Author: Erica Tavazzi

On July 10-12, 2024, our group had the pleasure of hosting Dr. Tirdad Seifi Ala, our esteemed partner from the Danish Medicines Agency (Lægemiddelstyrelsen, DMA), for an in-depth technical meeting within the REDDIE project. This visit was a significant milestone in our ongoing collaboration and provided an excellent opportunity to delve into the details of the current activities in Work Package 5 (WP5), titled “Advanced Methodological Frameworks for Enhancing Clinical Trials with Real-World and Virtual Evidence.”

The WP5 is led by our research group under the supervision of Principal Investigator Dr. Martina Vettoretti, with active involvement from both UNIPD and DMA. During the meeting, we had the opportunity to discuss some of the latest methodologies developed by our team, focusing on predictive models for the progression of chronic diseases and the conduction of retrospective observational studies.

The discussions were highly productive and insightful. Dr. Seifi Ala provided valuable feedback and shared his expertise, which helped refine our approaches and identify new avenues for future research. The collaborative atmosphere facilitated a deeper understanding of the challenges and opportunities in leveraging real-world evidence to enhance clinical trials.

As our partnership with DMA continues to strengthen, we are excited about the potential to contribute significantly to the use of real-world evidence for complementing randomized controlled trials. Our joint efforts aim to improve the efficacy, safety, and cost-effectiveness of technologies designed to prevent and treat diabetes, ultimately benefiting patients and healthcare systems alike.

We look forward to continuing this fruitful collaboration and are eager to see the impact of our combined contributions to the REDDIE project.

On July 3-4, 2024, our group hosted a productive technical meeting between UNIPD and NOH in the context of the REDDIE project. This significant event was focused on exploring collaborative opportunities and discussing the utilisation of data from Danmarks Statistik.
The meeting brought together key researchers from both institutions, who engaged in in-depth discussions on the methodologies for analysing diabetes outcomes using real-world data. The exchange of ideas and expertise was stimulating, as participants identified numerous points of contact and potential synergies.
Throughout the two days, the researchers delved into various aspects of diabetes research, emphasising the importance of leveraging real-world data to enhance the understanding of diabetes outcomes. The discussions underscored the potential benefits of integrating diverse datasets and employing advanced analytical techniques to improve research quality and impact. The event concluded with a shared vision of developing innovative solutions and methodologies that will ultimately benefit patients and healthcare providers.
This meeting marks a promising step forward in the collaboration between our group and NOH. The partners expressed enthusiasm about the future possibilities and are committed to continuing their joint efforts to advance diabetes research. 

Yesterday, June 4, 2024, a meeting took place between the Department of Information Engineering and the Department of Women’s and Children’s Health at the University of Padova. The objective of this meeting was to present our respective research areas and explore potential synergies and points of collaboration.
Our researchers, Martina Vettoretti and Giacomo Baruzzo, showcased the research directions of our group, highlighting ongoing projects and key topics. Their presentations were well-received and sparked a lively discussion, with numerous insightful questions from attendees.
This productive meeting demonstrated a strong interest in potential collaborations between the two departments. We are optimistic that this initial exchange will lead to fruitful cooperative opportunities in the near future.

Yesterday we had the pleasure to contribute with 3 presentations at the research event “DEI-AI 2024” hosted by our Department of Information Engineering, where we had the opportunity to showcase our latest groundbreaking work.

In our first presentation, titled “Exploring AI Applications in Medicine: Methodological Challenges and Examples,” Barbara Di Camillo, head of Sysbiobig, delved into the intricate world of AI applications in medicine, addressing methodological challenges head-on. From robust feature selection in omics data to dynamic Bayesian networks for simulating disease progression, we showcased cutting-edge methodologies and ongoing projects, including the BRAINTEASER project.

Following that, our second talk, “Artificial Intelligence Methods to Power Real-World Clinical Studies,” was delivered by our researcher Enrico Longato. This presentation underscored the importance of real-world effectiveness in evaluating treatments. We delved into the nuances of retrospective observational studies and discussed innovative AI approaches to combine weighting and matching techniques for robust analysis, a pivotal goal we are actively pursuing within the REDDIE project.

But that’s not all! Our researcher Martina Vettoretti also presented her recently funded project titled “BREATHE – Big data, internet-of-things and aRtificial intelligence to study the impact of personal Exposure to air pollution on AsTHma Exacerbations.” This project, funded under the PRIN 2022 call, aims to revolutionise our understanding of asthma control by studying the impact of personal exposure to air pollution. By leveraging an innovative infrastructure and employing advanced machine learning techniques, Martina and her team are paving the way for the development of prognostic models capable of predicting asthma exacerbations.

These presentations truly underscore our unwavering commitment to advancing AI research in healthcare and driving real-world impact. A heartfelt thank you goes to the event organisers for providing such a platform!

The 7th BRAINTEASER plenary meeting, held at the distinguished Universidade de Lisboa, has just concluded. The event garnered significant participation from various partner institutions, fostering lively discussions and idea exchanges. Attendees, including researchers, clinicians, and communication representatives contributed diverse perspectives, enriching the discourse. The meeting was complemented by an informal social dinner, enhancing networking opportunities.

Our team presented the upgrades and outcomes attained over the past months. We discussed the insights gleaned from examining the effectiveness of integrating pollutant data with clinical variables in Multiple Sclerosis (MS) progression predictive models. Additionally, we detailed the improvements made to both the Amyotrophic Lateral Sclerosis (ALS) and MS progression models. We explored the potential advantages of incorporating embedded stratification into our predictive models, alongside our strategy to ensure the interpretability of artificial intelligence (AI) model results. Lastly, we outlined our plan to exploit Explainable Artificial Intelligence (XAI) to identify and characterise patients for whom providing reliable predictions is challenging.

The collaborative atmosphere facilitated face-to-face meetings and stimulated engaging discussions. As the meeting drew to a close, coordinators and partners expressed optimism about BRAINTEASER’s future role in fostering collaboration and innovation within the global research community. Following two days of intensive exchanges, the consortium is poised to navigate the upcoming months with confidence.

In the context of the H2020 BRAINTEASER project, in collaboration with the University of Pavia and the other consortium partners, we are investigating the possible impact of air pollution on the progression of Multiple Sclerosis (MS).

Starting from the literature, where some potential correlations between pollutant agents and MS emerged, we are analysing the retrospective data provided within the project and made available to the whole research community here.
We explored the use of different machine learning techniques, including both linear and non-linear approaches, combined with the use of manual or automatic techniques for identifying the most robust features for prediction.

From our preliminary analyses on MS, the combination of dynamic environmental features with essential clinical variables appeared to be effective for enhancing the accuracy of predictive models when forecasting the occurrence of a relapse, i.e., an exacerbation of the symptoms. Noticeably, the role of environmental features was confirmed via all used feature selection approaches.