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{{Publikation Erster Autor
 
{{Publikation Erster Autor
|ErsterAutorNachname=Weller
+
|ErsterAutorNachname=Nguyen
|ErsterAutorVorname=Tobias
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|ErsterAutorVorname=Anna
 
}}
 
}}
 
{{Publikation Author
 
{{Publikation Author
 
|Rank=2
 
|Rank=2
|Author=Maria Maleshkova
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|Author=Tobias Weller
}}
 
{{Publikation Author
 
|Rank=3
 
|Author=Keno März
 
}}
 
{{Publikation Author
 
|Rank=4
 
|Author=Lena Maier-Hein
 
 
}}
 
}}
 
{{Inproceedings
 
{{Inproceedings
|Referiert=True
+
|Referiert=Ja
|Title=A RESTful Approach for Developing Medical Decision Support Systems
+
|Title=FAIRnets Search - A Prototype Search Service to Find Neural Networks
|Year=2015
+
|Year=2019
|Booktitle=The Semantic Web: ESWC 2015
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|Booktitle=Semantics: SEMPDS 2019
|Pages=376-384
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|Organization=Proceedings of the Posters and Demo Track of the 15th International Conference on Semantic Systems
|Publisher=Springer
+
|Publisher=CEUR
|Volume=9341
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|Volume=2451
 
}}
 
}}
 
{{Publikation Details
 
{{Publikation Details
|Abstract=Current developments in the medical sector are witnessing the growing digitalization of data in terms of patient tests, records and trials, use of sensors for monitoring and recording procedures, and em- ploying digital imagery. Besides the increasing number of published guide- lines and studies, it has been shown that clinicians are often unable to observe these guidelines correctly during the actual care process.[1] The increasing number of guidelines and studies, and also the fact that physi- cians are often unable to observe these guidelines correctly provide the foundation for this paper. We will tackle these problems by developing a medical assistance system which processes the gathered and integrated data from different sources, and assists the physicians in making deci- sions, preparing treatment plans, and even guide surgeons during invasive procedures. In this paper we demonstrate how a RESTful architecture combined with applying Linked Data principles for data storage and ex- change can effectively be used for developing medical decision support systems. We propose different autonomous subsystems that automati- cally process data relevant to their purpose. These so-called ”Cognitive Apps” provide RESTful interfaces and perform tasks such as convert- ing and uploading data and deducing medical knowledge by using in- ference rules. The result is an adaptive decision support system, based on distributed decoupled Cognitive Apps, which can preprocess data in advance but also support real-time scenarios. We demonstrate the prac- tical applicability of our approach by providing an implementation of a system for processing patients with liver tumors. Finally, we evaluate the system in terms of knowledge deduction and performance.
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|Abstract=Research on neural networks has gained significant momen- tum over the past few years. A vast number of neural networks is current- ly being developed and trained on available data in research as well as in industry. As the number of neural network architectures increases, we want to support people in the field of machine learning by making existing architectures easier to find and reuse. In this Demo, we support the findability and reusability of Neural Net- works by using the FAIRnets Search. Attendees will learn how to use the FAIRnets Search web service to search the FAIRnets dataset. The FAIRnets dataset is an RDF dataset containing information about alrea- dy modeled neural networks. By applying RDF and OWL, our system can be queried using SPARQL queries indicating the desired character- istics of the neural network. As a result, all neural networks fulfilling the search query are returned to the user. The returned search results support users to gain insights into existing neural networks. Furthermore, we give the possibility to get more detailed information about the archi- tecture of the networks, as well as further links. The demo is available at http://km.aifb.kit.edu/services/fairnets/.
|ISBN=978-3-319-25638-2
+
|ISBN=1613-0073
|ISSN=0302-9743
+
|Projekt=XLiMe
|Download=Weller RestfulApproachMedicalDomain.pdf,
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|Forschungsgruppe=Web Science
|DOI Name=10.1007/978-3-319-25639-9_50
 
|Projekt=SFB/Transregio 125
 
|Forschungsgruppe=Web Science und Wissensmanagement
 
 
}}
 
}}

Version vom 7. Oktober 2019, 10:38 Uhr


FAIRnets Search - A Prototype Search Service to Find Neural Networks




Published: 2019

Buchtitel: Semantics: SEMPDS 2019
Ausgabe: 2451
Verlag: CEUR
Organisation: Proceedings of the Posters and Demo Track of the 15th International Conference on Semantic Systems

Referierte Veröffentlichung

BibTeX

Kurzfassung
Research on neural networks has gained significant momen- tum over the past few years. A vast number of neural networks is current- ly being developed and trained on available data in research as well as in industry. As the number of neural network architectures increases, we want to support people in the field of machine learning by making existing architectures easier to find and reuse. In this Demo, we support the findability and reusability of Neural Net- works by using the FAIRnets Search. Attendees will learn how to use the FAIRnets Search web service to search the FAIRnets dataset. The FAIRnets dataset is an RDF dataset containing information about alrea- dy modeled neural networks. By applying RDF and OWL, our system can be queried using SPARQL queries indicating the desired character- istics of the neural network. As a result, all neural networks fulfilling the search query are returned to the user. The returned search results support users to gain insights into existing neural networks. Furthermore, we give the possibility to get more detailed information about the archi- tecture of the networks, as well as further links. The demo is available at http://km.aifb.kit.edu/services/fairnets/.

ISBN: 1613-0073

Projekt

XLiMe



Forschungsgruppe

Web Science


Forschungsgebiet