A State-of-the Art Review of SNOMED CT Terminology Binding and Recommendations for Practice and Research.
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ABSTRACT: Background
Unambiguous sharing of data requires information models and terminology in combination, but there is a lack of knowledge as to how they should be combined, leading to impaired interoperability.Objectives
To facilitate creation of guidelines for SNOMED CT terminology binding we have performed a literature review to find existing recommendations and expose knowledge gaps. The primary audience is practitioners and researchers working with terminology binding.Methods
PubMed, Scopus, and Web of Science were searched for papers containing "terminology binding," "subset," "map," "information model" or "implement" and the term "SNOMED."Results
The search yielded 616 unique papers published from 2004 to 2020, from which 55 papers were selected and analyzed inductively. Topics described in the papers include problems related to input material, SNOMED CT, information models, and lack of appropriate tools as well as recommendations regarding competence.Conclusion
Recommendations are given for practitioners and researchers. Many of the stated problems can be solved by better co-operation between domain experts and informaticians and better knowledge of SNOMED CT. Settings where these competences either work together or where staff with knowledge of both act as brokers are well equipped for terminology binding. Tooling is not thoroughly researched and might be a possible way to facilitate terminology binding.
SUBMITTER: Rossander A
PROVIDER: S-EPMC8714300 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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:BackgroundThe health sector has quickly become a target for cyberattacks. Hospitals are especially sensitive to these sorts of attacks as any disruption in operations or even disclosure of patient personal information can have far-reaching consequences. The objective of this study was to map the available literature on cyberattacks on hospitals and to identify the different domains of research, while extracting the recommendations and guidelines put forth in the literature.MethodsFour databases (PubMed, Web of Science, ProQuest, and Scopus) were searched using standardized and adapted search syntax in order to identify relevant manuscripts published between 1997 and 2017. These were screened by two reviewers and included or excluded based on inclusion and exclusion criteria. Data from articles were then extracted and analyzed.ResultsThe search identified 818 records of which 97 were included. Of the 97, 32% were published in 2017 while around 40% of the articles were published prior to the last three years. Six domains of research emerged through the analysis, which are included here: context and trends in cybersecurity (27.8%), connected medical devices and equipment (29.9%), hospital information systems (14.4%), raising awareness and lessons learned (6.2%), information security methodology (15.4%), and specific types of attacks (6.2%).ConclusionThere is a generally growing interest in the research field, but the available literature remains limited in number. There are important aspects of cybersecurity (e.g. cloud storage and access management) as well as specific medical fields that rely on various medical devices that have been neglected. Recommendations are available, but comprehensive guidelines and standardized best practice measures are still necessary.
| S-EPMC6330387 | biostudies-literature
Project description:ObjectiveThis article reviews recent literature on the use of SNOMED CT as an extension of Lee et al's 2014 review on the same topic. The Lee et al's article covered literature published from 2001-2012, and the scope of this review was 2013-2020.Materials and methodsIn line with Lee et al's methods, we searched the PubMed and Embase databases and identified 1002 articles for review, including studies from January 2013 to September 2020. The retrieved articles were categorized and analyzed according to SNOMED CT focus categories (ie, indeterminate, theoretical, pre-development, implementation, and evaluation/commodity), usage categories (eg, illustrate terminology systems theory, prospective content coverage, used to classify or code in a study, retrieve or analyze patient data, etc.), medical domains, and countries.ResultsAfter applying inclusion and exclusion criteria, 622 articles were selected for final review. Compared to the papers published between 2001 and 2012, papers published between 2013 and 2020 revealed an increase in more mature usage of SNOMED CT, and the number of papers classified in the "implementation" and "evaluation/commodity" focus categories expanded. When analyzed by decade, papers in the "pre-development," "implementation," and "evaluation/commodity" categories were much more numerous in 2011-2020 than in 2001-2010, increasing from 169 to 293, 30 to 138, and 3 to 65, respectively.ConclusionPublished papers in more mature usage categories have substantially increased since 2012. From 2013 to present, SNOMED CT has been increasingly implemented in more practical settings. Future research should concentrate on addressing whether SNOMED CT influences improvement in patient care.
| S-EPMC8363812 | biostudies-literature
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:This article summarizes outcomes of the behavioral interventions work group for the Veterans Health Administration (VHA) State of the Art Conference (SOTA) for Weight Management. Sixteen VHA and non-VHA subject matter experts, representing clinical care delivery, research, and policy arenas, participated. The work group reviewed current evidence of efficacy, effectiveness, and implementation of behavioral interventions for weight management, participated in phone- and online-based consensus processes, generated key questions to address gaps, and attended an in-person conference in March 2016. The work group agreed that there is strong evidence for efficacy and effectiveness of core behavioral intervention components and processes, but insufficient evidence to determine the comparative effectiveness of multiple clinician-delivered weight management modalities, as well as technologies that may or may not supplement clinician-delivered treatments. Effective strategies for implementation of weight management services in VHA were identified. The SOTA work group's foremost policy recommendations are to establish a system-wide culture for weight management and to identify a population-level health metric to measure the impact of weight management interventions that can be tracked and clearly communicated throughout VHA. The work group's top research recommendation is to determine how to deploy and scale the most effective behavioral weight management interventions for Veterans.
| S-EPMC5359158 | biostudies-literature
Project description:In patients with suspected coronary artery disease (CAD), dynamic myocardial computed tomography perfusion (CTP) imaging combined with coronary CT angiography (CTA) has become a comprehensive diagnostic examination technique resulting in both anatomical and quantitative functional information on myocardial blood flow, and the presence and grading of stenosis. Recently, CTP imaging has been proven to have good diagnostic accuracy for detecting myocardial ischemia, comparable to stress magnetic resonance imaging and positron emission tomography perfusion, while being superior to single photon emission computed tomography. Dynamic CTP accompanied by coronary CTA can serve as a gatekeeper for invasive workup, as it reduces unnecessary diagnostic invasive coronary angiography. Dynamic CTP also has good prognostic value for the prediction of major adverse cardiovascular events. In this article, we will provide an overview of dynamic CTP, including the basics of coronary blood flow physiology, applications and technical aspects including protocols, image acquisition and reconstruction, future perspectives, and scientific challenges. KEY POINTS: • Stress dynamic myocardial CT perfusion combined with coronary CTA is a comprehensive diagnostic examination technique resulting in both anatomical and quantitative functional information. • Dynamic CTP imaging has good diagnostic accuracy for detecting myocardial ischemia comparable to stress MRI and PET perfusion. • Dynamic CTP accompanied by coronary CTA may serve as a gatekeeper for invasive workup and can guide treatment in obstructive coronary artery disease.
| S-EPMC10326111 | biostudies-literature
Project description:BackgroundThere is worldwide demand for an affordable hemoglobin measurement solution, which is a particularly urgent need in developing countries. The smartphone, which is the most penetrated device in both rich and resource-constrained areas, would be a suitable choice to build this solution. Consideration of a smartphone-based hemoglobin measurement tool is compelling because of the possibilities for an affordable, portable, and reliable point-of-care tool by leveraging the camera capacity, computing power, and lighting sources of the smartphone. However, several smartphone-based hemoglobin measurement techniques have encountered significant challenges with respect to data collection methods, sensor selection, signal analysis processes, and machine-learning algorithms. Therefore, a comprehensive analysis of invasive, minimally invasive, and noninvasive methods is required to recommend a hemoglobin measurement process using a smartphone device.ObjectiveIn this study, we analyzed existing invasive, minimally invasive, and noninvasive approaches for blood hemoglobin level measurement with the goal of recommending data collection techniques, signal extraction processes, feature calculation strategies, theoretical foundation, and machine-learning algorithms for developing a noninvasive hemoglobin level estimation point-of-care tool using a smartphone.MethodsWe explored research papers related to invasive, minimally invasive, and noninvasive hemoglobin level measurement processes. We investigated the challenges and opportunities of each technique. We compared the variation in data collection sites, biosignal processing techniques, theoretical foundations, photoplethysmogram (PPG) signal and features extraction process, machine-learning algorithms, and prediction models to calculate hemoglobin levels. This analysis was then used to recommend realistic approaches to build a smartphone-based point-of-care tool for hemoglobin measurement in a noninvasive manner.ResultsThe fingertip area is one of the best data collection sites from the body, followed by the lower eye conjunctival area. Near-infrared (NIR) light-emitting diode (LED) light with wavelengths of 850 nm, 940 nm, and 1070 nm were identified as potential light sources to receive a hemoglobin response from living tissue. PPG signals from fingertip videos, captured under various light sources, can provide critical physiological clues. The features of PPG signals captured under 1070 nm and 850 nm NIR LED are considered to be the best signal combinations following a dual-wavelength theoretical foundation. For error metrics presentation, we recommend the mean absolute percentage error, mean squared error, correlation coefficient, and Bland-Altman plot.ConclusionsWe addressed the challenges of developing an affordable, portable, and reliable point-of-care tool for hemoglobin measurement using a smartphone. Leveraging the smartphone's camera capacity, computing power, and lighting sources, we define specific recommendations for practical point-of-care solution development. We further provide recommendations to resolve several long-standing research questions, including how to capture a signal using a smartphone camera, select the best body site for signal collection, and overcome noise issues in the smartphone-captured signal. We also describe the process of extracting a signal's features after capturing the signal based on fundamental theory. The list of machine-learning algorithms provided will be useful for processing PPG features. These recommendations should be valuable for future investigators seeking to build a reliable and affordable hemoglobin prediction model using a smartphone.
| S-EPMC8063099 | biostudies-literature
Project description:Study questionWhat recommendations can be provided to improve terminology for normal and ectopic pregnancy description on ultrasound?Summary answerThe present ESHRE document provides 17 consensus recommendations on how to describe normally sited and different types of ectopic pregnancies on ultrasound.What is known alreadyCurrent diagnostic criteria stipulate that each type of ectopic pregnancy can be defined by clear anatomical landmarks which facilitates reaching a correct diagnosis. However, a clear definition of normally sited pregnancies and a comprehensive classification of ectopic pregnancies are still lacking.Study design size durationA working group of members of the ESHRE Special Interest Group in Implantation and Early Pregnancy (SIG-IEP) and selected experts in ultrasound was formed in order to write recommendations on the classification of ectopic pregnancies.Participants/materials setting methodsThe working group included nine members of different nationalities with internationally recognised experience in ultrasound and diagnosis of ectopic pregnancies on ultrasound. This document is developed according to the manual for development of ESHRE recommendations for good practice. The recommendations were discussed until consensus by the working group, supported by a survey among the members of the ESHRE SIG-IEP.Main results and the role of chanceA clear definition of normally sited pregnancy on ultrasound scan is important to avoid misdiagnosis of uterine ectopic pregnancies. A comprehensive classification of ectopic pregnancy must include definitions and descriptions of each type of ectopic pregnancy. Only a classification which provides descriptions and diagnostic criteria for all possible locations of ectopic pregnancy would be fit for use in routine clinical practice. The working group formulated 17 recommendations on the diagnosis of the different types of ectopic pregnancies on ultrasound. In addition, for each of the types of ectopic pregnancy, a schematic representation and examples on 2D and 3D ultrasound are provided.Limitations reasons for cautionOwing to the limited evidence available, recommendations are mostly based on clinical and technical expertise.Wider implications of the findingsThis document is expected to have a significant impact on clinical practice in ultrasound for early pregnancy. The development of this terminology will help to reduce the risk of misdiagnosis and inappropriate treatment.Study funding/competing interestsThe meetings of the working group were funded by ESHRE. T.T. declares speakers' fees from GE Healthcare. The other authors declare that they have no conflict of interest.Trial registration numberN/A.DisclaimerThis Good Practice Recommendations (GPR) document represents the views of ESHRE, which are the result of consensus between the relevant ESHRE stakeholders and where relevant based on the scientific evidence available at the time of preparation. ESHRE's GPRs should be used for informational and educational purposes. They should not be interpreted as setting a standard of care or be deemed inclusive of all proper methods of care nor exclusive of other methods of care reasonably directed to obtaining the same results. They do not replace the need for application of clinical judgement to each individual presentation, nor variations based on locality and facility type. Furthermore, ESHRE's GPRs do not constitute or imply the endorsement, recommendation or favouring of any of the included technologies by ESHRE.
| S-EPMC7738750 | 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: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