Approximate Representations in the Medical Domain

The target of many studies in the health sciences is the discovery of cause-effect relationships among observed variables of interest, for example: treatments, exposures, preconditions, and outcomes. Causal modeling and causal discovery are central to medical science. In order to algorithmically con...

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Bibliographic Details
Main Author: Mazlack, Lawrence J.
Format: Conference Proceeding
Language:English
Subjects:
Mathematics of computing
Mathematics of computing > Discrete mathematics
Online Access:Request full text
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Summary:The target of many studies in the health sciences is the discovery of cause-effect relationships among observed variables of interest, for example: treatments, exposures, preconditions, and outcomes. Causal modeling and causal discovery are central to medical science. In order to algorithmically consider causal relations, the relations must be placed into a representation that supports manipulation. Knowledge of at least some causal effects is imprecise. The most widespread causal representation is directed acyclic graphs (DAGs). However, DAGs are severely limited in what portion of the common sense world they can represent. Another network methodology, fuzzy cognitive maps hold promise. This paper considers the needs of commonsense causality and suggests Fuzzy Cognitive Maps as a useful methodology.
DOI:10.1109/WI-IAT.2010.15