Luca Marzano

Biography:

ChatGPT says about me that "Luca Marzano is a PhD candidate at KTH Royal Institute of Technology, enrolled in the medicine and technology program. His research project focuses on developing data-driven approaches to achieve real-world evidence in various healthcare fields, including oncology (with a focus on small cell lung cancer), clinical trials design, emergency medicine, and complex adaptive systems. With a background in applied physics, Luca has previously worked on developing deep learning and complex graph theory applications in neuroimaging. Currently, he is a member of the Centre for Data-driven Healthcare (KTH-CDDH, https://www.kth.se/sv/cddh). As a member of KTH-CDDH, Luca is contributing to various projects aimed at creating an infrastructure solution to some major unsolved issues for health care, research, citizens, and society”

My publications:

Luca Marzano, Adam S. Darwich, Raghothama Jayanth et al. How to diagnose an overcrowded emergency department from its EHRs? Enhancing opportunities and challenges of real-world data from a whole-system perspective, 23 November 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-3620599/v1]

Marzano, L., Dan, A., Tendler, S., Darwich, A. S., Raghothama, J., De Petris, L., ... & Meijer, S. (2023). EP13. 03-14 A Comparative Analysis between Real-World Data and Clinical Trials to Evaluate Differences in Outcomes for SCLC Patients.Journal of Thoracic Oncology,18(11), S697.

Marzano, L., Darwich, A. S., Dan, A., Tendler, S., Raghotama, J., Lewensohn, R., ... & Meijer, S. (2023). Explainable machine learning to inform real-world evidence: A case study on small cell lung cancer survival analysis.

Marzano, L., Meijer, S., Dan, A., Tendler, S., De Petris, L., Lewensohn, R., ... & Darwich, A. S. (2023). Application of Process Mining for Modelling Small Cell Lung Cancer Prognosis. InCaring is Sharing–Exploiting the Value in Data for Health and Innovation (pp. 18-22). IOS Press. DOI: 10.3233/SHTI230056

Marzano, L., Darwich, A. S., Tendler, S., Dan, A., Lewensohn, R., De Petris, L., ... & Meijer, S. (2022). A novel analytical framework for risk stratification of real‐world data using machine learning: A small cell lung cancer study.Clinical and Translational Science,15(10), 2437-2447 https://doi.org/10.1111/cts.13371

Abourraja, M. N., Marzano, L., Raghothama, J., Asl, A. B., Darwich, A. S., Meijer, S., ... & Falk, N. (2022, December). A Data-Driven Discrete Event Simulation Model to Improve Emergency Department Logistics. In2022 Winter Simulation Conference (WSC) (pp. 748-759). IEEE. 10.1109/WSC57314.2022.10015465

Bellantuono, L., Marzano, L., La Rocca, M., Duncan, D., Lombardi, A., Maggipinto, T., ... & Bellotti, R. (2021). Predicting brain age with complex networks: From adolescence to adulthood.NeuroImage,225, 117458. https://doi.org/10.1016/j.neuroimage.2020.117458

Thesis works I supervised:

Osswald, J. (2024). Development of a System Dynamic Model of Mental Healthcare Structure in Stockholm (Dissertation). Retrieved from https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-343406

Alkhatib, N. (2024). A Simulation Game Approach for Improving Access to Specialized Healthcare Services in Sweden (Dissertation). Retrieved from https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-343004

Haraldsson, T. (2023). Development of a Machine Learning Survival Analysis Pipeline with Explainable AI for Analyzing the Complexity of ED Crowding : Using Real World Data collected from a Swedish Emergency Department (Dissertation). Retrieved from https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-329329

Dzubur, S. (2023). Modeling of Healthcare Delivery in Sweden (Dissertation). Retrieved from https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-329603

Rosamilia, U. (2022). Applying Nonlinear Mixed-Effects Modeling to Model Patient Flow in the Emergency Department : Evaluation of the Impact of Patient Characteristics on Emergency Department Logistics (Dissertation). Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-314814

Jenner, S. (2022). Offline Reinforcement Learning for Optimization of Therapy Towards a Clinical Endpoint (Dissertation). Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-325876

Daoud, T., & Zere Goitom, E. (2022). Classification and localization of extreme outliers in computer vision tasks in surveillance scenarios (Dissertation). Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-313585

Hallberg, C.-B., & Sjölinder, G. (2021). Reducing volatility for a linear and stable growth in a cryptocurrency : Encourage spending, while providing a stable store of value over time in a decentralized network (Dissertation). Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296645