Francesca Ieva

Associate Professor

Phone:+39 02 2399 4578
Fax: +39 02 2399
Office: Dip. di matematica - VI floor
Personal web page:

  • Available MOX Reports
  • Theses
  • Thesis Proposals
  • MOX Projects

Cavinato, L.; Sollini, M.; Kirienko, M.; Biroli, M.; Ricci, F.; Calderoni, L.; Tabacchi, E.; Nanni, C.; Zinzani, P. L.; Fanti, S.; Guidetti, A.; Alessi, A.; Corradini, P.; Seregni, E.; Carlo-stella, C.; Chiti, A.; Ieva, F.;
PET radiomics-based lesions representation in Hodgkin lymphoma patients

Massi, M. C.; Ieva, F.
Representation Learning Methods for EEG Cross-Subject Channel Selection and Trial Classification

Massi, M.c.; Franco, N.r; Ieva, F.; Manzoni, A.; Paganoni, A.m.; Zunino, P.
High-Order Interaction Learning via Targeted Pattern Search

Masci, C.; Ieva, F.; Paganoni A.m.
EM algorithm for semiparametric multinomial mixed-effects models

Cannistrà,m.; Masci, C.; Ieva, F.; Agasisti, T.; Paganoni, A.m.
Not the magic algorithm: modelling and early-predicting students dropout through machine learning and multilevel approach

Sollini, M.; Kirienko, M.; Cavinato, L.; Ricci, F.; Biroli, M.; Ieva, F.; Calderoni, L.; Tabacchi, E.; Nanni, C.; Zinzani, P.l.; Fanti, S.; Guidetti, A; Alessi, A.; Corradini, P.; Seregni, E.; Carlo-stella, C.; Chiti, A.
Methodological framework for radiomics applications in Hodgkin's lymphoma

Pellagatti, M.; Masci, C.; Ieva, F.; Paganoni A.m.
Generalized Mixed-Effects Random Forest: a flexible approach to predict university student dropout

Massi, M.c., Gasperoni, F., Ieva, F., Paganoni, A.m., Zunino, P., Manzoni, A., Franco, N.r., Et Al.
A deep learning approach validates genetic risk factors for late toxicity after prostate cancer radiotherapy in a REQUITE multinational cohort

Spreafico, M.; Ieva, F.; Fiocco, M.
Modelling dynamic covariates effect on survival via Functional Data Analysis: application to the MRC BO06 trial in osteosarcoma

Spreafico, M.; Ieva, F.
Functional modelling of recurrent events on time-to-event processes

Rea, F.; Ieva, F.,; Pastorino, U.; Apolone, G.; Barni, S.; Merlino, L.; Franchi, M.; Corrao, G.
Number of lung resections performed and long-term mortality rates of patients after lung cancer surgery: evidence from an Italian investigation

Ieva, F; Paganoni, A.m.; Romo, J.; Tarabelloni, N.
roahd Package: Robust Analysis of High Dimensional Data

Spreafico, M.; Ieva, F.
Dynamic monitoring of the effects of adherence to medication on survival in Heart Failure patients: a joint modelling approach exploiting time-varying covariates

Massi, M.c.; Ieva, F.; Gasperoni, F.; Paganoni, A.m.
Minority Class Feature Selection through Semi-Supervised Deep Sparse Autoencoders

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 dimensionalitÃ: 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

Performance indicators for survival outcome

Performance measures for institutions represent an important aspect concerning services quality. In the last years, there has been an increasing use of performance indicators in health care [1]. A performance indicator is a statistical measurement related to the quality of functioning of an institution. These measures concern different aspects and they must account for all indicators to have a summary outcome as plausible as possible. There is an ongoing debate about the best choice of indicator measures and their reliability. To establish which is the best performance indicator it is a difficult decision both for clinicians and statisticians.  The problem is how well a specific indicator reflects the quality of institution and how to draw conclusions from the estimates obtained by modeling the data. In order to quantify these results, in clinical literature rankings and league tables are usually created.

Performance indicators for hospitals could be for example proportion of complications, observed or expected mortality rate, proportion of patients still alive at a specific time point. When such indicators are built, appropriate statistical methodology to illustrate the presence of uncertainty in the presentation of results and the adjustment for case-mix is required. All individuals’ characteristics, known as case-mix, such as age, sex, diagnosis, stage of disease, therapy administered etc, are generally taken into account in statistical analysis. A fair comparison between centers is made after adjusting for case-mix [2-4].

Funnel plots [2] are well known tool used to compare institutions when the outcome is binary but little research has been performed for survival outcomes. Quaresma et al. [3] provide funnel plots as graphical tools designed to display performance indicators for cancer survival. To compare performance between institutions fixed or random effects models can be used.

The aim of this thesis is to provide performance indicators for survival outcomes to compare hospitals. 

The motivating example comes from data about patients that underwent a surgery for oesophageal cancer in a Dutch hospital. The presence of about 55% of patients with unknown surgery date, which is the starting time for the event of interest, requires an algorithm to estimate this quantity. In this thesis an algorithm to impute missing values will be investigated. A simulations study will be performed to study the performance of method proposed.

  1. Marshall C. and Spiegelhalter D.J., Reliability of league tables of in vitro fertil- ization clinics: retrospective analysis of live birth rates. British Medical Journal, 1998;316:1701-1705.
  2. Spiegelhalter D.J., Funnel plots for comparing institutional performance. Statis- ticsin Medicine, 2005; 24:1185-1202.
  3. Quaresma M., Funnel plots for population-based cancer survival: principles, methods and applications. Statistics in Medicine, 2014; 33: 1070-80.
  4. D. Henneman, A. C. M. van Bommel, A. S. Snijders, R.A. A. E. Tollenaar, M.W.J.M. Wouters, M. Fiocco. Ranking­ and­ rankability­ of­ hospital­ postoperati­ve­ mortality­ rates­ in­ colorectal­ cancer­ surgery. Annals of Surgery 20 4 May;259(5):844-9
  5. He Y., Normand S.-L. T., On the accuracy of classifying hospitals on their performance measures. Statistics in Medicine, 2014; 33:1081-1103.
  6. Laird N. M. and Louis T. A., Empirical Bayes Ranking Methods. Journal of Educational Statistics, Spring 1989, 14: 29-46.

Applicazione di tecniche di Machine Learning per l'analisi di immagini in ambito radio oncomico

Il progresso della radioterapia nella cura delle lesioni tumorali pone sfide importanti alla modellistica di supporto della prognosi medica. L'analisi delle immagini provenienti dalle strumentazioni radiologiche richiede lo sviluppo di opportuni modelli di previsione per personalizzare la terapia del paziente. La tesi si inserisce nella linea di ricerca su Health Analytics.

Health Analytics for Myelodisplastic Syndromes

Il contesto clinico è quello di alcuni tumori del sangue che si chiamano sindromi mielodisplastiche. Si tratta di leucemie croniche rare, che riducono drasticamente l’aspettativa di vita dei pazienti, e per i quali l’unica terapia curativa è il trapianto di midollo. Purtroppo il trapianto fallisce in una percentuale elevata di casi o per recidiva della malattia o per tossicità della procedura di trapianto stessa. Circa 10 anni fa sono cominciati studi per definire le mutazioni geniche ricorrenti alla base della malattia. Le mutazioni sono importanti per predire la prognosi dei pazienti e anche per definire la probabilità di successo del trapianto.

La/le tesi si focalizzerà/anno su:

1) Definire dei modelli predittivi innovativi, che comprendano anche le informazioni genomiche per predire la probabilità di successo del trapianto nel singolo paziente e quindi selezionare in modo ottimale i candidati a questa procedura.

2) Definire nel paziente candidato il momento ottimale (rispetto alla storia naturale della malattia) in cui eseguire la procedura di trapianto. Infatti la malattia ha diversi stati, e soprattutto per i pazienti diagnosticati in fase iniziale, puo’ passare molto tempo prima che la malattia intacchi lo stato di salute della persona. In questi soggetti, eseguire un trapianto subito al momento della diagnosi può risultare sconveniente, perché sarebbero sottoposti ai rischi del trapianto in una fase di malattia in cui l’impatto di salute è invece basso (rapporto rischi/beneficio sfavorevole)

Joint frailty modelling of time-to-event data to elicit the evolution pathway of events: a generalised linear mixed model approach.

La tesi ha risvolti metodologici e applicativi (ambito clinico ed epidemiologico). E' necessario un solido background in statistica applicata e una buona conoscenza di almeno uno dei seguenti software: R, Python.

Fattori di rischio cardiovascolari in soggetti con infezione da HIV

Sviluppo di algoritmi di algoritmi di Machine Learning per la valutazione dei fattori predittivi l’insorgenza di eventi cardiovascolari in soggetti con infezione da HIV con Follow Up di 10 anni.

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