BMC Medical Research Methodology
Capturing the variety of clinical pathways in patients with schizophrenic disorders through state sequences analysis
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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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