Interview with Dr. Giuliana Favara—Winner of Informatics 2022 Best Ph.D. Thesis Award

We are pleased to announce the winner of the Informatics 2022 Best Ph.D. Thesis Award. This award is for a Ph.D. student or recently qualified researcher who has produced a highly anticipated thesis with impressive academic potential.

The award has been granted to “Application of Public Health Informatics to Monitor and Prevent Healthcare Associated Infections and Related Outcomes in Intensive Care Units”, by Dr. Giuliana Favara, University of Catania, Italy.

The winner will receive CHF 800, a certificate, and a chance to publish a paper free of charge after peer review in Informatics (ISSN: 2227-9709) in 2023.

We congratulate Dr. Giuliana Favara on her accomplishments. We would like to take this opportunity to thank all the applicants for submitting their exceptional theses and the Award Committee for voting and supporting this award.

Dr. Giuliana Favara is an assistant professor (RTDA) for the Scientific Disciplinary Sector MED/01 (Medical Statistics), at the University of Catania (Italy), Department of Medical and Surgical Sciences and Advanced Technologies “G.F Ingrassia”, Scientific Supervisor Antonella Agodi, Full Professor of Hygiene and Public Health and Director of the Department “G.F Ingrassia”, University of Catania. She received a bachelor’s degree in biological sciences and a master’s degree cum laude in health and molecular biology from the University of Catania. Next, she received a Ph.D. in Informatics at the Department of Mathematics and Informatics, University of Catania. She is interested in epidemiology and biostatistics applied to public health issues.

The following is an interview with Dr. Giuliana Favara:

1. Could you please give us a brief overview of your research topic and the main objectives of your Ph.D. thesis?
Healthcare-associated infections represent a global public health threat, suggesting the need to identify patients whose clinical and personal conditions could lead to a higher risk of HAIs and other adverse outcomes in Intensive Care Units. Thus, my Ph.D. thesis aimed to develop and test a machine learning algorithm to further improve the predictive performance of conventional statistical approaches and to predict the risk of adverse outcomes in patients admitted to Intensive Care Units. Thus, we described the application of supervised and unsupervised models to distinguish Intensive Care Unit patients according to their characteristics at admission, and to identify determinants of risks of urinary tract infections, pneumoniae and associated adverse outcomes.

2. What motivated you to pursue this research topic, and how did you come up with your research questions?
Patients in Intensive Care Units show worse clinical prognoses and prolonged hospital stays, suggesting the need to develop novel approaches tailored to each patient’s requirement to monitor and predict their disease severity and health deterioration. Although several early warning scores represent the most used conventional approaches for the prediction of prognosis, clinical deterioration and risk assessment, recent advances in health informatics could be crucial to identifying subgroups of patients at higher risk.
With this in mind, the research project aims to develop and test a machine learning algorithm to further improve the predictive performance of conventional statistical approaches, and to predict the risk of adverse outcomes in patients staying in Intensive Care Units.

3. How did you manage your time and prioritize your tasks during your Ph.D. program, and what strategies did you use to stay focused and motivated?
During my Ph.D. program, a preliminary literature search was conducted to deeply investigate interesting research questions and to identify the most suitable models for public health issues. All the research activities were coordinated by my scientific supervisor, Professor Antonella Agodi, who encouraged and motivated me to achieve all the objectives. All the members of my research team collaborated and were crucial for my Ph.D. program.

4. What were some of the biggest challenges you faced during your Ph.D. journey, and how did you overcome them?
One of the biggest challenges I faced was to apply an integrated and multidisciplinary approach of epidemiology and informatics to public health issues concerning my Ph.D. program. Another issue encountered when analyzing data was related to the large amounts of missing data in our dataset. Since machine learning models require a lot of variables and records, ad hoc approaches were applied to create a dataset of synthetic records that was used as the training set.

5. When and how did you access Informatics? What prompted you to apply for this award, and would you like to share your experience with the journal Informatics?
I had access to the journal Informatics by reading research articles which could provide useful insights into the proposed research project. I decided to apply for this award to share my experience and disseminate the results obtained in the framework of my Ph.D. thesis, aiming to encourage clinicians and public health professionals to develop and validate novel strategies for improving patient care and health globally.

6. Finally, how do you plan to continue building on your research in the future, and what are your long-term career aspirations?
In the future, I will explore how data analysis can be applied to public health, as well as informatics and innovative tools, to shed light on global challenges and questions in the context of personalized medicine and prevention.