Wissensbasierte Diagnose- und Informationssysteme

Puppe, Frank; Bamberger, Stefan; Gappa, Ute; Poeck, Karsten

doi:10.1007/978-3-642-61471-2

Cited by 29 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, medical knowledge is not easily encoded to be machine readable. There have been advances in the domain of expert systems in narrowly specified areas (e. g. treatment planning in patients with a congenital heart disease [19]), however, in fields that require a holistic look at a situation (such as the initial diagnosis of incoming patients), humans beat automated systems [30]. This domain remains reserved for humans.…”

Section: Agent Based Approachmentioning

confidence: 99%

Beyond Automation: Hybrid Human-Computer Decision Support Systems in Hospitals

Niemann¹,

Eymann²

2008

Fifth IEEE Workshop on Engineering of Autonomic and Autonomous Systems (Ease 2008)

View full text Add to dashboard Cite

Scheduling in hospitals is a decision problem that relies on encoded information (such as room assignments or diagnoses) as much as on non-encoded information (medical knowledge). To support clinical scheduling with information systems, both types of information and their respective carriers have to be incorporated in combination, thus always requiring a "human-in-the-loop", which prohibits full autonomy. In contrast to other proposals in Autonomic Computing, this paper makes a case for a hybrid humancomputer decision support system to support scheduling in hospitals that combines humans' ability to improvise and to assess a situation, with computers' ability to communicate quickly to achieve emergent self-organization. Furthermore, the paper outlines an approach to evaluate such a system.

show abstract

Section: Agent Based Approachmentioning

confidence: 99%

Beyond Automation: Hybrid Human-Computer Decision Support Systems in Hospitals

Niemann¹,

Eymann²

2008

Fifth IEEE Workshop on Engineering of Autonomic and Autonomous Systems (Ease 2008)

View full text Add to dashboard Cite

show abstract

“…First, the expert system shell toolkit D3 (Puppe et al 1996) was used to set up meteorologic knowledge bases and to introduce the METSYS staff to knowledge management techniques. Unfortunately, the integration of D3 into the GRASS GIS environment did not seem reasonable at the level of maturity necessary at this time.…”

Section: Knowledge-based Classification In Grass Gismentioning

confidence: 99%

A Spatial Decision Support System for Radar‐meteorology Data in South Africa

Löwe¹

2004

Transactions in GIS

View full text Add to dashboard Cite

The South African Weather Service (SAWS) operates a radar station network, providing data continuity back to 1994, which is unique for southern Africa. The Geographical Resources Analysis Support System (GRASS) GIS was introduced to the SAWS "Meteorological Systems and Technology" (METSYS) radar research center in 1999 where it is still used for meteorologic research. The predominantly convective nature of precipitation in southern Africa creates a public demand for severe weather information systems for convective cells. Such a system was set up in GRASS GIS, using rule-based expert systems to classify convective clouds. It isolates storm cells from stacks of reflectivity fields and derives information about their development stages. This data is both archived and also used to send out customised messages to target groups in specific areas of interest. Further, nationwide HTML-maps can be created, serving as an interactive front-end for a web-based weather information system. This service can also be made accessible from remote locations by broadcasting it as a datastream from satellite through the Worldspace digital radio system.

show abstract

“…• D3 [3] is a diagnostic expert system shell kit developed at Wuerzburg University for building and using expert systems for diagnosis. • D3WWW is a web based interface for D3 which allows many users to simultaneously use the system from their own computers.…”

Section: Introductionmentioning

confidence: 99%

“…Values can be entered in questionnaires and popup-menus with additional information are available to all objects(2). Diagnoses are presented as soon as they are suspected or established(3). A kind of "Card Layout" is used to switch between questionnaires, explanations, and file operations / preferences to keep the interface simple(4).…”

mentioning

confidence: 99%

Building Web-based knowledge clusters

Berberich¹,

Bamberger²

1998

IEE Colloquium on Web-Based Knowledge Servers

View full text Add to dashboard Cite

Today the WWW as an easy-to-use environment for all kind of applications is widely accepted. Knowledge based systems profit in many ways from this new technology. Using an internet browser as a generally available low-cost front-end makes it possible to offer the power of an expert system to many users. Depending on the declarative nature of knowledge structures it is very easy to generate dynamic html pages and to build up a knowledge server [1]. In spite of this, it is very difficult for a user to find the appropriate knowledge server for his demands. We discuss an approach how it is possible to integrate an arbitrary number of distributed knowledge servers to a knowledge cluster using only one internet server as a central communication interface to the user. The tool is based on an expert system shell tool kit called Coop-D3 [2] which can be used to build up heterogeneous diagnostic knowledge bases.

show abstract

Wissensbasierte Diagnose- und Informationssysteme

Cited by 29 publications

References 0 publications

Beyond Automation: Hybrid Human-Computer Decision Support Systems in Hospitals

Beyond Automation: Hybrid Human-Computer Decision Support Systems in Hospitals

A Spatial Decision Support System for Radar‐meteorology Data in South Africa

Building Web-based knowledge clusters

Contact Info

Product

Resources

About