Enriching a primary health care version of ICD-10 using SNOMED CT mapping.
Ontology highlight
ABSTRACT: 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.
SUBMITTER: Nystrom M
PROVIDER: S-EPMC2908062 | biostudies-literature | 2010 Jun
REPOSITORIES: biostudies-literature
Publications
Enriching a primary health care version of ICD-10 using SNOMED CT mapping.
Nyström Mikael M Vikström Anna A Nilsson Gunnar H GH Ahlfeldt Hans H Orman Håkan H
Journal of biomedical semantics 20100617 1
<h4>Background</h4>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.<h4>Results</h4>The mappings are manually ev ...[more]
Similar Datasets
Project description:BackgroundData harmonisation is essential in real-world data (RWD) research projects based on hospital information systems databases, as coding systems differ between countries. The Hungarian hospital information systems and the national claims database use internationally known diagnosis codes, but data on medical procedures are recorded using national codes. There is no simple or standard solution for mapping the national codes to a standard coding system. Our aim was to map the Hungarian procedure codes (OENO) to SNOMED CT as part of the European Health Data Evidence Network (EHDEN) project.MethodsWe recruited 25 professionals from different specialties to manually map the procedure codes used between 2011 and 2021. A mapping protocol and training material were developed, results were regularly revised, and the challenges of mapping were recorded. Approximately 7% of the codes were mapped by more people in different specialties for validation purposes.ResultsWe mapped 4661 OENO codes to standard vocabularies, mostly SNOMED CT. We categorized the challenges into three main areas: semantic, matching, and methodological. Semantic refers to the occasionally unclear meaning of the OENO codes, matching to the different granularity and purpose of the OENO and SNOMED CT vocabularies. Lastly, methodological challenges were used to describe issues related to the design of the above-mentioned two vocabularies.ConclusionsThe challenges and solutions presented here may help other researchers to design their process to map their national codes to standard vocabularies in order to achieve greater consistency in mapping results. Moreover, we believe that our work will allow for better use of RWD collected in Hungary in international research collaborations.
| S-EPMC10585817 | biostudies-literature
Project description:The search for novel metabolic biomarkers is intense but has had limited practical outcomes for medicine. Part of the problem is that we lack knowledge of how different comorbidities influence biomarkers' performance. In this study, 49 metabolites were measured by targeted LC/MS protocols in the serum of 1011 volunteers. Their performance as potential biomarkers was evaluated by the area under the curve of receiver operator characteristics (AUC-ROC) for 105 diagnosis codes or code groups from the 10th revision of the international classification of diseases (ICD-10). Additionally, the interferences between diagnosis codes were investigated. The highest AUC-ROC values for individual metabolites and ICD-10 code combinations reached a moderate (0.7) range. Most metabolites that were found to be potential markers remained so independently of the control group composition or comorbidities. The precise value of the AUC-ROC, however, could vary depending on the comorbidities. Moreover, networks of metabolite and disease associations were built in order to map diseases, which may interfere with metabolic biomarker research on other diseases.
| S-EPMC7281140 | biostudies-literature
Project description:BACKGROUND:The phecode system was built upon the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for phenome-wide association studies (PheWAS) using the electronic health record (EHR). OBJECTIVE:The goal of this paper was to develop and perform an initial evaluation of maps from the International Classification of Diseases, 10th Revision (ICD-10) and the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes to phecodes. METHODS:We mapped ICD-10 and ICD-10-CM codes to phecodes using a number of methods and resources, such as concept relationships and explicit mappings from the Centers for Medicare & Medicaid Services, the Unified Medical Language System, Observational Health Data Sciences and Informatics, Systematized Nomenclature of Medicine-Clinical Terms, and the National Library of Medicine. We assessed the coverage of the maps in two databases: Vanderbilt University Medical Center (VUMC) using ICD-10-CM and the UK Biobank (UKBB) using ICD-10. We assessed the fidelity of the ICD-10-CM map in comparison to the gold-standard ICD-9-CM phecode map by investigating phenotype reproducibility and conducting a PheWAS. RESULTS:We mapped >75% of ICD-10 and ICD-10-CM codes to phecodes. Of the unique codes observed in the UKBB (ICD-10) and VUMC (ICD-10-CM) cohorts, >90% were mapped to phecodes. We observed 70-75% reproducibility for chronic diseases and <10% for an acute disease for phenotypes sourced from the ICD-10-CM phecode map. Using the ICD-9-CM and ICD-10-CM maps, we conducted a PheWAS with a Lipoprotein(a) genetic variant, rs10455872, which replicated two known genotype-phenotype associations with similar effect sizes: coronary atherosclerosis (ICD-9-CM: P<.001; odds ratio (OR) 1.60 [95% CI 1.43-1.80] vs ICD-10-CM: P<.001; OR 1.60 [95% CI 1.43-1.80]) and chronic ischemic heart disease (ICD-9-CM: P<.001; OR 1.56 [95% CI 1.35-1.79] vs ICD-10-CM: P<.001; OR 1.47 [95% CI 1.22-1.77]). CONCLUSIONS:This study introduces the beta versions of ICD-10 and ICD-10-CM to phecode maps that enable researchers to leverage accumulated ICD-10 and ICD-10-CM data for PheWAS in the EHR.
| S-EPMC6911227 | biostudies-literature
Project description:ObjectiveTo identify challenges in mapping internal International Classification of Disease, 9th edition, Clinical Modification (ICD-9-CM) encoded legacy data to Systematic Nomenclature of Medicine (SNOMED), using SNOMED-prescribed compositional approaches where appropriate, and to explore the mapping coverage provided by the US National Library of Medicine (NLM)'s SNOMED clinical core subset.DesignThis study selected ICD-CM codes that occurred at least 100 times in the organization's problem list or diagnosis data in 2008. After eliminating codes whose exact mappings were already available in UMLS, the remainder were mapped manually with software assistance.ResultsOf the 2194 codes, 784 (35.7%) required manual mapping. 435 of these represented concept types documented in SNOMED as deprecated: these included the qualifying phrases such as 'not elsewhere classified'. A third of the codes were composite, requiring multiple SNOMED code to map. Representing 45 composite concepts required introducing disjunction ('or') or set-difference ('without') operators, which are not currently defined in SNOMED. Only 47% of the concepts required for composition were present in the clinical core subset. Search of SNOMED for the correct concepts often required extensive application of knowledge of both English and medical synonymy.ConclusionStrategies to deal with legacy ICD data must address the issue of codes created by non-taxonomist users. The NLM core subset possibly needs augmentation with concepts from certain SNOMED hierarchies, notably qualifiers, body structures, substances/products and organisms. Concept-matching software needs to utilize query expansion strategies, but these may be effective in production settings only if a large but non-redundant SNOMED subset that minimizes the proportion of extensively pre-coordinated concepts is also available.
| S-EPMC2995664 | biostudies-literature
Project description:Periodic revisions of the international classification of diseases (ICD) ensure that the classification reflects new practices and knowledge; however, this complicates retrospective research as diagnoses are coded in different versions. For longitudinal disease trajectory studies, a crosswalk is an essential tool and a comprehensive mapping between ICD-8 and ICD-10 has until now been lacking. In this study, we map all ICD-8 morbidity codes to ICD-10 in the expanded Danish ICD version. We mapped ICD-8 codes to ICD-10, using a many-to-one system inspired by general equivalence mappings such that each ICD-8 code maps to a single ICD-10 code. Each ICD-8 code was manually and unidirectionally mapped to a single ICD-10 code based on medical setting and context. Each match was assigned a score (1 of 4 levels) reflecting the quality of the match and, if applicable, a "flag" signalling choices made in the mapping. We provide the first complete mapping of the 8596 ICD-8 morbidity codes to ICD-10 codes. All Danish ICD-8 codes representing diseases were mapped and 5106 (59.4%) achieved the highest consistency score. Only 334 (3.9%) of the ICD-8 codes received the lowest mapping consistency score. The mapping provides a scaffold for translation of ICD-8 to ICD-10, which enable longitudinal disease studies back to and 1969 in Denmark and to 1965 internationally with further adaption.
| S-EPMC10570238 | biostudies-literature
Project description:BackgroundInteroperability and secondary use of data is a challenge in health care. Specifically, the reuse of clinical free text remains an unresolved problem. The Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) has become the universal language of health care and presents characteristics of a natural language. Its use to represent clinical free text could constitute a solution to improve interoperability.ObjectiveAlthough the use of SNOMED and SNOMED CT has already been reviewed, its specific use in processing and representing unstructured data such as clinical free text has not. This review aims to better understand SNOMED CT's use for representing free text in medicine.MethodsA scoping review was performed on the topic by searching MEDLINE, Embase, and Web of Science for publications featuring free-text processing and SNOMED CT. A recursive reference review was conducted to broaden the scope of research. The review covered the type of processed data, the targeted language, the goal of the terminology binding, the method used and, when appropriate, the specific software used.ResultsIn total, 76 publications were selected for an extensive study. The language targeted by publications was 91% (n=69) English. The most frequent types of documents for which the terminology was used are complementary exam reports (n=18, 24%) and narrative notes (n=16, 21%). Mapping to SNOMED CT was the final goal of the research in 21% (n=16) of publications and a part of the final goal in 33% (n=25). The main objectives of mapping are information extraction (n=44, 39%), feature in a classification task (n=26, 23%), and data normalization (n=23, 20%). The method used was rule-based in 70% (n=53) of publications, hybrid in 11% (n=8), and machine learning in 5% (n=4). In total, 12 different software packages were used to map text to SNOMED CT concepts, the most frequent being Medtex, Mayo Clinic Vocabulary Server, and Medical Text Extraction Reasoning and Mapping System. Full terminology was used in 64% (n=49) of publications, whereas only a subset was used in 30% (n=23) of publications. Postcoordination was proposed in 17% (n=13) of publications, and only 5% (n=4) of publications specifically mentioned the use of the compositional grammar.ConclusionsSNOMED CT has been largely used to represent free-text data, most frequently with rule-based approaches, in English. However, currently, there is no easy solution for mapping free text to this terminology and to perform automatic postcoordination. Most solutions conceive SNOMED CT as a simple terminology rather than as a compositional bag of ontologies. Since 2012, the number of publications on this subject per year has decreased. However, the need for formal semantic representation of free text in health care is high, and automatic encoding into a compositional ontology could be a solution.
| S-EPMC7872838 | biostudies-literature
Project description:MotivationIn this paper we demonstrate the usage of RIO; a framework for detecting syntactic regularities using cluster analysis of the entities in the signature of an ontology. Quality assurance in ontologies is vital for their use in real applications, as well as a complex and difficult task. It is also important to have such methods and tools when the ontology lacks documentation and the user cannot consult the ontology developers to understand its construction. One aspect of quality assurance is checking how well an ontology complies with established 'coding standards'; is the ontology regular in how descriptions of different types of entities are axiomatised? Is there a similar way to describe them and are there any corner cases that are not covered by a pattern? Detection of regularities and irregularities in axiom patterns should provide ontology authors and quality inspectors with a level of abstraction such that compliance to coding standards can be automated. However, there is a lack of such reverse ontology engineering methods and tools.ResultsRIO framework allows regularities to be detected in an OWL ontology, i.e. repetitive structures in the axioms of an ontology. We describe the use of standard machine learning approaches to make clusters of similar entities and generalise over their axioms to find regularities. This abstraction allows matches to, and deviations from, an ontology's patterns to be shown. We demonstrate its usage with the inspection of three modules from SNOMED-CT, a large medical terminology, that cover "Present" and "Absent" findings, as well as "Chronic" and "Acute" findings. The module sizes are 5 065, 20 688 and 19 812 asserted axioms. They are analysed in terms of their types and number of regularities and irregularities in the asserted axioms of the ontology. The analysis showed that some modules of the terminology, which were expected to instantiate a pattern described in the SNOMED-CT technical guide, were found to have a high number of regularity deviations. A subset of these were categorised as "design defects" by verifying them with past work on the quality assurance of SNOMED-CT. These were mainly incomplete descriptions. In the worst case, the expected patterns described in the technical guide were followed by only 5% of the axioms in the module.ConclusionIt is possible to automatically detect regularities and then inspect irregularities in an ontology. We argue that RIO is a tool to find and report such matches and mismatches, for evaluations by the domain experts. We have demonstrated that standard clustering techniques from machine learning can offer a tool in the drive for quality assurance in ontologies.Availabilityhttp://riotool.sourceforge.net/Contacthttp://eleni.mikroyannidi@manchester.ac.uk, http://robert.stevens@manchehster.ac.uk.
| S-EPMC3637289 | biostudies-literature
Project description:BACKGROUND:Defining clinical conditions from electronic health record (EHR) data underpins population health activities, clinical decision support, and analytics. In an EHR, defining a condition commonly employs a diagnosis value set or "grouper." For constructing value sets, Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) offers high clinical fidelity, a hierarchical ontology, and wide implementation in EHRs as the standard interoperability vocabulary for problems. OBJECTIVE:This article demonstrates a practical approach to defining conditions with combinations of SNOMED CT concept hierarchies, and evaluates sharing of definitions for clinical and analytic uses. METHODS:We constructed diagnosis value sets for EHR patient registries using SNOMED CT concept hierarchies combined with Boolean logic, and shared them for clinical decision support, reporting, and analytic purposes. RESULTS:A total of 125 condition-defining "standard" SNOMED CT diagnosis value sets were created within our EHR. The median number of SNOMED CT concept hierarchies needed was only 2 (25th-75th percentiles: 1-5). Each value set, when compiled as an EHR diagnosis grouper, was associated with a median of 22 International Classification of Diseases (ICD)-9 and ICD-10 codes (25th-75th percentiles: 8-85) and yielded a median of 155 clinical terms available for selection by clinicians in the EHR (25th-75th percentiles: 63-976). Sharing of standard groupers for population health, clinical decision support, and analytic uses was high, including 57 patient registries (with 362 uses of standard groupers), 132 clinical decision support records, 190 rules, 124 EHR reports, 125 diagnosis dimension slicers for self-service analytics, and 111 clinical quality measure calculations. Identical SNOMED CT definitions were created in an EHR-agnostic tool enabling application across disparate organizations and EHRs. CONCLUSION:SNOMED CT-based diagnosis value sets are simple to develop, concise, understandable to clinicians, useful in the EHR and for analytics, and shareable. Developing curated SNOMED CT hierarchy-based condition definitions for public use could accelerate cross-organizational population health efforts, "smarter" EHR feature configuration, and clinical-translational research employing EHR-derived data.
| S-EPMC6115233 | biostudies-literature
Project description:OBJECTIVE: The aim of this paper is to report on the use of the systematised nomenclature of medicine clinical terms (SNOMED CT) by providing an overview of published papers. METHODS: Published papers on SNOMED CT between 2001 and 2012 were identified using PubMed and Embase databases using the keywords 'systematised nomenclature of medicine' and 'SNOMED CT'. For each paper the following characteristics were retrieved: SNOMED CT focus category (ie, indeterminate, theoretical, pre-development/design, implementation and evaluation/commodity), usage category (eg, prospective content coverage, used to classify or code in a study), medical domain and country. RESULTS: Our search strategy identified 488 papers. A comparison between the papers published between 2001-6 and 2007-12 showed an increase in every SNOMED CT focus category. The number of papers classified as 'theoretical' increased from 46 to 78, 'pre-development/design' increased from 61 to 173 and 'implementation' increased from 10 to 34. Papers classified as 'evaluation/commodity' only started to appear from 2010. CONCLUSIONS: The majority of studies focused on 'theoretical' and 'pre-development/design'. This is still encouraging as SNOMED CT is being harmonized with other standardized terminologies and is being evaluated to determine the content coverage of local terms, which is usually one of the first steps towards adoption. Most implementations are not published in the scientific literature, requiring a look beyond the scientific literature to gain insights into SNOMED CT implementations.
| S-EPMC3957381 | biostudies-other
Project description:ObjectivePatient phenotype definitions based on terminologies are required for the computational use of electronic health records. Within UK primary care research databases, such definitions have typically been represented as flat lists of Read terms, but Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) (a widely employed international reference terminology) enables the use of relationships between concepts, which could facilitate the phenotyping process. We implemented SNOMED CT-based phenotyping approaches and investigated their performance in the CPRD Aurum primary care database.Materials and methodsWe developed SNOMED CT phenotype definitions for 3 exemplar diseases: diabetes mellitus, asthma, and heart failure, using 3 methods: "primary" (primary concept and its descendants), "extended" (primary concept, descendants, and additional relations), and "value set" (based on text searches of term descriptions). We also derived SNOMED CT codelists in a semiautomated manner for 276 disease phenotypes used in a study of health across the lifecourse. Cohorts selected using each codelist were compared to "gold standard" manually curated Read codelists in a sample of 500 000 patients from CPRD Aurum.ResultsSNOMED CT codelists selected a similar set of patients to Read, with F1 scores exceeding 0.93, and age and sex distributions were similar. The "value set" and "extended" codelists had slightly greater recall but lower precision than "primary" codelists. We were able to represent 257 of the 276 phenotypes by a single concept hierarchy, and for 135 phenotypes, the F1 score was greater than 0.9.ConclusionsSNOMED CT provides an efficient way to define disease phenotypes, resulting in similar patient populations to manually curated codelists.
| S-EPMC9846670 | biostudies-literature