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{{Publikation Erster Autor
 
{{Publikation Erster Autor
|ErsterAutorNachname=Nguyen
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|ErsterAutorNachname=Weller
|ErsterAutorVorname=Anna
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|ErsterAutorVorname=Tobias
 
}}
 
}}
 
{{Publikation Author
 
{{Publikation Author
 
|Rank=2
 
|Rank=2
|Author=Tobias Weller
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|Author=Maria Maleshkova
 
}}
 
}}
 
{{Publikation Author
 
{{Publikation Author
 
|Rank=3
 
|Rank=3
|Author=York Sure-Vetter
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|Author=Keno März
 +
}}
 +
{{Publikation Author
 +
|Rank=4
 +
|Author=Lena Maier-Hein
 
}}
 
}}
 
{{Inproceedings
 
{{Inproceedings
|Title=Making Neural Networks FAIR
+
|Referiert=True
|Year=2019
+
|Title=A RESTful Approach for Developing Medical Decision Support Systems
|Month=Juli
+
|Year=2015
|Howpublished=https://arxiv.org/abs/1907.11569
+
|Booktitle=The Semantic Web: ESWC 2015
 +
|Pages=376-384
 +
|Publisher=Springer
 +
|Volume=9341
 
}}
 
}}
 
{{Publikation Details
 
{{Publikation Details
|Abstract=Research on neural networks has gained significant momentum over the past few years. A plethora of neural networks is currently being trained on available data in research as well as in industry. Because training is a resource-intensive process and training data cannot always be made available to everyone, there has been a recent trend to attempt to re-use already-trained neural networks. As such, neural networks themselves have become research data. In this paper, we present the Neural Network Ontology, an ontology to make neural networks findable, accessible, interoperable and reusable as suggested by the well-established FAIR guiding principles for scientific data management and stewardship. We created the new FAIRnets Dataset that comprises about 2,000 neural networks openly accessible on the internet and uses the Neural Network Ontology to semantically annotate and represent the neural networks. For each of the neural networks in the FAIRnets Dataset, the relevant properties according to the Neural Network Ontology such as the description and the architecture are stored. Ultimately, the FAIRnets Dataset can be queried with a set of desired properties and responds with a set of neural networks that have these properties. We provide the service FAIRnets Search which is implemented on top of a SPARQL endpoint and allows for querying, searching and finding trained neural networks annotated with the Neural Network Ontology. The service is demonstrated by a browser-based frontend to the SPARQL endpoint.
+
|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.
|Link=https://arxiv.org/abs/1907.11569
+
|ISBN=978-3-319-25638-2
|Forschungsgruppe=Web Science
+
|ISSN=0302-9743
 +
|Download=Weller RestfulApproachMedicalDomain.pdf,
 +
|DOI Name=10.1007/978-3-319-25639-9_50
 +
|Projekt=SFB/Transregio 125
 +
|Forschungsgruppe=Web Science und Wissensmanagement
 
}}
 
}}

Version vom 7. Oktober 2019, 10:31 Uhr


A RESTful Approach for Developing Medical Decision Support Systems




Published: 2015

Buchtitel: The Semantic Web: ESWC 2015
Ausgabe: 9341
Seiten: 376-384
Verlag: Springer

Referierte Veröffentlichung

BibTeX

Kurzfassung
[[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.]]

ISBN: 978-3-319-25638-2
ISSN: 0302-9743
Download: Media:Weller RestfulApproachMedicalDomain.pdf
DOI Link: 10.1007/978-3-319-25639-9_50

Projekt

SFB/Transregio 125



Forschungsgruppe

Web Science und Wissensmanagement


Forschungsgebiet