Ontology-Based Clinical Information Extraction Using SNOMED CT

Extracting and encoding clinical information captured in unstructured clinical documents with standard medical terminologies is vital to enable secondary use of clinical data from practice. SNOMED CT is the most comprehensive medical ontology with broad types of concepts and detailed relationships and it has been widely used for many clinical applications. However, few studies have investigated the use of SNOMED CT in clinical information extraction. In this dissertation research, we developed a fine-grained information model based on the SNOMED CT and built novel information extraction systems to recognize clinical entities and identify their relations, as well as to encode them to SNOMED CT concepts. Our evaluation shows that such ontology-based information extraction systems using SNOMED CT could achieve state-of-the-art performance, indicating its potential in clinical natural language processing

Translating and evaluating historic phenotyping algorithms using SNOMED CT

OBJECTIVE: Patient 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 METHODS: We 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. RESULTS: SNOMED 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. CONCLUSIONS: SNOMED CT provides an efficient way to define disease phenotypes, resulting in similar patient populations to manually curated codelists

Facilitating pre-operative assessment guidelines representation using SNOMED CT

Objective: To investigate whether SNOMED CT covers the terms used in pre-operative assessment guidelines, and if necessary, how the measured content coverage can be improved. Pre-operative assessment guidelines were retrieved from the websites of (inter)national anesthesiarelated societies. The recommendations in the guidelines were rewritten to ‘‘IF condition THEN action” statements to facilitate data extraction. Terms were extracted from the IF–THEN statements and mapped to SNOMED CT. Content coverage was measured by using three scores: no match, partial match and complete match. Non-covered concepts were evaluated against the SNOMED CT editorial documentation. Results: From 6 guidelines, 133 terms were extracted, of which 71% (n = 94) completely matched with SNOMED CT concepts. Disregarding the vague concepts in the included guidelines SNOMED CT’s content coverage was 89%. Of the 39 non-completely covered concepts, 69% violated at least one of SNOMED CT’s editorial principles or rules. These concepts were categorized based on four categories: non-reproducibility, classification-derived phrases, numeric ranges, and procedures categorized by complexity. Conclusion: Guidelines include vague terms that cannot be well supported by terminological systems thereby hampering guideline-based decision support systems. This vagueness reduces the content coverage of SNOMED CT in representing concepts used in the pre-operative assessment guidelines. Formalization of the guidelines using SNOMED CT is feasible but to optimize this, first the vagueness of some guideline concepts should be resolved and a few currently missing but relevant concepts should be added to SNOMED CT

Semantic validation of the use of SNOMED CT in HL7 clinical documents

<p>Abstract</p> <p>Background</p> <p>The HL7 Clinical Document Architecture (CDA) constrains the HL7 Reference Information model (RIM) to specify the format of HL7-compliant clinical documents, dubbed <it>CDA documents</it>. The use of clinical terminologies such as SNOMED CT^®further improves interoperability as they provide a shared understanding of concepts used in clinical documents. However, despite the use of the RIM and of shared terminologies such as SNOMED CT^®, gaps remain as to how to use both the RIM and SNOMED CT^®in HL7 clinical documents. The HL7 implementation guide on <it>Using SNOMED CT in HL7 Version 3 </it>is an effort to close this gap. It is, however, a human-readable document that is not suited for automatic processing. As such, health care professionals designing clinical documents need to ensure validity of documents manually.</p> <p>Results</p> <p>We represent the CDA using the Ontology Web Language OWL and further use the OWL version of SNOMED CT^®to enable the translation of CDA documents to so-called OWL <it>ontologies</it>. We formalize a subset of the constraints in the implementation guide on <it>Using SNOMED CT in HL7 Version 3 </it>as OWL <it>Integrity Constraints </it>and show that we can automatically validate CDA documents using OWL reasoners such as Pellet. Finally, we evaluate our approach via a prototype implementation that plugs in the Open Health Workbench.</p> <p>Conclusions</p> <p>We present a methodology to automatically check the validity of CDA documents which make reference to SNOMED CT^®terminology. The methodology relies on semantic technologies such as OWL. As such it removes the burden from IT health care professionals of having to manually implement such guidelines in systems that use HL7 Version 3 documents.</p

Towards supporting multiple semantics of named graphs using N3 rules

Semantic Web applications often require the partitioning of triples into subgraphs, and then associating them with useful metadata (e.g., provenance). This led to the introduction of RDF datasets, with each RDF dataset comprising a default graph and zero or more named graphs. However, due to differences in RDF implementations, no consensus could be reached on a standard semantics; and a range of different dataset semantics are currently assumed. For an RDF system not be limited to only a subset of online RDF datasets, the system would need to be extended to support different dataset semantics—exactly the problem that eluded consensus before. In this paper, we transpose this problem to Notation3 Logic, an RDF-based rule language that similarly allows citing graphs within RDF documents. We propose a solution where an N3 author can directly indicate the intended semantics of a cited graph— possibly, combining multiple semantics within a single document. We supply an initial set of companion N3 rules, which implement a number of RDF dataset semantics, which allow an N3-compliant system to easily support multiple different semantics

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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <it>Finding site </it>and about half of all concepts receive an attribute of type <it>Associated morphology</it>. Other types of attributes are less common.</p> <p>Conclusions</p> <p>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 <it>Finding site </it>and <it>Associated morphology</it>. The final mappings are available as additional files for this paper.</p

An evaluation of SNOMED CT® in the domain of complex chronic conditions

<p style="margin: 0pt; line-height: 200%; mso-layout-grid-align: none;"><strong>Objective</strong>: To determine the content coverage in SNOMED CT<strong style="mso-bidi-font-weight: normal;">®</strong> to represent the multidisciplinary terms and concepts in the domain for complex chronic conditions</p><p style="margin: 0pt; line-height: 200%; mso-layout-grid-align: none;"><strong>Methods</strong>: An evaluation of the coverage of multidisciplinary health factors in SNOMED CT<strong style="mso-bidi-font-weight: normal;">®</strong> for the complex and chronic condition, Multiple Chemical Sensitivity (MCS) is conducted in the study. The methodology included a retrospective audit of patient charts and feedback from multidisciplinary clinicians in the creation of a controlled vocabulary used in the generation of patient profiles for MCS. Clinicians and experts in the field reviewed and tested the vocabulary for its usefulness (scope, specificity and structure) by re-coding 3 patient profiles using the vocabulary. Cohen's kappa analysis was conducted to determine inter-rater reliability. Cronbach's alpha analysis was conducted to determine the internal reliability of the survey questionnaire.</p><p style="margin: 0pt; line-height: 200%; mso-layout-grid-align: none;"><strong>Results</strong>: One hundred patient charts and 9 clinicians from varying health disciplines participated in the study. SNOMED CT<strong style="mso-bidi-font-weight: normal;">®</strong> was shown to capture nearly 82% of the concepts spanning multidisciplinary areas of health focus. The nutrition area of health focus had the highest level of exact matches Furthermore post-coordination was applied in an attempt to improve coverage of concepts to 75% ( of 45 terms) of the missing terms in SNOMED CT ® . Seventy-five percent (n=9) of the clinicians agreed on the overall usefulness of the vocabulary.</p><p style="margin: 0pt; line-height: 200%; mso-layout-grid-align: none;"><strong>Conclusions</strong>: SNOMED CT® had a reasonable coverage of the multidisciplinary health concepts required to describe a complex and chronic condition. Standardizing the multidisciplinary vocabulary with reference tag to a widely used reference terminology such as SNOMED CT® to discuss the terms and concepts used may improve the understanding across disciplines and communities of practice. Overall, based on the availability of concepts in SNOMED CT® and the feedback from clinicians, the approach looks promising and should be further explored.</p