The ATHENA project, supported by MEDVIA and VLAIO, explores the concept of machine learning for the realization of predictive analytics in oncology. By creating a federated and standardized analytics platform, different types of data can be combined in one predictive model.
ATHENA (Augmenting Therapeutic Effectiveness through Novel Analytics) is hosting a symposium on 23 November to communicate the significant strides it is making in the field of oncology by harnessing groundbreaking machine-learning techniques for predictive analytics that respect privacy.
23 November, 9.30-18.15
Imec, Kapeldreef 75, Leuven
Program & registration
Treatment of cancer is currently insufficient, inappropriate and comes with debilitating side-effects for many patients. So what is hampering research in finding novel and adequate therapies for everyone who needs it?
A typical methodology to obtain novel insights into diagnosis, progression or treatment of disease is to set up defined clinical studies. However, the small number of participants – and thus patient data – limits the variability in the test population and hence the relevance and applicability of the results to a broad audience (Sherman et al, 2016).
A promising novel strategy is to include virtually all data from research involving patients suffering from a certain condition. This so-called real world data (data obtained from patients in a standard care setting) allows for the discovery of different patterns of disease progression and drug response through machine learning (Shah et al, 2019).
Early identification of these patterns in patients will lead to an optimal diagnosis and treatment strategy, adapted to the patient’s needs and aiming at maximization of survival rates.
Data driven Innovation in Personalized Medicine and Care is a free symposium, but space is limited, so register as soon as possible.
