Scherrer, AlexanderAlexanderScherrerZimmermann, TobiasTobiasZimmermannRiedel, SinanSinanRiedelVenios, StefanosStefanosVeniosKoussouris, SotiriosSotiriosKoussourisPlakia, MariaMariaPlakiaDiamantopoulos, SotirisSotirisDiamantopoulosAthanassopoulos, SotirisSotirisAthanassopoulosLaras, ParisParisLarasMousa, FihmiFihmiMousaZifrid, RobertRobertZifridTillil, HartmutHartmutTillilMusisi, Isa WasswaIsa WasswaMusisiKosmidis, ThanosThanosKosmidisReis, Joaquim C.Joaquim C.ReisMoehler, Markus H.Markus H.MoehlerOestreicher, GabrielleGabrielleOestreicherKalamaras, IliasIliasKalamarasPantelidou, KonstantinaKonstantinaPantelidouVotis, Konstantinos V.Konstantinos V.VotisVassiliou, CharalamposCharalamposVassiliou2023-09-262023-09-262023https://publica.fraunhofer.de/handle/publica/45096110.1007/s00521-023-08765-w2-s2.0-85163942302This publication presents a solution approach to oncological aftercare for cancer patients by means of artificial intelligence (AI) methods. This approach shall support patients in overcoming the after-effects of therapy effectively with suitable supportive actions and health-care professionals in goal-oriented planning of these actions. Different AI methods are used for analyzing patients’ needs for supportive actions depending on the available health data and for a monitoring of these actions. Decision support methods are used for effective planning of actions based on the AI results of analysis. The solution approach is realized in the form of a web application for health-care professionals, which allows for data analysis and planning of actions, and a mobile application for patients, which facilitates documentation and monitoring of supportive actions. In combination, they facilitate a closed-loop workflow for the effective cooperation of health-care professionals and cancer patients. The solution approach is illustrated for an exemplary case scenario of colorectal cancer.enArtificial intelligenceData analysisDecision supportOncological aftercarejournal article