[en] 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.
Disciplines :
Oncology Computer science
Author, co-author :
Manzo, Gaetano ; University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland, National Institutes of Health (NIH), Bethesda, MD, USA. Electronic address: gaetano.manzo@nih.gov
Pannatier, Yvan; University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland
Duflot, Patrick ; Centre Hospitalier Universitaire de Liège - CHU > > Secteur Appui méthodologique aux Projets GSI et Planification (APP)
Kolh, Philippe ; Centre Hospitalier Universitaire de Liège - CHU > > Service des informations médico économiques (SIME)
Chavez, Marcela; CHU of Liege, Department of Information System Management, Belgium
Bleret, Valerie ; Centre Hospitalier Universitaire de Liège - CHU > > Service de sénologie
Calvaresi, Davide; University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland
Jimenez-Del-Toro, Oscar; University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland
Schumacher, Michael; University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland
Calbimonte, Jean-Paul; University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland, The Sense Innovation and Research Center, Lausanne and Sion, Switzerland
Language :
English
Title :
Breast cancer survival analysis agents for clinical decision support.
H2020 - 875406 - PERSIST - Patients-centered SurvivorShIp care plan after Cancer treatments based on Big Data and Artificial Intelligence technologies
Funders :
CE - Commission Européenne [BE] University of Applied Sciences and Arts Western Switzerland [CH] NIH - National Institutes of Health [US-MD] [US-MD]
Funding text :
This work was partially supported by the H2020 Project PERSIST under grant agreement No. 875406. The authors gratefully acknowledge the helpful remarks from Dina Demner-Fushman (National Institute of Health).
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