Andreas Holzinger Boeken

This book constitutes the refereed proceedings of the 7th Conference of the Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society, USAB 2011, in Graz, Austria, in November 2011. The 18 revised full papers together with 29 revised short papers and 2 posters presented were carefully reviewed and selected from 103 submissions. The papers are organized in topical sections on cognitive approaches to clinical data management for decision support, human-computer interaction and knowledge discovery in databases (hci-kdd), information usability and clinical workflows, education and patient empowerment, patient empowerment and health services, information visualization, knowledge & analytics, information usability and accessibility, governmental health services & clinical routine, information retrieval and knowledge discovery, decision making support & technology acceptance, information retrieval, privacy & clinical routine, usability and accessibility methodologies, information usability and knowledge discovery, human-centred computing, and biomedical informatics in health professional education.

Data driven Artificial Intelligence (AI) and Machine Learning (ML) in digital pathology, radiology, and dermatology is very promising. In specific cases, for example, Deep Learning (DL), even exceeding human performance. However, in the context of medicine it is important for a human expert to verify the outcome. Consequently, there is a need for transparency and re-traceability of state-of-the-art solutions to make them usable for ethical responsible medical decision support. Moreover, big data is required for training, covering a wide spectrum of a variety of human diseases in different organ systems. These data sets must meet top-quality and regulatory criteria and must be well annotated for ML at patient-, sample-, and image-level. Here biobanks play a central and future role in providing large collections of high-quality, well-annotated samples and data. The main challenges are finding biobanks containing ‘‘fit-for-purpose’’ samples, providing quality related meta-data, gaining access to standardized medical data and annotations, and mass scanning of whole slides including efficient data management solutions.

The Workgroup Human–Computer Interaction & Usability Engineering (HCI&UE) of the Austrian Computer Society (OCG) serves as a platform for interdisciplinary research and development. While HCI traditionally connects psychologists and computer scientists, usability engineering (UE) focuses on the effective implementation of applications within software engineering. In 2008, the theme was Human–Computer Interaction for Education and Work (HCI4EDU), culminating in the 4th annual Usability Symposium USAB 2008 in Graz, Austria. Similar to the previous year's focus on HCI in Medicine and Health Care (HCI4MED), technological advancements in education and work are rapidly increasing. Learners, teachers, and knowledge workers are constantly exposed to new, cost-effective technologies. However, the knowledge gained in educational institutions may not be sufficient for a lifetime. Learning and working are parallel processes, making lifelong learning (LLL) essential in today’s society. The surge in educational technologies is significant, but it is crucial to remember that learning is fundamentally a cognitive and social process that cannot be wholly replaced by technology.

One of the grand challenges in our digital world are the large, complex and often weakly structured data sets, and massive amounts of unstructured information. This “big data” challenge is most evident in biomedical informatics: the trend towards precision medicine has resulted in an explosion in the amount of generated biomedical data sets. Despite the fact that human experts are very good at pattern recognition in dimensions of <= 3; most of the data is high-dimensional, which makes manual analysis often impossible and neither the medical doctor nor the biomedical researcher can memorize all these facts. A synergistic combination of methodologies and approaches of two fields offer ideal conditions towards unraveling these problems: Human–Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human capabilities with machine learning. This state-of-the-art survey is an output of the HCI-KDD expert network and features 19 carefully selected and reviewed papers related to seven hot and promising research areas: Area 1: Data Integration, Data Pre-processing and Data Mapping; Area 2: Data Mining Algorithms; Area 3: Graph-based Data Mining; Area 4: Entropy-Based Data Mining; Area 5: Topological Data Mining; Area 6 Data Visualization and Area 7: Privacy, Data Protection, Safety and Security.