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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:dateCreated |
1994-4-21
|
| pubmed:abstractText |
This paper investigates the advantages of introducing feedback between the processes of automated medical diagnosis and automated diagnostic-knowledge acquisition. Experimental results show that a diagnostic system with such feedback is capable of an efficiency/accuracy trade-off when applied to the problem of diagnosing multiple disorders. A primary feature of this work is a new mechanism, called the "diagnostic-unit" representation, for remembering results of previous diagnoses. The diagnostic-unit representation is explicitly tailored to capture the most likely relationships between disorders and clusters of findings. Unlike typical bipartite "If-Then" representations, the diagnostic-unit representation uses a general graph representation to efficiently represent complex causal relationships between disorders and clusters of findings. In addition to the basic diagnostic-unit concept, this paper presents experience-based strategies for incrementally deriving and updating diagnostic units and the various relationships between them. Techniques for selecting diagnostic units relevant to a given problem and then combining them to generate solutions are also described.
|
| pubmed:grant | |
| pubmed:language |
eng
|
| pubmed:journal | |
| pubmed:citationSubset |
IM
|
| pubmed:status |
MEDLINE
|
| pubmed:issn |
0195-4210
|
| pubmed:author | |
| pubmed:issnType |
Print
|
| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
|
| pubmed:pagination |
454-60
|
| pubmed:dateRevised |
2008-11-20
|
| pubmed:meshHeading | |
| pubmed:year |
1993
|
| pubmed:articleTitle |
A hybrid system for diagnosing multiple disorders.
|
| pubmed:affiliation |
Laboratory for Computer Science, Massachusetts Institute of Technology, Cambridge 02139.
|
| pubmed:publicationType |
Journal Article,
Research Support, U.S. Gov't, P.H.S.,
Research Support, Non-U.S. Gov't
|