GALEN based formal representation of ICD10

Background: Many medication errors are encountered in drug prescriptions, which would not occur if practitioners could remember the drug properties. They can refer to drug monographs to find these properties, however drug monographs are long and tedious to read during consultation. We propose a two-step approach for facilitating access to drug monographs. The first step, presented here, is the design of a graphical language, called VCM.

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“…[44]. The choice of pictograms, shapes and colors was based on existing conventions and possible analogies, but remained somewhat arbitrary.…”

Section: Discussionmentioning

confidence: 99%

An iconic language for the graphical representation of medical concepts

Lamy

Duclos

Bar-Hen

et al. 2008

BMC Med Inform Decis Mak

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“…The most frequent attribute relationships in SNOMED CT that were connected to the diagnostic categories in KSH97-P corresponded to the pathophysiological definition of ICD-10 disorders using anatomical location, morphology and cause of the disease. 15 This study showed that it is possible to generate new statistical information from primary care data with SNOMED CT using diagnostic data coded by primary care physicians according to KSH97-P/ICD-10. Our study shows that it may be possible in the future to combine a subset of ICD-10, which is well known, fairly small and widely accepted in many countries, with a complex, large, IT-system dependent terminology system like SNOMED CT for statistical purposes.…”

Section: Comparison With the Literaturementioning

confidence: 95%

“…29 The present recommendation for obtaining statistics on certain diseases of interest is manual selection of KSH97-P categories, 13 which is a method that could lead to arbitrary, non-comparable groups and is prone to error. 15 Defining attribute relationships in SNOMED CT are used to describe clinical findings in primary care expressed as 'Finding site', 'Causative agent' and 'Associated morphology', which are chosen from the 58 defining relationship types present in SNOMED CT. However, many of the attribute relationships are rarely used.…”

Section: Comparison With the Literaturementioning

confidence: 99%

“…13 Attempts at restructuring the monohierarchical structure of ICD-10 have been proposed. 15 The different levels of granularity in coding systems have been described previously, 16 as have the generations of terminology systems. 17 ICD-10 belongs to the first generation, and is generally considered to be a widely accepted classification, although it is based on a number of compromises regarding aetiology, anatomical site, circumstances of onset etc., 18 and has internal inconsistencies.…”

Section: Introductionmentioning

confidence: 99%

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Views of diagnosis distribution in primary care in 2.5 million encounters in Stockholm: a comparison between ICD-10 and SNOMED CT

Vikström¹,

Nyström²,

Åhlfeldt³

et al. 2010

Journal of Innovation in Health Informatics

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Background Primary care (PC) in Sweden provides ambulatory and home health care outside hospitals. Within the County Council of Stockholm, coding of diagnoses in PC is mandatory and is done by general practitioners (GPs) using a Swedish primary care version of the International Statistical Classification of Diseases, version 10 (ICD-10). ICD-10 has a mono-hierarchical structure. SNOMED CT is poly-hierarchical and belongs to a new generation of terminology systems with attributes (characteristics) that connect concepts in SNOMED CT and build relationships. Mapping terminologies and classifications has been pointed out as a way to attain additional advantages in describing and documenting healthcare data. A poly-hierarchical system supports the representation and aggregation of healthcare data on the basis of specific medical aspects and various levels of clinical detail. Objective To describe and compare diagnoses and health problems in KSH97-P/ICD-10 and SNOMED CT using primary care diagnostic data, and to explore and exemplify complementary aggregations of diagnoses and health problems generated from a mapping to SNOMED CT.

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“…This gives tree structural representation of the ICD-10-CM data helping to understand just the classification of the main term to sub terms alone with the diagnosis. Even though the OWL Ontology Graph shown in Fig 3, provides us with sufficient information like the sub classes, super classes, disjoint classes and annotation of the diagnosis as given in [5] there are some disadvantages.…”

Section: H4011 Primary Open-angle Glaucomamentioning

confidence: 99%

Conceptual Graph Representation Framework for ICD-10

Hema¹,

Selwyn²

2015

Procedia Computer Science

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Codification and representation of knowledge from structured and unstructured repositories have been the requirement for Expert Systems designing. Structured repositories remain open for knowledge extraction and representation for building a knowledgebase (KB). ICD-v10 (International Classification of Diseases), a structured repository, is the 10 th revision of the collection of Disease codes and health related problems, symptoms, complaints and external causes of injury. ICD-v10 uses a 7-bit code to represent all diseases. The main focus of our research work is to propose new methods for capturing and representing these codes in the forms of Knowledge from Semantic Structured Repository with better materialization, quality and repository usage, along with logical unit and logical entailments to form conceptual relations among them. Apart from creating a new representation structure, this work gives direction on the storage of these structures as Knowledge Unit (KU).

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GALEN based formal representation of ICD10

Cited by 16 publications

References 4 publications

An iconic language for the graphical representation of medical concepts

An iconic language for the graphical representation of medical concepts

Views of diagnosis distribution in primary care in 2.5 million encounters in Stockholm: a comparison between ICD-10 and SNOMED CT

Conceptual Graph Representation Framework for ICD-10

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