A neural-network approach for predicting time to cardiovascular diseases in HIV patients based on real-world data
Agostino Lurani Cernuschi,
Chiara Masci (),
Federica Corso (),
Camilla Muccini (),
Daniele Ceccarelli (),
Laura Galli (),
Francesca Ieva (),
Antonella Castagna () and
Anna Maria Paganoni ()
Additional contact information
Agostino Lurani Cernuschi: Politecnico di Milano
Chiara Masci: Politecnico di Milano
Federica Corso: Politecnico di Milano
Camilla Muccini: Ospedale San Raffaele
Daniele Ceccarelli: Ospedale San Raffaele
Laura Galli: Ospedale San Raffaele
Francesca Ieva: Politecnico di Milano
Antonella Castagna: Ospedale San Raffaele
Anna Maria Paganoni: Politecnico di Milano
Operational Research, 2025, vol. 25, issue 4, No 10, 29 pages
Abstract:
Abstract At the end of 2021, 38.4 million people were living with HIV (PLWH) worldwide. The advent of anti retroviral therapy (ART) has significantly reduced the mortality and increased life expectancy of PLWH. Nowadays, the management of people with HIV on virological suppression is partly focused on the onset of comorbidities, such as the occurrence of cardiovascular diseases (CVDs). In this real-world study, we analyse the 15 years CVD risk in PLWH, following a survival analysis approach based on neural networks (NNs). We adopt a NN-based deep learning approach to flexibly model and predict the time to a CVD event, relaxing the linearity and the proportional-hazard assumptions typical of the COX model and including time-varying features. Results of this approach are compared to the ones obtained via more classical survival analysis methods, both in terms of predictive performance and interpretability. A further aim is to explore the potential of deep learning approaches in modelling survival data with time-varying features for supporting decision-making in real clinical setting.
Keywords: Cardiovascular disease; Survival analysis; Deep learning; DeepHit; HIV; Neural network; Time-dependent data (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s12351-025-00972-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:operea:v:25:y:2025:i:4:d:10.1007_s12351-025-00972-8
Ordering information: This journal article can be ordered from
https://www.springer ... search/journal/12351
DOI: 10.1007/s12351-025-00972-8
Access Statistics for this article
Operational Research is currently edited by Nikolaos F. Matsatsinis, John Psarras and Constantin Zopounidis
More articles in Operational Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().