Title: Toward cognitive pipelines of medical assistance algorithms
Authors: Philipp, Patrick
Maleshkova, Maria 
Katic, Darko
Weber, Christian
Götz, Michael
Rettinger, Achim
Speidel, Stefanie
Kämpgen, Benedikt
Nolden, Marco
Wekerle, Anna Laura
Dillmann, Rüdiger
Kenngott, Hannes
Müller, Beat
Studer, Rudi
Language: eng
Keywords: Cognitive architecture;Computer aided medicine;Phase recognition;Semantic Web;Tumor progression mapping
Issue Date: 1-Sep-2016
Publisher: Springer
Document Type: Article
Journal / Series / Working Paper (HSU): International Journal of Computer Assisted Radiology and Surgery
Volume: 2016
Issue: 11
Page Start: 1743
Page End: 1753
Publisher Place: Berlin
Purpose: Assistance algorithms for medical tasks have great potential to support physicians with their daily work. However, medicine is also one of the most demanding domains for computer-based support systems, since medical assistance tasks are complex and the practical experience of the physician is crucial. Recent developments in the area of cognitive computing appear to be well suited to tackle medicine as an application domain. Methods: We propose a system based on the idea of cognitive computing and consisting of auto-configurable medical assistance algorithms and their self-adapting combination. The system enables automatic execution of new algorithms, given they are made available as Medical Cognitive Apps and are registered in a central semantic repository. Learning components can be added to the system to optimize the results in the cases when numerous Medical Cognitive Apps are available for the same task. Our prototypical implementation is applied to the areas of surgical phase recognition based on sensor data and image progressing for tumor progression mappings. Results: Our results suggest that such assistance algorithms can be automatically configured in execution pipelines, candidate results can be automatically scored and combined, and the system can learn from experience. Furthermore, our evaluation shows that the Medical Cognitive Apps are providing the correct results as they did for local execution and run in a reasonable amount of time. Conclusion: The proposed solution is applicable to a variety of medical use cases and effectively supports the automated and self-adaptive configuration of cognitive pipelines based on medical interpretation algorithms.
Organization Units (connected with the publication): Karlsruhe Institute of Technology
ISSN: 18616410
Publisher DOI: 10.1007/s11548-015-1322-y
Appears in Collections:6 - Publication references (only metadata) of your publications before HSU

Show full item record

CORE Recommender


checked on Feb 21, 2024

Google ScholarTM




Items in openHSU are protected by copyright, with all rights reserved, unless otherwise indicated.