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Converting the Representation of Medical Data: Criteria to Code the Underlying Cause of Death

Authors: A Rossi-Mori; D M Pisanelli;

Converting the Representation of Medical Data: Criteria to Code the Underlying Cause of Death

Abstract

AbstractThis study deals with a set of coding directives that were conceived for trained coding clerks and rely upon knowledge of their cultural background. These directives were formalized and adapted for computer use and in this form must rely upon a background of explicit medical knowledge. Medical data on death certificates are an invaluable source of information regarding prevention of major causes of death. These causes are coded and tabulated worldwide by means of the International Classification of Diseases (ICD). The ICD manual issues directives to achieve uniformity of coding throughout the world. The coder is required to trace back the flow of events which caused death and to single out the most significant concept from the statistical point of view. After emphasizing the problems encountered in the formalization, the methodological contribution of this work to the identification of a modular architecture for a system which represents and “reshapes” knowledge from medical documents is presented. Therefore we focus on the features of the two kinds of knowledge that must be supplied to a knowledge-based system, in order to enable it to perform semantic conversions on given medical data, namely: i) generic guidelines; ii) detailed medical knowledge.

Keywords

Software Design, Cause of Death, Data Interpretation, Statistical, Software Validation, Humans, Expert Systems, Death Certificates

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Top 10%
Average
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