In Computer methods and programs in biomedicine
Personalized support and assistance are essential for cancer survivors, given the physical and psychological consequences they have to suffer after all the treatments and conditions associated with this illness. Digital assistive technologies have proved to be effective in enhancing the quality of life of cancer survivors, for instance, through physical exercise monitoring and recommendation or emotional support and prediction. To maximize the efficacy of these techniques, it is challenging to develop accurate models of patient trajectories, which are typically fed with information acquired from retrospective datasets. This paper presents a Machine Learning-based survival model embedded in a clinical decision system architecture for predicting cancer survivors' trajectories. The proposed architecture of the system, named PERSIST, integrates the enrichment and pre-processing of clinical datasets coming from different sources and the development of clinical decision support modules. Moreover, the model includes detecting high-risk markers, which have been evaluated in terms of performance using both a third-party dataset of breast cancer patients and a retrospective dataset collected in the context of the PERSIST clinical study.
Manzo Gaetano, Pannatier Yvan, Duflot Patrick, Kolh Philippe, Chavez Marcela, Bleret Valérie, Calvaresi Davide, Jimenez-Del-Toro Oscar, Schumacher Michael, Calbimonte Jean-Paul
2023-Jan-25
Decision-system, Machine learning, Modular architecture, Survival analysis
