GALEN based formal representation of ICD10

Upper-level Ontologies for Health Information Systems

Objectives: We examine the potential of archetype patterns for upper-level ontology development in health information systems (HISs).

Methods: Archetype patterns, based on the integration of archetype concepts and design patterns, are conceptualized and developed for ontology in the HIS domain. The UML provides the underlying modeling support.

Results: We argue in favor of the archetype pattern providing models for upper-level ontologies in HIS. This, in turn, has the potential to offer a foundation for interoperability across HIS applications and enterprises. Our research also shows the limitations of current ontology development methods as well as the challenges faced in archetype pattern development. The framework has potential for general, widespread usage. Overall, this approach supports ontology development in HIS.

Conclusions: The research demonstrates the applicability of archetype patterns to ontology development in HIS. While numerous ontologies exist in biomedicine, there are few well-developed ontologies for general healthcare. Properly modeled, archetype patterns have potential to reconcile the differences in high-level design views across health care inter-enterprises. Future research can focus on governance, standards and tools for archetype patterns, as well as development of a comprehensive set of high-level healthcare archetype patterns.

Dione: An OWL representation of ICD-10-CM for classifying patients' diseases

Background

Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) has been designed as standard clinical terminology for annotating Electronic Health Records (EHRs). EHRs textual information is used to classify patients’ diseases into an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) category (usually by an expert). Improving the accuracy of classification is the main purpose of using ontologies and OWL representations at the core of classification systems. In the last few years some ontologies and OWL representations for representing ICD-10-CM categories have been developed. However, they were not designed to be the basis for an automatic classification tool nor do they model ICD-10-CM inclusion terms as Web Ontology Language (OWL) axioms, which enables automatic classification. In this context we have developed Dione, an OWL representation of ICD-10-CM.

Results

Dione is the first OWL representation of ICD-10-CM, which is logically consistent, whose axioms define the ICD-10-CM inclusion terms by means of a methodology based on SNOMED CT/ICD-10-CM mappings. The ICD-10-CM exclusions are handled with these mappings. Dione currently contains 391,669 classes, 391,720 entity annotation axioms and 11,795 owl:equivalentClass axioms which have been constructed using 104,646 relationships extracted from the SNOMED CT/ICD-10-CM and BioPortal mappings included in Dione using the owl:intersectionOf and the owl:someValuesFrom statements. The resulting OWL representation has been classified and its consistency tested with the ELK reasoner. We have also taken three clinical records from the Virgen de la Victoria Hospital (Málaga, Spain) which have been manually annotated using SNOMED CT. These annotations have been included as instances to be classified by the reasoner. The classified instances show that Dione could be a promising ICD-10-CM OWL representation to support the classification of patients’ diseases.

Conclusions

Dione is a first step towards the automatic classification of patients’ diseases by using SNOMED CT annotations embedded in Electronic Health Records (EHRs). The purpose of Dione is to standardise and formalise a medical terminology, thereby enabling new kinds of tools and new sets of functionalities to be developed. This in turn assists health specialists by providing classified information from EHRs and enables the automatic annotation of patients’ diseases with ICD-10-CM codes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13326-016-0105-x) contains supplementary material, which is available to authorized users.

Combining Bayesian Text Classification and Shrinkage to Automate Healthcare Coding

This article analyzes the data quality issues that emerge when training a shrinkage-based classifier with noisy data. A statistical text analysis technique based on a shrinkage-based variation of multinomial naive Bayes is applied to a set of free-text discharge diagnoses occurring in a number of hospitalizations. All of these diagnoses were previously coded according to the Spanish Edition of ICD9-CM. We deal with the issue of analyzing the predictive power and robustness of the statistical machine learning algorithm proposed for ICD-9-CM classification. We explore the effect of training the models using both clean and noisy data. In particular our work investigates the extent to which errors in free-text diagnoses propagate to the classification model. A measure of predictive accuracy is calculated for the text classification algorithm under analysis. Subsequently, the quality of the sample data is incrementally deteriorated by simulating errors in the text and/or codes. The predictive accuracy is recomputed for each of the noisy samples for comparison purposes. Our research shows that the shrinkage-based classifier is a valid alternative to automate ICD9-CM coding even under circumstances in which the quality of the training data is in question.

Enriching a primary health care version of ICD-10 using SNOMED CT mapping

BACKGROUND

In order to satisfy different needs, medical terminology systems must have richer structures. This study examines whether a Swedish primary health care version of the mono-hierarchical ICD-10 (KSH97-P) may obtain a richer structure using category and chapter mappings from KSH97-P to SNOMED CT and SNOMED CT's structure. Manually-built mappings from KSH97-P's categories and chapters to SNOMED CT's concepts are used as a starting point.

RESULTS

The mappings are manually evaluated using computer-produced information and a small number of mappings are updated. A new and poly-hierarchical chapter division of KSH97-P's categories has been created using the category and chapter mappings and SNOMED CT's generic structure. In the new chapter division, most categories are included in their original chapters. A considerable number of concepts are included in other chapters than their original chapters. Most of these inclusions can be explained by ICD-10's design. KSH97-P's categories are also extended with attributes using the category mappings and SNOMED CT's defining attribute relationships. About three-fourths of all concepts receive an attribute of type Finding site and about half of all concepts receive an attribute of type Associated morphology. Other types of attributes are less common.

CONCLUSIONS

It is possible to use mappings from KSH97-P to SNOMED CT and SNOMED CT's structure to enrich KSH97-P's mono-hierarchical structure with a poly-hierarchical chapter division and attributes of type Finding site and Associated morphology. The final mappings are available as additional files for this paper.

Alignment of the UMLS semantic network with BioTop: methodology and assessment

MOTIVATION

For many years, the Unified Medical Language System (UMLS) semantic network (SN) has been used as an upper-level semantic framework for the categorization of terms from terminological resources in biomedicine. BioTop has recently been developed as an upper-level ontology for the biomedical domain. In contrast to the SN, it is founded upon strict ontological principles, using OWL DL as a formal representation language, which has become standard in the semantic Web. In order to make logic-based reasoning available for the resources annotated or categorized with the SN, a mapping ontology was developed aligning the SN with BioTop.

METHODS

The theoretical foundations and the practical realization of the alignment are being described, with a focus on the design decisions taken, the problems encountered and the adaptations of BioTop that became necessary. For evaluation purposes, UMLS concept pairs obtained from MEDLINE abstracts by a named entity recognition system were tested for possible semantic relationships. Furthermore, all semantic-type combinations that occur in the UMLS Metathesaurus were checked for satisfiability.

RESULTS

The effort-intensive alignment process required major design changes and enhancements of BioTop and brought up several design errors that could be fixed. A comparison between a human curator and the ontology yielded only a low agreement. Ontology reasoning was also used to successfully identify 133 inconsistent semantic-type combinations.

AVAILABILITY

BioTop, the OWL DL representation of the UMLS SN, and the mapping ontology are available at http://www.purl.org/biotop/.

An iconic language for the graphical representation of medical concepts

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.

Methods

The VCM graphical language was designed using a small number of graphical primitives and combinatory rules. VCM was evaluated over 11 volunteer general practitioners to assess if the language is easy to learn, to understand and to use. Evaluators were asked to register their VCM training time, to indicate the meaning of VCM icons and sentences, and to answer clinical questions related to randomly generated drug monograph-like documents, supplied in text or VCM format.

Results

VCM can represent the various signs, diseases, physiological states, life habits, drugs and tests described in drug monographs. Grammatical rules make it possible to generate many icons by combining a small number of primitives and reusing simple icons to build more complex ones. Icons can be organized into simple sentences to express drug recommendations. Evaluation showed that VCM was learnt in 2 to 7 hours, that physicians understood 89% of the tested VCM icons, and that they answered correctly to 94% of questions using VCM (versus 88% using text, p = 0.003) and 1.8 times faster (p < 0.001).

Conclusion

VCM can be learnt in a few hours and appears to be easy to read. It can now be used in a second step: the design of graphical interfaces facilitating access to drug monographs. It could also be used for broader applications, including the design of interfaces for consulting other types of medical document or medical data, or, very simply, to enrich medical texts.