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Enrichment of Terminology Systems for Use and Reuse in Medical Information Systems
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.ORCID iD: 0000-0001-6468-2432
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Electronic health record systems (EHR) are used to store relevant heath facts about patients. The main use of the EHR is in the care of the patient, but an additional use is to reuse the EHR information to locate and evaluate clinical evidence for treatments. To efficiently use the EHR information it is essential to use appropriate methods for information compilations. This thesis deals with use of information in medical terminology systems and ontologies to be able to better use and reuse EHR information and other medical information.

The first objective of the thesis is to examine if word alignment on bilingual English-Swedish rubrics from five medical terminology systems can be used to build a bilingual dictionary. A study found that it was possible to generate a dictionary with 42 000 entries containing a high proportion of medical entries using word alignment. The method worked best using sets of rubrics with many unique words that are consistently translated. The dictionary can be used as a general medical dictionary, for use in semi-automatic translation methods, for use in cross-language information retrieval systems, and for enrichment of other terminology systems.

The second objective of the thesis is to explore how connections from existing terminology systems and information models to SNOMED CT and the structure in SNOMED CT can be used to reuse information. A study examined whether the primary health care diagnose terminology system KSH97-P can obtain a richer structure using category and chapter mappings from KSH97-P to SNOMED CT and the structure in SNOMED CT. The study showed that KSH97-P can be enriched with a poly-hierarchical chapter division and additional attributes. The richer structure was used to compile statistics in new manners that showed new views of the primary care diagnoses. A literature study evaluated which kinds of information compilations those are necessary to create graphical patient overviews based on information from EHRs. It was found that a third of the patient overviews can have their information needs satisfied using compilations based on SNOMED CT encodings of the information entities in the EHR and the structure in SNOMED CT. The other overviews also need access to individual values in the EHR. This can be achieved by using well-defined information models in the EHR.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press , 2010. , 79 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1335
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:liu:diva-58621ISBN: 978-91-7393-328-5 (print)OAI: oai:DiVA.org:liu-58621DiVA: diva2:344339
Public defence
2010-09-10, Eken, Campus US, Linköpings universitet, Linköping, 09:00 (English)
Opponent
Supervisors
Available from: 2010-08-30 Created: 2010-08-18 Last updated: 2015-09-22Bibliographically approved
List of papers
1. Creating a medical English-Swedish dictionary using interactive word alignment
Open this publication in new window or tab >>Creating a medical English-Swedish dictionary using interactive word alignment
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2006 (English)In: BMC Medical Informatics and Decision Making, ISSN 1472-6947, E-ISSN 1472-6947, Vol. 6, no 35Article in journal (Refereed) Published
Abstract [en]

Background: This paper reports on a parallel collection of rubrics from the medical terminology systems ICD-10, ICF, MeSH, NCSP and KSH97-P and its use for semi-automatic creation of an English-Swedish dictionary of medical terminology. The methods presented are relevant for many other West European language pairs than English-Swedish. Methods: The medical terminology systems were collected in electronic format in both English and Swedish and the rubrics were extracted in parallel language pairs. Initially, interactive word alignment was used to create training data from a sample. Then the training data were utilised in automatic word alignment in order to generate candidate term pairs. The last step was manual verification of the term pair candidates. Results: A dictionary of 31,000 verified entries has been created in less than three man weeks, thus with considerably less time and effort needed compared to a manual approach, and without compromising quality. As a side effect of our work we found 40 different translation problems in the terminology systems and these results indicate the power of the method for finding inconsistencies in terminology translations. We also report on some factors that may contribute to making the process of dictionary creation with similar tools even more expedient. Finally, the contribution is discussed in relation to other ongoing efforts in constructing medical lexicons for non-English languages. Conclusion: In three man weeks we were able to produce a medical English-Swedish dictionary consisting of 31,000 entries and also found hidden translation errors in the utilized medical terminology systems. © 2006 Nyström et al, licensee BioMed Central Ltd.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-35769 (URN)10.1186/1472-6947-6-35 (DOI)28502 (Local ID)28502 (Archive number)28502 (OAI)
Note
Original Publication: Mikael Nyström, Magnus Merkel, Lars Ahrenberg, Pierre Zweigenbaum, Håkan Petersson and Hans Åhlfeldt, Creating a medical English-Swedish dictionary using interactive word alignment, 2006, BMC Medical Informatics and Decision Making, (6), 35. http://dx.doi.org/10.1186/1472-6947-6-35 Licensee: BioMed Central http://www.biomedcentral.com/ Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2017-12-13
2. Creating a medical dictionary using word alignment: The influence of sources and resources
Open this publication in new window or tab >>Creating a medical dictionary using word alignment: The influence of sources and resources
2007 (English)In: BMC Medical Informatics and Decision Making, ISSN 1472-6947, E-ISSN 1472-6947, Vol. 7, no 37Article in journal (Refereed) Published
Abstract [en]

Background. Automatic word alignment of parallel texts with the same content in different languages is among other things used to generate dictionaries for new translations. The quality of the generated word alignment depends on the quality of the input resources. In this paper we report on automatic word alignment of the English and Swedish versions of the medical terminology systems ICD-10, ICF, NCSP, KSH97-P and parts of MeSH and how the terminology systems and type of resources influence the quality. Methods. We automatically word aligned the terminology systems using static resources, like dictionaries, statistical resources, like statistically derived dictionaries, and training resources, which were generated from manual word alignment. We varied which part of the terminology systems that we used to generate the resources, which parts that we word aligned and which types of resources we used in the alignment process to explore the influence the different terminology systems and resources have on the recall and precision. After the analysis, we used the best configuration of the automatic word alignment for generation of candidate term pairs. We then manually verified the candidate term pairs and included the correct pairs in an English-Swedish dictionary. Results. The results indicate that more resources and resource types give better results but the size of the parts used to generate the resources only partly affects the quality. The most generally useful resources were generated from ICD-10 and resources generated from MeSH were not as general as other resources. Systematic inter-language differences in the structure of the terminology system rubrics make the rubrics harder to align. Manually created training resources give nearly as good results as a union of static resources, statistical resources and training resources and noticeably better results than a union of static resources and statistical resources. The verified English-Swedish dictionary contains 24,000 term pairs in base forms. Conclusion. More resources give better results in the automatic word alignment, but some resources only give small improvements. The most important type of resource is training and the most general resources were generated from ICD-10. © 2007 Nyström et al, licensee BioMed Central Ltd.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-40825 (URN)10.1186/1472-6947-7-37 (DOI)54255 (Local ID)54255 (Archive number)54255 (OAI)
Note
Original Publication: Mikael Nyström, Magnus Merkel, Håkan Petersson and Hans Åhlfeldt, Creating a medical dictionary using word alignment: The influence of sources and resources, 2007, BMC Medical Informatics and Decision Making, (7), 37. http://dx.doi.org/10.1186/1472-6947-7-37 Licensee: BioMed Central http://www.biomedcentral.com/ Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2017-12-13
3. Enriching a primary health care version of ICD-10 using SNOMED CT mapping
Open this publication in new window or tab >>Enriching a primary health care version of ICD-10 using SNOMED CT mapping
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2010 (English)In: Journal of Biomedical Semantics, ISSN 2041-1480, Vol. 1, no 7Article in journal (Refereed) Published
Abstract [en]

Background: In order to satisfy different needs, medical terminology systems musthave richer structures. This study examines whether a Swedish primary health careversion of the mono-hierarchical ICD-10 (KSH97-P) may obtain a richer structureusing category and chapter mappings from KSH97-P to SNOMED CT and SNOMEDCT’s structure. Manually-built mappings from KSH97-P’s categories and chapters toSNOMED CT’s concepts are used as a starting point

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

Conclusions: It is possible to use mappings from KSH97-P to SNOMED CT andSNOMED CT’s structure to enrich KSH97-P’s mono-hierarchical structure with a polyhierarchicalchapter division and attributes of type Finding site and Associatedmorphology. The final mappings are available as additional files for this paper.

Place, publisher, year, edition, pages
London, United Kingdom: BioMed Central, 2010
National Category
Medical and Health Sciences Computer and Information Science
Identifiers
urn:nbn:se:liu:diva-58030 (URN)10.1186/2041-1480-1-7 (DOI)20618919 (PubMedID)
Available from: 2010-07-21 Created: 2010-07-21 Last updated: 2014-11-13
4. Views of diagnosis distribution in primary care in 2.5 million encounters in Stockholm: a comparison between ICD-10 and SNOMED CT
Open this publication in new window or tab >>Views of diagnosis distribution in primary care in 2.5 million encounters in Stockholm: a comparison between ICD-10 and SNOMED CT
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2010 (English)In: Informatics in Primary Care, ISSN 1476-0320, E-ISSN 1475-9985, Vol. 18, no 1, 17-29 p.Article in journal (Refereed) Published
Abstract [en]

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. Methods We used diagnostic data collected throughout 2006 and coded in electronic patient records (EPRs) and a mapping from KSH97-P/ICD-10 to SNOMED CT to aggregate the diagnostic data with SNOMED CT defining hierarchical relationship Is a and selected attribute relationships. Results The chapter level comparison between ICD-10 and SNOMED CT showed minor differences except for infectious and digestive system disorders. The relationships chosen aggregated the diagnostic data to 2861 concepts showing a multidimensional view on different medical and specific levels and also including clinically relevant characteristics through attribute relationships. Conclusions SNOMED CT provides a different view of diagnoses and health problems on a chapter level and adds significant new views of the clinical data with aggregations generated from SNOMED CT Is a and attribute relationships. A broader use of SNOMED CT is therefore of importance when describing and developing primary care. © 2010 PHCSG, British Computer Society.

Place, publisher, year, edition, pages
The British Computer Society, 2010
Keyword
Classification; Diagnosis; ICD-10; Medical records systems computerised; Primary care; SNOMED CT
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-57019 (URN)20429975 (PubMedID)
Available from: 2010-06-14 Created: 2010-06-09 Last updated: 2017-12-12
5. Data Needs for Patient Overviews: A Literature ReviewCompared with SNOMED CT and openEHR
Open this publication in new window or tab >>Data Needs for Patient Overviews: A Literature ReviewCompared with SNOMED CT and openEHR
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Patient overviews automatically generated fromelectronic healthcare data have different data needsdepending on their complexity. A literature reviewbased on a broad MEDLINE search found 16 suchoverviews for which the data needs were analyzedand compared with features provided bySNOMED CT and openEHR. Five systems used onlyinformation type, while five systems also presentedparticular values from its information entities. Sixsystems also aggregated or filtered the information.In addition to that, two systems provided referenceranges and three systems provided more advanceddecision support. The simple data needs can be metusing information entity markups based onSNOMED CT and SNOMED CT relationships. Morecomplex data needs can be satisfied using theopenEHR reference model and archetypes tostructure data and the archetype query language toretrieve individual data values. The most advancedoverviews also need additional methods foraggregation, filtering and connection to knowledgerepresentation.

National Category
Medical and Health Sciences Computer and Information Science
Identifiers
urn:nbn:se:liu:diva-58615 (URN)
Available from: 2010-08-18 Created: 2010-08-18 Last updated: 2015-09-22

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