Università Cattolica's Research
Università Cattolica's Research
Digital Innovation & Patient Engagement
The project is led by the EngageMinds HUB research team. Thanks to the non-conditional support of the Fondazione MSD and Datawizard (technological partner), it has worked on the development of an app dedicated to people with HIV to improve the empathic relationship with the infectivologist, allowing the patient to give voice to his emotions, concerns and relational and communicative expectations linked to the encounter with the treating doctor. The patient will also be able to receive advice - also shared with the doctor - to make communication between the two actors increasingly effective.
Professors in charge:
- Guendalina Graffigna, Full Professor of Consumer & Health Psychology
- Serena Barello, Assistant Professor of Consumer Health Psychology
Funding Source: Fondazione MSD
The project aims to assess the impact of using voice assistants by elderly people in their home environment on their well-being and quality of life. Università Cattolica's EngageMinds HUB Research Centre leads it in collaboration with DataWizard and with a non-conditional contribution from Amazon.
Professors in charge:
- Guendalina Graffigna, Full Professor of Consumer & Health Psychology
- Giuseppe Riva, Full Professor of General Psychology
- Serena Barello, Assistant Professor of Consumer Health Psychology
- Silvia Serino, Researcher in General Psychology
- Clelia Malighetti, Ph.D Candidate in Psychology
Funding source: Amazon Media EU SRL
The aim of Gravitate-Health is to develop a digital health information tool called the Gravitate Lens (G-Lens). As the name suggests, the G-Lens will focus approved information on medicines and guide patients to understandable, trustworthy, up-to-date information that meets the patient’s needs and fits with their health context and literacy levels. The functionality of the G-Lens will be supported by an open source digital platform.
Professors in charge:
- Guendalina Graffigna, Full Professor of Consumer & Health Psychology
- Lorenzo Palamenghi, Research fellow at the Faculty of Psychology
- Serena Barello, Assistant Professor of Consumer Health Psychology
Funding source: European Commission - IMI
The prevalence of atrial fibrillation (AF) amongst Europe’s elderly population is growing. Associated with more severe strokes, AF is an abnormal heart rhythm with rapid and irregular beating. In the context of multimorbidity, improving the management of AF is vital and requires an holistic approach. The novel approach underpinning the AFFIRMO project is to focus on clusters of multimorbidity where atrial fibrillation represents one of the chronic conditions.
Improving the management of AF in the context of multimorbidity may benefit individuals on a larger scale, with a holistic approach to optimize clinical management of older AF patients taking into account the multifaceted aspects of individuals’ health, including multimorbidity, polypharmacy, personal preferences, and social context. The challenge is to move from fragmentation to an integrated care strategy designed to be patient-centred. AFFIRMO's answer to this challenge is to develop a holistic care approach based on the 'Atrial Fibrillation Better Care'(ABC) model.
Professors in charge:
- Guendalina Graffigna, Full Professor of Consumer & Health Psychology
- Serena Barello, Assistant Professor of Consumer Health Psychology
Funding source: European Commission - Horizon
Beyond Algorithms: toward a new Humanism
The unprecedented level of transformation and revolution through technology has foremost resulted in a disruption of the traditional representation of the world. At the time algorithms, data mining techniques and the related elaboration through Artificial Intelligence show a number of vulnerabilities, since these processes are managed by private companies that at the same time exercise public functions.
The objective of the research is to explore the space between public functions and private control, with the aim to understand if current multi-stakeholders’ governance strategies are able to fill the gap. In particular, the purpose of the current research is to highlight in which ways the algorithmic exercise of public authority affects time and space and consequently modifies the benchmarks of various domains (legal, economic, social, public health, etc.).
Professor in charge:
- Gabriele Della Morte, Full Professor of International Law
Funding source: D.3.2. (Università Cattolica's Projects of Interest)
AI, kids and children
ySKILLS proposes a holistic, child-centric approach to understanding how the internet has variable consequences for children’s rights to participation, information, freedom of expression, education and play, and protection from harm. ySKILLS examines risks and opportunities related to children’s and adolescents’ (aged 12 to 17) ICT uses and their digital skills to understand how to purposefully use ICTs towards greater cognitive, physical, psychological and social wellbeing.
We offer a new measurement of digital skills in survey and performance tests based on a four-dimension classification of digital skills (technical, information-navigation, social, content-creation). Through a longitudinal three-wave survey in six countries, fMRI in two countries, and in-depth studies among at-risk groups in six complementary countries, ySKILLS will predict which children are more at risk of having low levels of wellbeing because of their ICT use and understand how digital skills can function as building resilience against negative impacts. This results in a comprehensive, evidence-based explanatory and foresight model predicting the complex impacts of ICT use on children’s and adolescents’ wellbeing in Europe, and the role of digital skills that can enhance their wellbeing.
Professor in charge:
- Giovanna Mascheroni, Associate Professor of Sociology of Media and Communication
Funding source: H2020-SC6-TRANSFORMATIONS-2018-2019-2020 RIA, Grant Agreement no. 870612
DataChildFutures will generate robust evidence based on the datafication of childhood as a socially situated and embodied experience. In so doing, it will generate a grounded understanding and novel theorizations of how families engage in a variety of data practices and surveillance practices in the digital-material contexts of their everyday lives.
To achieve these goals, DataChildFutures employs an interdisciplinary theoretical framework that integrates four fields of investigation: Children and Media studies (CAM); mediatization research; surveillance and critical data studies; the sociology of childhood; a longitudinal, mixed-methods research design. The latter combines: a survey to a national representative sample of parents of young children (0-to-8-years-old); qualitative longitudinal research (QLR) with young children and their families; an analysis of the affordances of apps, IoTs and IoToys used by children and parents through the walkthrough method.
Professor in charge:
- Giovanna Mascheroni, Associate Professor of Sociology of Media and Communication
Funding source: Fondazione Cariplo, Bando Ricerca Sociale 2019
Artificial Intelligence (AI) and the Rights of the Child (AIRoC) is a research activity of the Joint Research Centre of the European Commission. Framed under the Cybersecurity Education, Awareness and Societal aspects (CEAS) and the HUMAINT scientific projects, AIRoC has the aim to explore and contribute to the current knowledge regarding AI and the implications of its development and use in relation to children and their rights. This activity contributes to the wider activity of the European Commission towards the transformation of Europe into the global hub for trustworthy Artificial Intelligence (AI).
Professor in charge:
- Giovanna Mascheroni, Associate Professor of Sociology of Media and Communication
Funding source: Joint Research Centre, EC
Algorithms and Fake News
Artificial Intelligence (AI) and the Rights of the Child (AIRoC) is a research activity of the Joint Research Centre of the European Commission. Framed under the Cybersecurity Education, Awareness and Societal aspects (CEAS) and the HUMAINT scientific projects, AIRoC has the aim to explore and contribute to the current knowledge regarding AI and the implications of its development and use in relation to children and their rights. This activity contributes to the wider activity of the European Commission towards the transformation of Europe into the global hub for trustworthy Artificial Intelligence (AI).
Professor in charge:
- Giovanna Mascheroni, Associate Professor of Sociology of Media and Communication
Funding source: Joint Research Centre, EC
The project focuses on the high school classroom as a critical social milieu for the engagement with knowledge refused by the official scientific community (Refused Scientific Knowledge, or RSK) and intends to shed light on the role of algorithms and of broader socio-technical dynamics of circulation of online information in facilitating the acceptance of RSK and unreliable technological information among young audiences.
The project's main purpose is to develop educational tools and practices to help high school students master the flow of online information to discern between reliable and unreliable information.
The project adopts a mixed-method approach based on qualitative social research (focus groups, in depth interviews and ethnographic observation) to shed light on the social dynamics of circulation of RSK within the classroom; on quantitative data network analysis, to understand the pattern of circulation of RSK within the young audiences’ media ecologies; on action research, to be accomplished with teacher’s collaboration, to enhance the students’ skills in assessing the reliability of online techno-scientific information.
Professor in charge:
- Simone Tosoni, Associate Professor in Sociology of Cultural and Communicative Processes
Funding source: PRIN 2017
Technology and Health
The aim of the study is to develop an e-health system dedicated to patients with chronic HIV infection that allows to collect patient self-reported outcomes (PROs), analyse them and integrate them with health data through Artificial Intelligence methods. It will allow the creation a sort of “virtual clinic” for chronic patients who, in this pandemic phase, cannot freely access their outpatient care pathway, or who have difficulties in following a standard care pathway.
Using the Healthentia App, in fact, it will be possible to monitor the patient’s state of health, provide assistance and care, carry out early screening for COVID-19 and also interact in order to establish proximity and relief.
Researchers in charge: Dr. Cingolani, Dr. Murri, Dr. Tamburrini, Prof. Cauda, Dr. Patarnello, Dr. Lanzotti, Dr. Tagliaferri, Dr. Kostopoulou, Dr. Luraschi, Dr. Pnevmatikakis, Dr. Lamonica, Dr. Kyriazakos, Dr. Iacomini, Dr. Micheli, Dr. Seguiti, Dr. Kanavos, Dr. Cesario, Prof. Valentini, Prof. Cauda
Funding source: institutional fund
A service for the Fondazione Policlinico Gemelli’s collaborators which, through the Healthentia App, aims to offer support and guidance to all those who have to manage a situation related to the new Coronavirus SARS-CoV-2.
Researchers in charge: Dr. Cambieri, Prof. Laurenti, Dr. Murri, Dr. Masciocchi, Dr. Lenkowicz, Dr. Fantoni, Prof. Damiani, Dr. Marchetti, Dr. Sergi, Dr. Arcuri, Dr. Cesario, Dr. Patarnello, Prof. Antonelli, Prof. Bellantone, Prof. Bernabei, Prof. Boccia, Prof. Calabresi, Prof. Cauda, Prof. Colosimo, Prof. Crea, Prof. De Maria, Prof. De Stefano, Prof. Franceschi, Prof. Gasbarrini, Prof. Landolfi, Prof. Parolini, Prof. Richeldi, Prof. Sanguinetti, Prof. Urbani, Dr. Zega, Prof. Scambia, Prof. Valentini and the Gemelli against Covid Group
Funding source: institutional fund
The primary aim of the TOTAL Radiomics (disTributed cOx wiTh feAture seLection using Radiomics) consortium is to develop and validate a radiomics based outcome prediction model for lung cancer via distributed learning [1]. The model will be based on data from patients treated through multimodal oncological therapies across multiple international centres, without any individual level patient data leaving the institution it originates from, therefore preserving patient data privacy. For the TOTAL Radiomics project, we are specifically aiming to investigate predictors for overall survival using radiomic features extracted from radiotherapy (RT) planning CTs.
Researchers in charge: Dr. Boldrini, Dr. Masciocchi, Prof. Damiani, Dr. Gottardelli, Dr. Martino, Dr. Massaccesi, Dr. Mazzarella, Prof. Valentini
Funding source: institutional fund
To create a Datamart that consolidates, with a constant updating from clinical sources, data related to COVID-19 pathology (detected values, laboratory analysis, symptoms evolution, specialist reports). This DataMart is currently being used in numerous clinical researches aimed at analyzing patterns of correlation between symptoms and clinical evolution, efficacy of therapies and drugs used, evolution of the clinical picture in the presence of previous pathologies, and other useful factors to constantly improve the early diagnosis of the pathology and an accurate identification of treatment.
Researchers in charge: Dr. Murri, Dr. Masciocchi, Dr. Lenkowicz, Dr. Fantoni, Prof. Damiani, Dr. Marchetti, Dr. Sergi, Dr. Arcuri, Dr. Cesario, Dr. Patarnello, Prof. Antonelli,Prof. Bellantone, Prof. Bernabei,, Prof. Boccia, Prof. Calabresi, Dr. Cambieri,, Dr. Cauda, Prof. Colosimo, Prof. Crea, Prof. De Maria, Prof. De Stefano, Prof. Franceschi, Prof. Gasbarrini, Prof.Landolfi, Prof. Parolini, Prof. Richeldi, Prof. Sanguinetti, Prof. Urbani, Prof. Zega, Prof. Scambia,, Prof. Valentini and the Gemelli against Covid Group
Funding source: institutional fund
Aim of BACTERIUM.BOT project is to leverage Artificial Intelligence methods to implement a predictive model which, through the analysis of clinical, laboratory and microbiological variables, provides clinicians with an alerting system able to support them in the early assessment of the risk of bloodstream infections (BSI) and, consequently, in a more efficient planning and use of resources.
Researchers in charge: Prof. Murri, Dr. De Angelis, Prof. Antonelli, Prof. Posteraro, Prof. Sanguinetti, Dr. Fantoni, Dr. Masciocchi, Dr. Lenkowicz, Prof. Damiani, Dr. Cesario, Dr. Taccari, Dr. Iacomini, Dr. Rinaldi, Dr. Marchetti, Dr. Sergi, Dr. Patarnello, Prof. Antonelli, Dr. Cambieri, Prof. Cauda, Prof. Franceschi, Prof. Gasbarrini, Dr. Zega, Prof. Valentini
Funding source: institutional fund
To have a real time prediction for stroke patients, admitted from ER, on the discharge NIHSS improvement. The predictive model is to be built on longitudinal data: ranging from ER admission to clinical diariesto discharge status. The updated prediction result is to be incorporated into clinical dashboards.
Researchers in charge: Prof. Antonelli, Dr. Mercurio, Dr. Gottardelli, Dr. Lenkowicz, Dr. Patarnello, Dr. Bellavia, Dr. Scala, Dr. Rizzo, Dr. Del Signore; Prof. de Belvis, Dr. Angioletti, Dr. Maviglia, Dr. Bocci, Prof. Olivi, Prof. Franceschi, Prof. Urbani, Prof. Calabresi, Dr. Della Marca, Prof. Antonelli, Dr. Frisullo
Funding source: institutional fund
The aim of the study is to associate survival with the presence or absence of liver damage defined as hypertransaminasemia (ALT > 1.5 x UNL). The hypothesis underlying the present study is that the severity of COVID is determined by factors modulating the cellular response to oxidative stress and that the transition of the adaptive response to the development of damage can be recognised by a combination of markers reflecting the balance between pro- and anti-inflammatory responses to oxidative stress with the liver as the main organ involved.
Researchers in charge: Dr. Miele, Prof. Grieco, Dr. Marrone, Dr. Biolato, Dr. Liguori, Prof. Gasbarrini, Prof. Valentini, Prof. Rapaccini, Dr. Papa, Dr. Vetrone, Dr. Schepis, Dr. Masciocchi, Dr. Patarnello
Funding source: institutional fund
Among the projects based on DataMart COVID-19, this study foresees the development of a predictive model for the early assessment of critical conditions (access to intensive care, intubation and/or risk of death) for positive COVID-19 patients. The system also provides an analysis of clinical flows to optimise the use of critical resources such as intensive care equipment.
Researchers in charge: Prof. Murri, Dr. Fantoni, Dr. Masciocchi, Dr. Lenkowicz
Funding source: institutional fund
The project aims at developing a predictive model for ERCP-related acute pancreatitis, based on individual patients’ data at the time of the procedure. The study is single-center retrospective; inclusion criteria for the study cohort are: individuals > 18 years old who underwent an ERCP procedure at Gemelli Hospital in the timeframe January 2014 to December 2019. Candidate predictive variables are extracted from Data warehouse sources at the time of ERCP procedure, also exploiting text mining techniques in the case of unstructured data such as free text clinical reports, radiological reports and discharge letters.
Similarly, the study outcome information of ERCp-related acute pancreatitis will be extracted both from structured sources such as discharge IC9 codes, and from unstructured sources such as clinical reports after the ERCP procedure. The so extracted variables will be used to train machine learning models for predicting the risk of acute pancreatitis at the individual patient level. The final predictive model will be included in an interactive dashboard available to the involved clinicians via the hospital intranet for testing purposes.
Researchers in charge: Dr. Coratti, Prof. Mercuri, Dr. Lenkowicz
Funding source: institutional fund
Covid-X: Care4Covid Personalized C
The purpose of the project is to create an innovative solution able to provide an effective support in post-COVID management.
Healthentia CARE4COVID is digital therapeutic solution for long-Covid patients, in the form of a mobile app with virtual coaching, that helps them to recover from symptoms faster, while decreasing the probabilities for respiratory or neurological chronic conditions. At the same time, CARE4COVID allows hospitals to offer continuous coaching to patients to address long-Covid conditions and go from a standard day hospital support towards a hybrid model with reduced need for onsite care, by means of an intelligent Patient Remote Monitoring (PRM) and decisions support portal for doctors.
The solution is certified to capture Real World Data (RWD) and by applying Artificial Intelligence (AI) and Machine Learning (ML) algorithms on the captured data, it orchestrates digital content optimally to be delivered to the patient through an engaging smartphone app, aiming to support remote rehabilitation for long-Covid. CARE4COVID has a starting point clinical data from long-Covid patients, as well as predictive models related to the evolution of the long-Covid conditions.
Researchers in charge: Dr.Tagliaferri, Prof. Valentini, Dr. Patarnello, Dr. Luraschi
Funding source: Horizon Europe 2020, Grant Agreement 101016065
