115 research outputs found
The Use of SNOMED CT for Representing Concepts Used in Preoperative Guidelines
The use of guidelines to improve quality of care depends on presenting
them in a standard machine-interpretable form and using common terms in
guidelines as well as in patient records. In this study, the use of SNOMED CT for
representing concepts used in preoperative assessment guidelines was evaluated.
Terms used in six of these guidelines were mapped to this terminology. Mappings
were presented based on three scores: no match, partial match, and complete
match. As eleven of the terms were repeatedly used in different guidelines, we
analyzed the results based on âtokenâ and âtypeâ coverage. Of 133 extracted terms
from guidelines, 107 terms should be covered by SNOMED CT of which 87% was
completely represented by this terminology. Our study showed that SNOMED CT
content should be extended before preoperative assessment guidelines can be
completely automated
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 ââIF condition THEN actionâ
statements to facilitate data extraction. Terms were extracted from the IFâTHEN 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 CTâs content
coverage was 89%. Of the 39 non-completely covered concepts, 69% violated at least one of SNOMED CTâs
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
Demonstration of Semantic Web-based Medical Ontologies and Clinical Decision Support Systems
Master's thesis in Information- and communication technology IKT590 - University of Agder 2016Konfidensiell til / confidential until 01.01.202
Construction of an Interface Terminology on SNOMED CT Generic Approach and Its Application in Intensive...
Objective: To provide a generic approach for
developing a domain-specific interface terminology on SNOMED CT and to apply this
approach to the domain of intensive care.
Methods:The process of developing an interface terminology on SNOMED CT can be regarded as six sequential phases: domain
analysis, mapping from the domain con -
cepts to SNOMED CT concepts, creating the
SNOMED CT subset guided by the mapping,
extending the subset with non-covered concepts, constraining the subset by removing
irrelevant content, and deploying the subset
in a terminology server.
Results:The APACHE IV classification, a standard in the intensive care with 445 diagnostic
categories, served as the starting point for designing the interface terminology. The majority (89.2%) of the diagnostic categories from
APACHE IV could be mapped to SNOMED CT
concepts and for the remaining concepts a
partial match was identified. The resulting initial set of mapped concepts consisted of 404
SNOMED CT concepts. This set could be extended to 83,125 concepts if all taxonomic
children of these concepts were included. Also
including all concepts that are referred to in
the definition of other concepts lead to a subset of 233,782 concepts. An evaluation of the
interface terminology should reveal what
level of detail in the subset is suitable for the
intensive care domain and whether parts
need further constraining. In the final phase,
the interface terminology is implemented in
the intensive care in a locally developed terminology server to collect the reasons for intensive care admission.
Conclusions: We provide a structure for the
process of identifying a domain-specific interface terminology on SNOMED CT. We use this
approach to design an interface terminology
on SNOMED CT for the intensive care domain.
This work is of value for other researchers who
intend to build a domain-specific interface
terminology on SNOMED CT
Characterization of patients with idiopathic normal pressure hydrocephalus using natural language processing within an electronic healthcare record system
OBJECTIVE: Idiopathic normal pressure hydrocephalus (iNPH) is an underdiagnosed, progressive, and disabling condition. Early treatment is associated with better outcomes and improved quality of life. In this paper, the authors aimed to identify features associated with patients with iNPH using natural language processing (NLP) to characterize this cohort, with the intention to later target the development of artificial intelligenceâdriven tools for early detection. /
METHODS: The electronic health records of patients with shunt-responsive iNPH were retrospectively reviewed using an NLP algorithm. Participants were selected from a prospectively maintained single-center database of patients undergoing CSF diversion for probable iNPH (March 2008âJuly 2020).
Analysis was conducted on preoperative health records including clinic letters, referrals, and radiology reports accessed through CogStack. Clinical features were extracted from these records as SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) concepts using a named entity recognition machine learning model.
In the first phase, a base model was generated using unsupervised training on 1 million electronic health records and supervised training with 500 double-annotated documents. The model was fine-tuned to improve accuracy using 300 records from patients with iNPH double annotated by two blinded assessors. Thematic analysis of the concepts identified by the machine learning algorithm was performed, and the frequency and timing of terms were analyzed to describe this patient group. /
RESULTS: In total, 293 eligible patients responsive to CSF diversion were identified. The median age at CSF diversion was 75 years, with a male predominance (69% male). The algorithm performed with a high degree of precision and recall (F1 score 0.92).
Thematic analysis revealed the most frequently documented symptoms related to mobility, cognitive impairment, and falls or balance. The most frequent comorbidities were related to cardiovascular and hematological problems. /
CONCLUSIONS: This model demonstrates accurate, automated recognition of iNPH features from medical records. Opportunities for translation include detecting patients with undiagnosed iNPH from primary care records, with the aim to ultimately improve outcomes for these patients through artificial intelligenceâdriven early detection of iNPH and prompt treatment
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