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A neural-network approach for predicting time to cardiovascular diseases in HIV patients based on real-world data

Author

Listed:
  • Agostino Lurani Cernuschi

    (Politecnico di Milano)

  • Chiara Masci

    (Politecnico di Milano)

  • Federica Corso

    (Politecnico di Milano
    Human Technopole)

  • Camilla Muccini

    (Ospedale San Raffaele)

  • Daniele Ceccarelli

    (Ospedale San Raffaele)

  • Laura Galli

    (Ospedale San Raffaele)

  • Francesca Ieva

    (Politecnico di Milano
    Human Technopole)

  • Antonella Castagna

    (Ospedale San Raffaele)

  • Anna Maria Paganoni

    (Politecnico di Milano)

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.

Suggested Citation

  • Agostino Lurani Cernuschi & Chiara Masci & Federica Corso & Camilla Muccini & Daniele Ceccarelli & Laura Galli & Francesca Ieva & Antonella Castagna & Anna Maria Paganoni, 2025. "A neural-network approach for predicting time to cardiovascular diseases in HIV patients based on real-world data," Operational Research, Springer, vol. 25(4), pages 1-29, December.
    • Handle: RePEc:spr:operea:v:25:y:2025:i:4:d:10.1007_s12351-025-00972-8
    DOI: 10.1007/s12351-025-00972-8
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