P08. 01 Building Personalized Follow-Up Care Through AI by Bringing the Lung Cancer Patient, Data Scientist and Oncologist Together

• Authors: TORRENTE M.; FRANCO F.; CALVO V.; LORDUY A. Collazo; MENASALVAS E.; VIDAL M. E.; SOUSA P.; PIMENTAO J.; NOVÁČEK Vít; MINERVINI P.; FEY D.; COSTABELLO L.; POCS M.; PROVENCIO M.
• Year of publication: 2021
• Type: Article in Periodical
• Magazine / Source: Journal of Thoracic Oncology
• MU Faculty or unit: Faculty of Informatics
• Web: https://www.jto.org/article/S1556-0864(21)02717-9/fulltext
• Doi: http://dx.doi.org/10.1016/j.jtho.2021.08.294
• Keywords: machine learning; lung cancer; relapse; relapse prediction
• Description: Survival rates of lung cancer patients were rather poor until recent decades, when screening protocols, diagnostic techniques improvement and novel therapeutic options were developed. This leads to a new challenge: to increase lung cancer patients’ post-treatment quality of life (QoL) and well-being. We here report on a first integration of an NLP framework for the analysis and integration of comprehensive eElectronic Health Records, genomic data, open data sources, wearable devices and QoL questionnaires, in order to determine the factors that predict poor health status and design personalized interventions that will improve the patient's QoL.