Prof. Dr. Felix Naumann

The meeting will be dedicated to organisational issues of our cluster:

-          New members (DSE Research School and member groups)

-          Retrospective of our retreat

-          Weekly meetings

-          Web site and fall reports

-          Shared tasks

-          Other cluster activities

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.

Sumit Shekhar

Abstract: Achieving a PhD is a monumental accomplishment, but the path to this achievement can be fraught with stress, potentially impacting the health and well-being of doctoral students. Without timely intervention, these challenges can significantly diminish the quality of life and academic performance. In this talk, I will explore the multifaceted dimensions of a PhD student's health, encompassing the social, mental, and physical aspects. The objective is to provide a set of general guidelines for maintaining one's health while navigating the demanding world of doctoral studies. Moreover, I will share some real-world examples of burnout, equipping attendees with the knowledge to recognize and address such issues. This talk draws from personal experiences and the collective wisdom of fellow PhD students, offering insights to enhance the well-being of those on the challenging path to academic excellence.

Ilin Tolovski

Title: Interoperability in Data Science Pipelines

Abstract: The development of data science pipelines (DSP) has been steadily increasing in the past decade, reaching its peak in 2021. While the growth of applications can be attributed to novel algorithms and analytics libraries, the interoperability of new DSP has been limited. In a comprehensive analysis of over 494k Python repositories available on GitHub, we have found that only 34% are data science repositories that provide access to their input data. The absence of input data leads to projects that are difficult to execute in a new environment, and results that are difficult to reproduce. We propose Stork, a system for automated data and pipeline migration. By performing static code analysis, Stork removes the human from the loop when migrating projects and reproducing results. Stork supports the migration to database management systems, such as PostgesSQL, as well as cloud object storage, AWS S3.

Prof. Dr. Patrick Baudisch

Prof. Dr. Tilmann Rabl

Prof. Dr. Holger Karl