The development of the next generation clinical decision support systems while using state-of-the-art statistical techniques have the potential to identify the best strategy towards personalized care by reaching a better understanding of the question “What works for whom and when?”. This way of thinking moves away from the traditional approach of summarizing individual patients into the average patient. Additionally, by using real-time information, more precise estimates of a patient’s risk of (clusters of) outcomes could be generated. These more precise estimates could also guide evidence based personalized care and patient involvement in decision making, since physicians can discuss this real time information together with the patient. In this research line, we evaluate the role of the next generation of clinical decision support systems within real-world clinical practice. For this, we will develop and evaluate the next generation of clinical models that incorporate the knowledge obtained from research lines 1, 2, and 3; i.e. they are flexible enough to maintain within clinical practice, they can explain why certain advise is generated and they provide patient-oriented advice.
We focus on 4 different domains, lung cancer, prostate and gastric cancer, and endometrial cancer:
4.1 Demonstrator: Lung Cancer; Amsterdam UMC; PI Annemarie Becker-Commissaris; and Radboud UMC; PI Iris Walraven; vacancy for joint PhD candidate.
4.2 Demonstrator: Prostate and Gastric Cancer; NKI / AvL; PI Lonneke van de Poll-Franse; and Radboud UMC; PI Iris Walraven; vacancy for joint PhD candidate.
4.3 Demonstrator: Endometrial Cancer; Radboud UMC; PIs Rosella Hermens and Hanny Pijnenborg; vacancy for PhD candidate.