Big Data Models and Intelligent tools for Quality of Life monitorinBig Data Models and Intelligent tools for Quality of Life monitoring and participatory empowerment of head and neck cancer survivors (BD4QoL)

Head and neck cancer can take away a patient’s “right to feel human,” and its impact on physical appearance, physical functioning, psychological status and general quality of life (QoL) can be devastating. Over the past several decades, the number of patients who survive head and neck cancer (HNC) has increased; this makes lifelong surveillance critical. HNC imposes an extremely high socioeconomic burden on patients during and after cancer compared to other tumors, including costs from treatment-induced morbidities, loss of workforce participation and short-term disability. Current survivorship care plans mostly focus on functional and health conditions of treated patients, whereas socioeconomic determinants of quality of life are often neglected due to difficult data collection. The widespread technologies for social communication and unobtrusive personal monitoring embedded in smartphones and object we commonly use and in our living environments have the potential to unobtrusively collect wealth of indicators of individual QoL. BD4QoL objective is to improve HNC survivor’s Quality of Life through person-centred monitoring and follow-up plan by contribution of artificial intelligence and big data unobtrusively collected from commonly used mobile devices, in combination with multi-source clinical, -omic, socioeconomic data and patients reported outcomes, to profile HNC survivors for pBD4QoL objective is to improve HNC survivor’s Quality of Life through person-centred monitoring and follow-up plan by contribution of artificial intelligence and big data unobtrusively collected from commonly used mobile devices, in combination with multi-source clinical, -omic, socioeconomic data and patients reported outcomes, to profile HNC survivors for personalized monitoring and support. The analysis of QoL indicators collected over time will allow to early detect risks, prevent long-term effects of treatment and inform patients and caregivers for personalized interventions.