Francesca Ieva

Tantardini, M.; Ieva, F.; Tajoli, L.; Piccardi, C.
Comparing methods for comparing networks

Masci, C.; Ieva, F.; Agasisti, T.; Paganoni A.m.
Evaluating class and school effects on the joint achievements in different subjects: a bivariate semi-parametric mixed-effects model

Gasperoni, F.; Ieva, F.; Paganoni, A.m.; Jackson, C.; Sharples, L.
Evaluating the effect of healthcare providers on the clinical path of Heart Failure patients through a novel semi-Markov multi-state model

Massi, M.c.; Ieva, F.; Lettieri, E.
Data Mining Application to Healthcare Fraud Detection: A Two-Step Unsupervised Clustering Model for Outlier Detection with Administrative Databases

Fontana, L.; Masci, C.; Ieva, F.; Paganoni, A.m.
Performing Learning Analytics via Generalized Mixed-Effects Trees

Ieva, F.; Palma, F.; Romo, J.
Bootstrap-based Inference for Dependence in Multivariate Functional Data

Ieva, F.; Bitonti, D.
Network Analysis of Comorbidity Patterns in Heart Failure Patients using Administrative Data

Ekin, T.; Ieva, F.; Ruggeri, F.; Soyer, R.
Statistical Medical Fraud Assessment: Exposition to an Emerging Field

Masci, C.; Paganoni, A.m.; Ieva, F.
Non-parametric mixed-effects models for unsupervised classification of Italian schools

Gasperoni, F.; Ieva, F.; Paganoni, A.m.; Jackson C.h.; Sharples L.d.
Nonparametric frailty Cox models for hierarchical time-to-event data

Martino, A.; Ghiglietti, A.; Ieva, F.; Paganoni, A.m.
A k-means procedure based on a Mahalanobis type distance for clustering multivariate functional data

Bottle, A.; Ventura, C.m.; Dharmarajan, K.; Aylin, P.; Ieva, F.; Paganoni, A.m.
Regional variation in hospitalisation and mortality in heart failure: comparison of England and Lombardy using multistate modelling

Paulon, G.; De Iorio, M.; Guglielmi, A.; Ieva, F.
Joint modelling of recurrent events and survival: a Bayesian nonparametric approach

Ghiglietti, A.; Scarale, M.g.; Miceli, R.; Ieva, F.; Mariani, L.; Gavazzi, C.; Paganoni, A.m.; Edefonti, V.
Urn models for response-adaptive randomized designs: a simulation study based on a non-adaptive randomized trial

Gasperoni, F.; Ieva, F.; Barbati, G.; Scagnetto, A.; Iorio, A.; Sinagra, G.; Di Lenarda, A.
Multi state modelling of heart failure care path: a population-based investigation from Italy

Ekin, T.; Ieva, F.; Ruggeri, F.; Soyer, R.
On the Use of the Concentration Function in Medical Fraud Assessment

Tarabelloni, N.; Schenone, E.; Collin, A.; Ieva, F.; Paganoni, A.m.; Gerbeau, J.-f.
Statistical Assessment and Calibration of Numerical ECG Models

Ieva, F.; Paganoni, A.m.
A taxonomy of outlier detection methods for robust classification in multivariate functional data

Tarabelloni, N.; Ieva, F.
On Data Robustification in Functional Data Analysis

Ghiglietti, A.; Ieva, F.; Paganoni, A.m.
Statistical inference for stochastic processes: two sample hypothesis tests

Guglielmi, A.; Ieva, F.; Paganoni, A.m.; Quintana, F.a.
A semiparametric Bayesian joint model for multiple mixed-type outcomes: an Application to Acute Myocardial Infarction

Agasisti,t.; Ieva, F.; Masci, C.; Paganoni, A.m.
Does class matter more than school? Evidence from a multilevel statistical analysis on Italian junior secondary school students

Masci, C,; Ieva, F.; Agasisti, T.; Paganoni, A.m.
Bivariate multilevel models for the analysis of mathematics and reading pupils' achievements

Ghiglietti, A.; Ieva, F.; Paganoni, A.m.; Aletti, G.
On linear regression models in infinite dimensional spaces with scalar response

Ieva, F.; Paganoni, A.m., Pietrabissa, T.
Dynamic clustering of hazard functions: an application to disease progression in chronic heart failure

Ieva, F., Jackson, C.h., Sharples, L.d.
Multi-State modelling of repeated hospitalisation and death in patients with Heart Failure: the use of large administrative databases in clinical epidemiology

Ieva, F., Paganoni, A.m., Tarabelloni, N.
Covariance Based Unsupervised Classification in Functional Data Analysis

Agasisti, T.; Ieva, F.; Paganoni, A.m.
Heterogeneity, school-effects and achievement gaps across Italian regions: further evidence from statistical modeling

Biasi, R.; Ieva, F.; Paganoni, A.m.; Tarabelloni, N.
Use of depth measure for multivariate functional data in disease prediction: an application to electrocardiographic signals

Ieva, F.; Marra, G.; Paganoni, A.m.; Radice, R.
A semiparametric bivariate probit model for joint modeling of outcomes in STEMI patients

Ekin, T.; Ieva, F.; Ruggeri, F.; Soyer, R.
Statistical Issues in Medical Fraud Assessment

Ieva, F.; Paganoni, A.m.
Detecting and visualizing outliers in provider profiling via funnel plots and mixed effect models

Ieva, F.; Paganoni, A.m.
Risk Prediction for Myocardial Infarction via Generalized Functional Regression Models

Ieva, F.; Paganoni, A.m.; Ziller, S.
Operational risk management: a statistical perspective

Guglielmi, A.; Ieva, F.; Paganoni, A.m.; Ruggeri, F.; Soriano, J.
Semiparametric Bayesian models for clustering and classification in presence of unbalanced in-hospital survival

Ieva, F.; Paganoni, A. M.; Zanini, P.
Statistical models for detecting Atrial Fibrillation events

Guglielmi, A.; Ieva, F.; Paganoni, A.m.; Ruggeri, F.
Hospital clustering in the treatment of acute myocardial infarction patients via a Bayesian semiparametric approach

Ieva, F.; Paganoni, A.m.
Depth Measures For Multivariate Functional Data

Azzimonti, L.; Ieva, F.; Paganoni, A.m.
Nonlinear nonparametric mixed-effects models for unsupervised classification

Ieva, F.; Paganoni, A.m.; Secchi, P.
Mining Administrative Health Databases for epidemiological purposes: a case study on Acute Myocardial Infarctions diagnoses

Ieva, F.; Paganoni, A.m.; Pigoli, D.; Vitelli, V.
Multivariate functional clustering for the analysis of ECG curves morphology

Baraldo, S.; Ieva, F.; Paganoni, A. M.; Vitelli, V.
Generalized functional linear models for recurrent events: an application to re-admission processes in heart failure patients

Ieva, Francesca; Paganoni, Anna Maria
Designing and mining a multicenter observational clinical registry concerning patients with Acute Coronary Syndromes

Grieco, Niccolê; Ieva, Francesca; Paganoni, Anna Maria
Provider Profiling Using Mixed Effects Models on a Case Study concerning STEMI Patients

Guglielmi, Alessandra; Ieva, Francesca; Paganoni, Anna Maria; Ruggeri, Fabrizio
A Bayesian random-effects model for survival probabilities after acute myocardial infarction

Ieva, Francesca; Paganoni, Anna Maria
Multilevel models for clinical registers concerning STEMI patients in a complex urban reality: a statistical analysis of MOMI^2 survey

Barbieri, P.; Grieco, N.; Ieva, F.; Paganoni, A. M.; Secchi, P.
Exploitation, integration and statistical analysis of Public Health Database and STEMI archive in Lombardia Region

Ieva, Francesca; Paganoni, Anna Maria
A case study on treatment times in patients with ST-Segment Elevation Myocardial Infarction

Grieco, Niccolò; Corrada, Elena; Sesana, Giovanni; Fontana, Giancarlo; Lombardi, Federico; Ieva, Francesca; Paganoni, Anna Maria; Marzegalli, Maurizio
Predictors of the reduction of treatment time for ST-segment elevation myocardial infarction in a complex urban reality. The MoMi2 survey

Author:Bitonti, Daniele
Advisors:Ieva, F.
Network Analysis of Comorbidity Patterns in Heart Failure Patients using Administrative Data

Author:Ventura, Chiara Maria
Advisors:Paganoni, A.m. Ieva, F.
Models for predicting readmissions in heart failure patients: a comparison between Lombardia and England

Author:Indino, Federico Siro
Advisors:Paganoni, A.m. Ieva, F.
Analisi statistica di dati ad alta dimensionalità: una applicazione ai segnali elettrocardiografici

Author:Desgranges, Nina Ines Bertille
Advisors:Paganoni, A.m. Ieva, F.
Generalization of the PC algorithm for non-linear and non-Gaussian data and its application to biological data

Author:Gasperoni, Francesca
Advisors:Paganoni, A. Ieva, F.
Frailty multi-state models for the analysis of heart failure patients

Author:Tarabelloni Nicholas
Advisors:Paganoni, A.m. Ieva, F.
Metodi numerici e statistici per la simulazione e validazione di ECG

Author:Ieva, Francesca
Advisors:Paganoni, A.m.
Statistical methods for classification in cardiovascular healthcare

Author:Zanini, Paolo
Advisors:Paganoni, A.m. Ieva, F. Vitelli, V.
Modelli statistici per lo studio della Fibrillazione Atriale

Author:Cremaschi, Andrea ; Ziller, Stefano
Advisors:Paganoni, A. M. Ieva, F.
Il problema del record linkage tra dataset: un approccio probabilistico

Author:Ieva, Francesca
Advisors:Paganoni, A.m. Sesana, G.
Modelli statistici per lo studio dei tempi di intervento nell infarto miocardico acuto

Author:Ieva, Francesca; Martinelli, Gabriele
Advisors:Paganoni, Anna Maria
Modelli stocastici e deterministici per la crescita tumorale: teoria e simulazione

Advisor:Ieva, F.,Ceri, S.
Genomic-based prediction of chemotherapy-immune behavior in patients affected by Hodgkin Lymphoma


Advisor:Ieva, F.,
Hazard based funnel plots: identifyin outliers in cancer survival


Advisor:Ieva, F.,
Applicazione di tecniche di Machine Learning per l'analisi di immagini in ambito radio oncomico


Advisor:Ieva, F.,Paganoni, A.m.
Machine learning for personalized treatment planning of radiotherapy

Farb - Health And Education Systems Assessment,Politecnico Di Milano
The core interest of this project is in improving the quality of health and educational services at local, regional and national levels, through a program of applied research and close involvement with the Italian Ministry of Health, the Italian Institute for the Evaluation of Educational Systems (Invalsi) and other healthcare/educational organizations and institutions. Studying and monitoring outcomes and process indicators in healthcare and educational processes allow decision makers to improve the system performances with an optimized resource allocation; indeed, decision-makers involved in public administration (health/education) need information about efficiency and effectiveness of provided services. In this project, we propose an innovative knowledge discovery model that takes advantage by the massive use of administrative data that have been collected only with storage and control purposes. Administrative datasets are naturally updated, complete and available with a lower cost with respect to ad-hoc data collections. Even if their use has been questioned, the recent statistical literature points out that the risks of biased results can be minimized with suitable statistical and reporting techniques. Conditionally to a field of interest (pathology or pattern of care for health care students' achievement for education) a main outcome jointly with possibly quite secondary ones, are pointed out. To improve the outcomes is mandatory to understand the variables correlated, and eventually their causal impact on them. Specifically the variability affecting outcomes can be decomposed in many different sources. Due to the natural hierarchical structure of data (patients within divisions, divisions within hospitals, pupils within classes, classes within schools, …) they are affected by over-dispersion that can be catched and modeled by means of statistical “mixed effect models”. Despite the relevance of such research, funding is at moment very problematic. Public decision-makers need to see first results before committing in this direction. Private funding (e.g. biomedical companies interested to follow-up their technologies) need a commitment from the regional/national Institutions that allow them to proceed in this direction. Within this background, we think that FARB funding is a tremendous opportunity to seed this new research stream and create the first results useful to create the conditions for a next public and private funding context. The main objects of the project are then i) identification and study of the outcomes and process indicators for the problem of interest, ii) monitoring and analysis of these parameters, iii) supporting decisions in order to improve the process, iv) checking of the change effectiveness, or at least implementing a method for this and testing it; v) increasing the present awareness about the value of opening and using administrative databases for informing decision-making and promote both the public and the private funding of further researches in this direction. Within this background, during the three year FARB project we will start working in two specific contexts: a) the healthcare process for patients affected by heart failure, b) educational attainments of students at grades 2, 5 (primary education), 6 and 8 (lower secondary schooling). In both the cases, we have already been authorized to use the related administrative databases for this research.

Heart,Azienda Ospedaliera Niguarda Cà Granda
Utilization of regional health services databases for evaluating epidemiology, short-and medium-term out come, and process indexes in patients hospitalized for heart failure

Programma Strategico,Ministero Della Salute, Regione Lombardia
Exploitation, integration and study of current and future health databases in Lombardia for Acute Coronary Sindromes