The next generation of PRO-React by CANKADO is designed to predict impending incident threats at an earlier stage than previously feasible and -- by more timely intervention -- help physicians to eliminate or mitigate the severity of an unfavourable event, reduce the required intensity of countermeasures, or otherwise reduce patient risks.~A highly reliable identification of situations classified as low-risk by CANKADO could also enable a more focused utilization of resources as well as enhanced patient comfort and decreased stress, e.g., due to less frequent monitoring visits or reduced need for invasive diagnostics.~The OMCAT Register aims to provide learning databases in cancer comprising both PRO data using PRO-React and ground truth (outcome data verified by the physician during patient examinations). Intelligent learning and knowledge engineering procedures will utilize this PRO data to provide high-quality event prediction algorithms. The ground-truth data enables so-called supervised learning techniques of artificial intelligence, because predicted events can be verified with a high level of certainty from ground-truth data.~The PRO data of a patient provide what is known in engineering, physics, and statistics as time series of observations. The unique feature of PRO time series for applications in cancer is the very high sampling frequency (e.g., daily or better) compared to examinations, which generally occur at fixed, and much less frequent intervals. Prediction algorithms based on PRO data would thus be ideally suited to reduce the delay in detecting events, for example, by triggering physician appointments or indicating the need for more intensive medical diagnostics.
The OMCAT Register aims to provide learning databases in cancer comprising both PRO data using PRO-React and ground truth (outcome data verified by the physician during patient examinations). Intelligent learning and knowledge engineering procedures will utilize this PRO data to provide high-quality event prediction algorithms. The ground-truth data enables so-called supervised learning techniques of artificial intelligence, because predicted events can be verified with a high level of certainty from ground-truth data.