TY - CHAP A1 - Philipp, Patrick A1 - Morales Georgi, Rafael X. A1 - Beyerer, Jürgen A1 - Robert, Sebastian A1 - Beyerer, Jürgen T1 - Analysis of Control Flow Graphs Using Graph Convolutional Neural Networks T2 - 2019 6th International Conference on Soft Computing & Machine Intelligence (ISCMI) N2 - With the digital transformation of companies, ever larger amounts of data are generated and available for analysis. Process mining techniques can be used to extract and analyze process models from these data. Related techniques have quickly developed into an important field with constantly increasing investments in recent years. Thus, the automated analysis of processes has gained an important role in many companies. In this context, graphs have been shown to be an intuitive representation of how the gathered processes are carried out using the aforementioned techniques. For the analysis of these so-called control flow graphs, we investigate the use of convolution neural networks, which are specially designed for graphs: graph convolution networks (GCNs). In our contribution, GCNs are used to perform a regression task based on individual control flows of a process in which farmers apply for specific governmental payments. The approach achieved promising results on this publicly available data set. KW - workflow KW - process analysis KW - process mining KW - convolutional neural networks KW - digital transformation KW - control flow Y1 - 2021 UR - https://doi.org/10.1109/ISCMI47871.2019.9004296 SP - 73 EP - 77 ER - TY - CHAP A1 - Philipp, Patrick A1 - Becker, Silvia A1 - Robert, Sebastian A1 - Hempel, Dirk A1 - Beyerer, Jürgen T1 - Modeling of Medical Treatment Processes for an Interactive Assistance Based on the Translation of UML Activities into PROforma T2 - 2019 International Conference on Computational Science and Computational Intelligence (CSCI) N2 - In modern medicine, Clinical Practice Guidelines (CPGs) are well-established resources for the appropriate treatment of diseases. Evidence-based CPGs contain recommendations which are based on the state of the art and which have been achieved by consensus of several experts. Nevertheless, there is a potential for problems in translating guideline documents into specific actions for physicians. Therefore we propose to formalize the treatment process in an understandable representation as UML activities together with a domain expert. This formalization serves as a basis for the transfer of knowledge into a model, in this case PROforma, which directly allows execution in an interactive assistance software. The results of this work are part of an ongoing research project on the treatment of colon cancer based on the corresponding evidence-based CPG. KW - Clinical Practice Guidelines KW - Medical Treatment KW - Assistance Y1 - 2021 UR - https://doi.org/10.1109/CSCI49370.2019.00184 SP - 972 EP - 976 ER - TY - CHAP A1 - Philipp, Patrick A1 - Bommersheim, Marie A1 - Robert, Sebastian A1 - Beyerer, Jürgen T1 - Probabilistic Estimation of Human Interaction Needs in Context of a Robotic Assistance in Geriatrics T2 - Current Directions in Biomedical Engineering N2 - The key purpose of assistance robots is to help people coping with work-related or everyday tasks. To ensure an intuitive and effective support by an assistance robot, its expectation conform behavior is essential. In particular, when using assistance robots in geriatrics to assist elderly patients, special attention to the human-robot interaction should be paid. In order to help elderly patients maintain their independence and abilities as much as possible, the robot should only intervene when its support is needed. Therefore, the continuous estimation of the patient’s need for interaction is of particular importance. For enabling suitable models to estimate this need, we elaborate the use of Bayesian Networks. The analysis of our results seems promising, yielding a robust and practical approach. KW - Bayesian Networks KW - Virtual Evidence KW - Robotics KW - Gerontology KW - Social Robot KW - Assistance Technology Y1 - 2021 UR - https://doi.org/10.1515/cdbme-2019-0109 SP - 433 EP - 436 ER -