@misc{horsak_reliabilitat_2019, address = {Berlin}, type = {Invited talk}, title = {Reliabilität von {Messergebnissen} in der {Gang}- und {Bewegungsanalyse} – {Erfahrungsbericht} zu gängigen {Maßzahlen}}, author = {Horsak, Brian}, year = {2019}, note = {Projekt: ReMoCap-Lab}, keywords = {Center for Digital Health Innovation, Center for Digital Health and Social Innovation, Gait Analysis, Institut für Gesundheitswissenschaften, SP CDHSI Motor Rehabilitation, SP IGW Clinical \& Healthcare Research, Studiengang Physiotherapie, Wiss. Beitrag, best, best-bhorsak}, } @inproceedings{rind_towards_2019, title = {Towards a {Structural} {Framework} for {Explicit} {Domain} {Knowledge} in {Visual} {Analytics}}, url = {https://arxiv.org/abs/1908.07752}, doi = {10/gh377m}, abstract = {Clinicians and other analysts working with healthcare data are in need for better support to cope with large and complex data. While an increasing number of visual analytics environments integrates explicit domain knowledge as a means to deliver a precise representation of the available data, theoretical work so far has focused on the role of knowledge in the visual analytics process. There has been little discussion about how such explicit domain knowledge can be structured in a generalized framework. This paper collects desiderata for such a structural framework, proposes how to address these desiderata based on the model of linked data, and demonstrates the applicability in a visual analytics environment for physiotherapy.}, booktitle = {Proc. {IEEE} {Workshop} on {Visual} {Analytics} in {Healthcare} ({VAHC})}, author = {Rind, Alexander and Wagner, Markus and Aigner, Wolfgang}, year = {2019}, note = {Projekt: KAVA-Time Projekt: ReMoCap-Lab}, keywords = {Center for Digital Health Innovation, Digital Health, Forschungsgruppe Media Computing, Institut für Creative Media Technologies, Visual Computing, Vortrag, Wiss. Beitrag, peer-reviewed}, pages = {33--40}, }