Georg Tennigkeit
Haptically Rendered Impossible Space for VR
Abstract: Two unsolved problems of Virtual Reality are 1) exploration of virtual worlds through natural walking and 2) large-scale haptics. We propose solutions to both problems that complement one another:
To enable natural walking in a small space, we place the entire play area in a grid of 2x3 tiles, which takes up 3m² of physical space. This grid can contain more than six virtual tiles by overlaying multiple tiles in the same position, a technique known as impossible space. As impossible spaces commonly lose the continuity of the larger space, we have defined repeatable walking patterns that can be rationalized into a non-overlapping motion.
Due to the limited room layouts possible in 2x3-Space, haptic walls become more feasible. We place vertical rotary actuators at each pillar and attach an upright wall panel to them. Each wall panel can represent four of the seven possible walls between two tiles. We describe a motion control algorithm that considers the impossibility of the space and seamlessly transitions between haptically rendering a wall and making way for the user.
Linea Schmidt
Extraction of Crohn’s Disease Clinical Phenotypes from Clinical Text Using Natural Language Processing
Abstract: Background: Crohn’s Disease (CD) patient heterogeneity in clinical practice is captured by the Montreal Classification. While the underlying concepts, disease behavior and age at diagnosis, are relevant outcomes and covariates in studies from real-world data, extracting this clinical information through manual chart review is labor-intensive and with limited scalability.
Methods: We developed and evaluated automated phenotyping algorithms to extract disease behavior and age at diagnosis from clinical narrative texts, using a rule-based approach based on the spaCy framework, and an approach based on zero-shot inference. The underlying data included 49,572 clinical notes and 2,204 radiology reports from 584 CD patients of the Mount Sinai Crohn’s and Colitis Registry. A test set of 200 clinical texts
per classification category was labeled at sentence-level, in addition to patient-level ground truth data. The algorithms were evaluated based on their recall, precision, specificity values, and F1-scores.
Results: For the labeled dataset, an overall Cohen’s kappa inter-annotator agreement of 0.84 was achieved. The rule-based approach yielded high recall and precision values (0.75 - 1.00) on a note level for the behavioral disease phenotype using clinical notes, with slightly reduced performance using radiology reports. For age at diagnosis, recall and precision values of 0.81 and 0.88 were achieved on note-level, respectively. For both categories, the performance on patient- compared to note-level was reduced, potentially due to the accumulation of false positives and limitations in the data availability.
Conclusion: Based on our newly annotated dataset, we demonstrated the feasibility of automatically extracting disease behavior and age at diagnosis from clinical text. The resulting labels may facilitate extensive cohort analyses based on electronic health records, and support chart review processes in the future.
