University Research
University Research
Digital Innovation & Patient Engagement
The project is led by the research team of EngageMinds HUB which – thanks to the unconditional support of the MSD Foundation and Datawizard (technological partner) has worked on the development of an app dedicated to people with HIV to improve the empathic relationship with the infectious disease specialist, allowing the patient to give voice to his emotions, relational and communicative concerns and expectations related to the meeting with the attending physician. The patient will also be able to receive advice – also shared with the doctor – in order to make communication between the two actors increasingly effective.
Lecturers:
- Guendalina Graffigna, Full Professor of Consumer and Health Psychology
- Serena Barello, Researcher in Consumer and Health Psychology
Funding Line: MSD Foundation
The project, conducted by the Università Cattolica Research Center EngageMinds HUB in collaboration with DataWizard and with the unconditional contribution of Amazon, aims to conduct an experiment to evaluate the impact of the use of voice assistants by the elderly in their home environment on their well-being and quality of life.
Lecturers:
- Guendalina Graffigna, Full Professor of Consumer and Health Psychology
- Giuseppe Riva, Full Professor of General Psychology
- Serena Barello, Researcher in Consumer and Health Psychology
- Silvia Serino, Researcher in General Psychology
- Clelia Malighetti, PhD student in Psychology
Funding Line: Amazon Media EU SRL
The goal of Gravitate-Health is to develop a digital health information tool called Gravitate Lens (G-Lens). As the name suggests, the G-Lens will focus approved information on medicines and guide patients to understandable, reliable and up-to-date information that meets the patient's needs and adapts to their healthcare context and literacy and empowerment/engagement levels. The functionality of the G-Lens will be supported by an open source digital platform.
Lecturers:
- Guendalina Graffigna, Full Professor of Consumer and Health Psychology
- Lorenzo Palamenghi, Research Fellow at the Faculty of Psychology
- Serena Barello, Researcher in Consumer and Health Psychology
Funding line: European Commission - IMI
The prevalence of atrial fibrillation (AF) among the European elderly population is increasing. In the context of multimorbidity, improving AF management is vital and requires a holistic approach. The new approach behind the AFFIRMO project is to focus on multimorbidity groups where atrial fibrillation is one of the chronic conditions.
Improving the management of atrial fibrillation in the context of multimorbidity can benefit individuals on a larger scale, thanks to a holistic approach of optimizing the clinical management of patients with older atrial fibrillation that takes into account the multifaceted aspects of people's 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-centered. AFFIRMO's response to this challenge is therefore to develop a holistic and multidisciplinary care approach based on the "Atrial Fibrillation Better Care" (ABC) model.
Lecturers:
- Guendalina Graffigna, Full Professor of Consumer and Health Psychology
- Serena Barello, Researcher in Consumer and Health Psychology
Funding line: European Commission - Horizon
Beyond Algorithms: Towards a New Humanism
The rapid transformations resulting from technological development have led to a decisive change in the traditional ways of representing the world. However, because algorithms and data mining techniques that perform public functions are operated by private companies, they have inherent vulnerabilities.
The aim of the research is to explore the space between public functions and private control, to understand whether current multi-stakeholder governance strategies have adequate guarantees. In particular, the aim is to investigate how the algorithmic decision adopted in the exercise of public functions, affecting the main categories of space and time, modifies decision-making processes in numerous sectors (legal, economic, social, medical, etc.) and with what consequences.
Lecturer of reference:
- Gabriele Della Morte, Full Professor of International Law
Funding line: D.3.2. (Projects of interest to the University)
Artificial Intelligence, teenagers and children
ySKILLS proposes a holistic, child-centred approach to understand how internet use can have variable consequences for children's rights to participation, information, freedom of expression, education and play, and protection from threats. ySKILLS examines the risks and opportunities related to the use of ICT technologies by children and adolescents (aged 12 to 17) and their digital skills, in order to identify an intentional use that contributes to the achievement of greater cognitive, physical, psychological and social well-being.
The project offers a new measurement of digital skills in surveys and performance tests, which are based on a four-dimensional classification of digital skills: technical, information browsing, social and content creation. Through a three-wave longitudinal 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 most at risk of having low levels of well-being due to their use of ICT and how digital skills can foster resilience against negative impacts. This results in a comprehensive, evidence-based explanatory and forecasting model that predicts the complex impacts of ICT use on the well-being of children and adolescents in Europe, and the role of digital skills that can improve their well-being.
Lecturer of reference:
- Giovanna Mascheroni, Associate Professor in Sociology of Cultural and Communicative Processes
Funding Line: H2020-SC6-TRANSFORMATIONS-2018-2019-2020 RIA, Grant Agreement no. 870612
DataChildFutures will generate a robust evidence base on the dataization of childhood as a socially localized, everyday experience. In doing so, it will facilitate a grounded understanding and new theorizations about how households engage in a variety of data and surveillance practices in the digital-material contexts of their daily lives.
To achieve these goals, DataChildFutures employs an interdisciplinary theoretical framework that integrates four fields of inquiry: Children and Media studies (CAM); mediatisation research; surveillance and critical data studies; sociology of childhood; a longitudinal research design, with mixed methods. The latter combines: a survey of a nationally representative sample of parents of young children (0 to 8 years old); longitudinal qualitative research (QLR) with young children and their families; an analysis of the affordances of apps, IoT and IoToys used by children and parents through the walkthrough method.
Lecturer of reference:
- Giovanna Mascheroni, Associate Professor in Sociology of Cultural and Communicative Processes
Funding Line: Fondazione Cariplo, Call for Social Research 2019
Artificial Intelligence (AI) and Children's Rights (AIRoC) is a research activity of the European Commission's Joint Research Centre. Framed within the scientific projects Cybersecurity Education, Awareness and Societal aspects (CEAS) and HUMAINT, AIRoC aims to explore and contribute to current knowledge regarding AI and the implications of its development and use in relation to children and their rights. This activity contributes to the European Commission's broader commitment to transforming Europe into the global hub for trustworthy Artificial Intelligence (AI).
Lecturer of reference:
- Giovanna Mascheroni, Associate Professor in Sociology of Cultural and Communicative Processes
Funding line: Joint Research Centre of the European Commission
Algorithms and Fake News
This project aims to shed light on the processes and factors that foster the production, circulation and acceptance of Refused Scientific Knowledge (RSK). This implies, on the one hand, the understanding of the production practices, legitimation and circulation of the RSK by specific social actors; the identification of the communication frames and formats through which the RSK is circulated; the analysis of the role of media, social media and algorithms in the diffusion of RSK. On the other hand, this also entails the need to focus on the social dynamics that lead to the acceptance of RSK by non-experts.
To address these goals, a theoretically informed mixed-methods approach will be implemented to investigate four cases of engagement with FSK: the No-5G and No-Vax communities, the five biological laws, and the cases of water alkalinization. The project is intended to have a scientific impact, enriching the understanding of RSK circulation, and a social impact, supporting actions to address the phenomenon of non-expert engagement in knowledge questioned by scientific communities.
Lecturer of reference:
- Simone Tosoni, Associate Professor in Sociology of Cultural and Communicative Processes
Financing Line: PRIN 2017
The project focuses on high school classrooms as a social means for engagement with knowledge rejected by the mainstream scientific community (RSK). In particular, it aims to shed light on the role of algorithms, and the broader socio-technical dynamics of online information circulation, in facilitating the acceptance of RSK and unreliable technological information among young audiences.
The main aim of the project is to develop educational tools and practices to help high school students master the flow of information online, in order to discern between reliable and unreliable information. In this regard, the project adopts a mixed approach based on qualitative social research (focus groups, in-depth interviews and ethnographic observation) to shed light on the social dynamics of RSK circulation within classrooms; on the quantitative analysis of the data network to understand the circulation pattern of RSK within the media ecologies of the young audience; on action research, to be carried out with the collaboration of teachers, to improve students' skills in assessing the reliability of online technical-scientific information.
Lecturer of reference:
- Simone Tosoni, Associate Professor in Sociology of Cultural and Communicative Processes
Funding line: PRIN 2017
Technology & Health
The aim of the study is to develop an e-health system dedicated to patients with chronic HIV infection that allows to collect self-reported patient results (PROs), analyze them and integrate them with health data through Artificial Intelligence methods.
It will make it possible to create a sort of "virtual clinic" for chronic patients who, in this pandemic phase, cannot freely access their outpatient care pathway, or who have difficulty following a standard care pathway. Through 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 to establish closeness and rescue.
Reference researchers: 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
Financing line: institutional funds
A service for the collaborators of the Fondazione Policlinico Gemelli 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.
Reference researchers: 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 Group against Covid
Financing line: institutional funds
The primary objective of the TOTAL Radiomics (disDistributed cOx wiTh feAture seLection using Radiomics) consortium is to develop and validate a radiomics-based outcome prediction model for lung cancer through distributed learning [1]. The model will be based on data from patients treated with multimodal cancer therapies in multiple international centers, without individual patient data leaving the institution from which it originates, thus preserving the privacy of patient data. For the TOTAL Radiomics project, we specifically aim to study predictors for overall survival using radiomic features extracted from radiotherapy planning (RT) CTs.
Reference researchers: Dr. Boldrini, Dr. Masciocchi, Prof. Damiani, Dr. Gottardelli, Dr. Martino, Dr. Massaccesi, Dr. Mazzarella, Prof. Valentini
Financing line: institutional funds
Create a Datamart that consolidates, with constant updating from clinical sources, the data relating to the COVID-19 pathology (values detected, laboratory analysis, evolution of symptoms, specialist reports). This Datamart is currently used in numerous clinical researches aimed at analyzing the correlation patterns between symptoms and clinical evolution, the efficacy of therapies and drugs used, the evolution of the clinical picture in the presence of previous pathologies, and other factors useful to constantly improve the early diagnosis of the disease and an accurate identification of the treatment.
Reference researchers : 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, 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, Prof. Zega, Prof. Scambia,, Prof. Valentini and the Gemelli Group against Covid
Financing line: institutional funds
The aim of the BACTERIUM. BOT is to exploit Artificial Intelligence methods to implement a predictive model that, through the analysis of clinical, laboratory and microbiological variables, provides doctors with an alert system capable of supporting them in the early assessment of the risk of blood infections (BSI) and, consequently, in a more efficient planning and use of resources.
Reference researchers: 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
Financing line: institutional funds
To have a real-time forecast for stroke patients, admitted by the emergency department, on NIHSS improvement at discharge. The predictive model must be built on longitudinal data: from admission to the emergency department to clinical diaries to discharge status. The up-to-date prediction result should be incorporated into the clinical pictures.
Reference researchers: 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 A, Prof. Calabresi, Dr. Della Marca, Prof. Antonelli, Dr. Frisullo
Financing line: institutional funds
The aim of the study is to associate survival with the presence or absence of liver injury defined as hypertransaminasemia (ALT > 1.5 x UNL). The hypothesis underlying the present study is that the severity of COVID is determined by factors that modulate the cellular response to oxidative stress and that the transition of the adaptive response to the development of damage can be recognized 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.
Reference researchers: 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
Financing line: institutional funds
Among the projects based on DataMart COVID-19, this study involves the development of a predictive model for the early assessment of critical conditions (access to intensive care, intubation and/or risk of death) for COVID-19 positive patients. The system also provides clinical flow analysis to optimize the use of critical resources such as critical care equipment.
Reference researchers: Prof. Murri, Dr. Fantoni, Dr. Masciocchi, Dr. Lenkowicz
Financing line: institutional funds
The project aims to develop a predictive model for ERCP-related acute pancreatitis, based on individual patient data at the time of the procedure. The study is retrospective, single-center; the inclusion criteria of the study cohort are: individuals over 18 years of age who have undergone an ERCP procedure at Policlinico Gemelli in the period January 2014 - December 2019. Candidate predictive variables are extracted from Datawarehouse sources at the time of the ERCP procedure, also exploiting text mining techniques in the case of unstructured data, such as free-text clinical reports, radiology reports and discharge letters. Similarly, study outcome information of ERCP-related acute pancreatitis will be extracted from both structured sources such as IC9 discharge codes, and unstructured sources such as clinical reports after the ERCP procedure. The extracted variables will be used to train machine learning models to predict the risk of acute pancreatitis at the individual patient level. The final predictive model will be included in an interactive dashboard available to the clinicians involved via the hospital's intranet for testing purposes.
Reference researchers: Dr. Coratti, Prof. Mercuri, Dr. Lenkowicz
Financing line: institutional funds
The aim of the project is to create an innovative solution that can provide effective support in post-COVID management. Healthentia CARE4COVID is a digital therapeutic solution for Long Covid patients, in the form of a mobile application with virtual coaching, which helps them recover faster from symptoms, decreasing the chances of chronic respiratory or neurological conditions.
At the same time, CARE4COVID allows hospitals to offer continuous coaching to patients to deal with Long Covid conditions and to move from standard day hospital support to a hybrid model with a reduced need for on-site care. This is done by means of an intelligent remote patient monitoring (PRM) and decision support portal for clinicians.
The solution is certified to collect real-world data (RWD). By applying Artificial Intelligence (AI) and machine learning (ML) algorithms to the acquired data, it orchestrates digital content in such a way that it is delivered to the patient through an engaging smartphone app, with the aim of supporting remote rehabilitation for Long Covid. CARE4COVID has as its starting point the clinical data of patients with long-covid, as well as predictive models related to the evolution of long-Covid conditions.
Reference researchers: Dr. Tagliaferri, Prof. Valentini, Dr. Patarnello, Dr. Luraschi
Funding line: Horizon Europe 2020, Grant Agreement 101016065
