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Clustering diagnoses: a method of interpreting morbidity data.

P Hodgkin·D Metcalfe
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Abstract

Detailed classification of morbidity data provides problems in large-scale surveys in general practice: a balance between precise diagnosis and realistic uncertainty must be maintained, and it can be hard to detect the overall pattern when a large number of rubrics is involved. This paper reports the development of a system of clustered diagnoses in which similar diagnoses are linked together in homogeneous clusters. The system is based on the RCGP codes and is compatible with ICHPPC-2. The aim was not to produce another classification of morbidity but to use the system to apply to data already coded using a specific primary code.

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DiagnosisDiseaseEnglandHumansMorbidityPhysicians, FamilySpace-Time ClusteringJournal Article

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P Hodgkin, D Metcalfe. 1984. Clustering diagnoses: a method of interpreting morbidity data.. https://doi.org/10.1093/fampra%2F1.4.228

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