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|Abstract=The growing number of connected medical devices in the operating room, as well as electronic health records, provide valuable data to analyze surgical processes. In this area of surgical data science, i.e. surgical process analysis, is the challenge to combine methods from computer science and statistics with surgical knowledge about processes and data, such as standard operating procedures, surgical guidelines, uncertainty and data quality. We propose to develop a framework, that integrates handling of data, knowledge models and data analysis with an intuitive user interface for data visualization, process analytics and knowledge capturing, which is usable by domain experts such as surgeons. The aim of this so-called “Surgical Process Analyzer” is to bring surgical process analysis into surgical practice, to provide benefit for patients and advance research in surgical data science by addressing real-world use-cases with collaboration of surgeons and data scientists.
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|Abstract=The growing number of connected medical devices in the operating room and electronic health records provide valuable data to analyze surgical processes for workflow optimization, context-aware assistance systems and prediction of adverse events. In this area of surgical data science, i.e. surgical process analysis, a key challenge is to combine methods from computer science and statistics with surgical knowledge about processes and data, such as standard operating procedures, surgical guidelines, uncertainty and quality of data. We propose a novel framework that integrates handling of data, knowledge models and data analysis with an intuitive user interface for data visualization, process analytics and knowledge capturing. A key goal of this so called “Surgical Process Analyzer” is to empower surgical domain experts to perform process analysis in their surgical practice. As a result we expect to advance research in surgical data science by addressing real-world use-cases with collaboration of surgeons and data scientists that provides benefit for patients.
|Download=Bringing data-driven process analysis into surgical practice – the surgi....pdf,  
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|Download=Bringing data-driven process analysis into surgical practice – the surgi....pdf,
 
|Link=http://www.surgical-data-science.org
 
|Link=http://www.surgical-data-science.org
 
|Forschungsgruppe=Web Science und Wissensmanagement
 
|Forschungsgruppe=Web Science und Wissensmanagement
 
}}
 
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Aktuelle Version vom 9. Juni 2016, 16:02 Uhr


Bringing Data-Driven Process Analysis into Surgical Practice - the Surgical Process Analyzer


Bringing Data-Driven Process Analysis into Surgical Practice - the Surgical Process Analyzer



Published: 2016 Juni

Buchtitel: Proceedings Surgical Data Science
Seiten: 6
Verlag: Springer
Erscheinungsort: Heidelberg
Organisation: Surgical Data Science

Referierte Veröffentlichung

BibTeX

Kurzfassung
The growing number of connected medical devices in the operating room and electronic health records provide valuable data to analyze surgical processes for workflow optimization, context-aware assistance systems and prediction of adverse events. In this area of surgical data science, i.e. surgical process analysis, a key challenge is to combine methods from computer science and statistics with surgical knowledge about processes and data, such as standard operating procedures, surgical guidelines, uncertainty and quality of data. We propose a novel framework that integrates handling of data, knowledge models and data analysis with an intuitive user interface for data visualization, process analytics and knowledge capturing. A key goal of this so called “Surgical Process Analyzer” is to empower surgical domain experts to perform process analysis in their surgical practice. As a result we expect to advance research in surgical data science by addressing real-world use-cases with collaboration of surgeons and data scientists that provides benefit for patients.

Download: Media:Bringing data-driven process analysis into surgical practice – the surgi....pdf
Weitere Informationen unter: Link



Forschungsgruppe

Web Science und Wissensmanagement


Forschungsgebiet