Clinical Vocabularies Research Articles

The DO-KB Knowledgebase: a 20-year journey developing the disease open science ecosystem.

In 2003, the Human Disease Ontology (DO, https://disease-ontology.org/) was established at Northwestern University. In the intervening 20 years, the DO has expanded to become a highly-utilized disease knowledge resource. Serving as the nomenclature and classification standard for human diseases, the DO provides a stable, etiology-based structure integrating mechanistic drivers of human disease. Over the past two decades the DO has grown from a collection of clinical vocabularies, into an expertly curated semantic resource of over 11300 common and rare diseases linking disease concepts through more than 37000 vocabulary cross mappings (v2023-08-08). Here, we introduce the recently launched DO Knowledgebase (DO-KB), which expands the DO's representation of the diseaseome and enhances the findability, accessibility, interoperabilityand reusability (FAIR) of disease data through a new SPARQL service and new Faceted Search Interface. The DO-KB is an integrated data system, built upon the DO's semantic disease knowledge backbone, with resources that expose and connectthe DO's semantic knowledge withdisease-related data across Open Linked Data resources. This update includes descriptions of efforts to assess the DO's global impact and improvements to data quality and content, with emphasis on changes in the last two years.

Considering non-hospital data in clinical informatics use cases, a review of the National Emergency Medical Services Information System (NEMSIS)

BackgroundThe National Emergency Medical Services (EMS) Information System (NEMSIS) Technical Assistance Center (TAC) collects and curates EMS activation level records for the United States. Originated as an outcomes assessment and service comparison tool, NEMSIS may have other high value clinical and public health uses. MethodsThis study acquired a 100% activation level public dataset for 2019 from NEMSIS TAC and assessed item response quantities. Subsumption of NEMSIS terms within other controlled clinical vocabularies was also considered. ResultsNone of the assessed terminologies (LOINC, ICD10-CM, SNOMED-CT) could describe meaningful volumes of NEMSIS item response codes. The 2019 activation year dataset included 36,525 non-date/time or calculated distinct item responses for 43 activation descriptive items. Said item responses yielded 2,101,844,053 activation distinct non-blank responses. Several NEMSIS item responses had high clinical and public health value. ConclusionsNEMSIS can support multiple public health use cases in addition to EMS outcomes assessment. A comprehensive custom value set is appropriate to integrate NEMSIS item response codes into controlled terminologies, FHIR or hospital Electronic Health Record applications.

Designing a standardized framework for data integration between zoonotic diseases systems: Towards one health surveillance

BackgroundZoonotic diseases or zoonoses account for a considerable ratio of infectious diseases outbreak; their effective surveillance demands coordinated actions by human and animal health organizations. However, zoonoses surveillance data are collected individually from standalone information systems for either humans or animals with varied structures, processes and applications. In moving towards one health (OH) surveillance, integrating the zoonoses data may help prevent and control these diseases. Therefore, this research aimed to determine essential data elements and a consistent reporting template towards interoperability. Material and methodsIn this study, first, the zoonotic diseases minimum dataset (ZD-MDS) was identified according to a comprehensive literature review coupled with the agreements of experts. Then, the ZD-MDS was mapped to structured clinical vocabularies. Also, the health level seven-clinical document architecture (HL7-CDA) standard was used to define the interoperable and human-machine reporting template. ResultsThe ZD-MDS was divided into administrative and clinical sections with five and seven data classes and total of 38 and 57 data elements, respectively. Then, the corresponding data values and systematized nomenclature of medicine-clinical terms (SNOMED-CT) codes were defined for each data element. The reporting template was structured according to three sections of CDA template, extensible markup language (XML) hierarchical and tags. ConclusionOur study suggested that zoonoses surveillance could be improved by integrating and exchanging data from different databases across humans and animal organizations. The developed template provided a comprehensive and interoperable dataset, making data more comparable and reportable across multiple studies and settings.

A Data-Driven Iterative Approach for Semi-automatically Assessing the Correctness of Medication Value Sets: A Proof of Concept Based on Opioids

Background Value sets are lists of terms (e.g., opioid medication names) and their corresponding codes from standard clinical vocabularies (e.g., RxNorm) created with the intent of supporting health information exchange and research. Value sets are manually-created and often exhibit errors. Objectives The aim of the study is to develop a semi-automatic, data-centric natural language processing (NLP) method to assess medication-related value set correctness and evaluate it on a set of opioid medication value sets. Methods We developed an NLP algorithm that utilizes value sets containing mostly true positives and true negatives to learn lexical patterns associated with the true positives, and then employs these patterns to identify potential errors in unseen value sets. We evaluated the algorithm on a set of opioid medication value sets, using the recall, precision and F 1 -score metrics. We applied the trained model to assess the correctness of unseen opioid value sets based on recall. To replicate the application of the algorithm in real-world settings, a domain expert manually conducted error analysis to identify potential system and value set errors. Results Thirty-eight value sets were retrieved from the Value Set Authority Center, and six (two opioid, four non-opioid) were used to develop and evaluate the system. Average precision, recall, and F 1 -score were 0.932, 0.904, and 0.909, respectively on uncorrected value sets; and 0.958, 0.953, and 0.953, respectively after manual correction of the same value sets. On 20 unseen opioid value sets, the algorithm obtained average recall of 0.89. Error analyses revealed that the main sources of system misclassifications were differences in how opioids were coded in the value sets—while the training value sets had generic names mostly, some of the unseen value sets had new trade names and ingredients. Conclusion The proposed approach is data-centric, reusable, customizable, and not resource intensive. It may help domain experts to easily validate value sets.

Coding Systems for Clinical Decision Support: Theoretical and Real-World Comparative Analysis.

BackgroundEffective clinical decision support systems require accurate translation of practice recommendations into machine-readable artifacts; developing code sets that represent clinical concepts are an important step in this process. Many clinical coding systems are currently used in electronic health records, and it is unclear whether all of these systems are capable of efficiently representing the clinical concepts required in executing clinical decision support systems.ObjectiveThe aim of this study was to evaluate which clinical coding systems are capable of efficiently representing clinical concepts that are necessary for translating artifacts into executable code for clinical decision support systems.MethodsTwo methods were used to evaluate a set of clinical coding systems. In a theoretical approach, we extracted all the clinical concepts from 3 preventive care recommendations and constructed a series of code sets containing codes from a single clinical coding system. In a practical approach using data from a real-world setting, we studied the content of 1890 code sets used in an internationally available clinical decision support system and compared the usage of various clinical coding systems.ResultsSNOMED CT and ICD-10 (International Classification of Diseases, Tenth Revision) proved to be the most accurate clinical coding systems for most concepts in our theoretical evaluation. In our practical evaluation, we found that International Classification of Diseases (Tenth Revision) was most often used to construct code sets. Some coding systems were very accurate in representing specific types of clinical concepts, for example, LOINC (Logical Observation Identifiers Names and Codes) for investigation results and ATC (Anatomical Therapeutic Chemical Classification) for drugs.ConclusionsNo single coding system seems to fulfill all the needs for representing clinical concepts for clinical decision support systems. Comprehensiveness of the coding systems seems to be offset by complexity and forms a barrier to usability for code set construction. Clinical vocabularies mapped to multiple clinical coding systems could facilitate clinical code set construction.

Extracting Alcohol and Substance Abuse Status from Clinical Notes: The Added Value of Nursing Data.

We applied an open source natural language processing (NLP) system "NimbleMiner" to identify clinical notes with mentions of alcohol and substance abuse. NimbleMiner allows users to rapidly discover clinical vocabularies (using word embedding model) and then implement machine learning for text classification. We used a large inpatient dataset with over 50,000 intensive care unit admissions (MIMIC II). Clinical notes included physician-written discharge summaries (n = 51,201) and nursing notes (n = 412,343). We first used physician-written discharge summaries to train the system's algorithm and then added nursing notes to the physician-written discharge summaries and evaluated algorithms prediction accuracy. Adding nursing notes to the physician-written discharge summaries resulted in almost two-fold vocabulary expansion. NimbleMiner slightly outperformed other state-of-the-art NLP systems (average F-score = .84), while requiring significantly less time for the algorithms development.: Our findings underline the importance of nursing data for the analysis of electronic patient records.

Adoption of clinical vocabularies LOINC® and SNOMED CT® in the microbiology laboratory and their integration in the information systems and electronic medical records

Adoption of clinical vocabularies LOINC® and SNOMED CT® in the microbiology laboratory and their integration in the information systems and electronic medical records

Disease Ontology: improving and unifying disease annotations across species.

ABSTRACTModel organisms are vital to uncovering the mechanisms of human disease and developing new therapeutic tools. Researchers collecting and integrating relevant model organism and/or human data often apply disparate terminologies (vocabularies and ontologies), making comparisons and inferences difficult. A unified disease ontology is required that connects data annotated using diverse disease terminologies, and in which the terminology relationships are continuously maintained. The Mouse Genome Database (MGD, http://www.informatics.jax.org), Rat Genome Database (RGD, http://rgd.mcw.edu) and Disease Ontology (DO, http://www.disease-ontology.org) projects are collaborating to augment DO, aligning and incorporating disease terms used by MGD and RGD, and improving DO as a tool for unifying disease annotations across species. Coordinated assessment of MGD's and RGD's disease term annotations identified new terms that enhance DO's representation of human diseases. Expansion of DO term content and cross-references to clinical vocabularies (e.g. OMIM, ORDO, MeSH) has enriched the DO's domain coverage and utility for annotating many types of data generated from experimental and clinical investigations. The extension of anatomy-based DO classification structure of disease improves accessibility of terms and facilitates application of DO for computational research. A consistent representation of disease associations across data types from cellular to whole organism, generated from clinical and model organism studies, will promote the integration, mining and comparative analysis of these data. The coordinated enrichment of the DO and adoption of DO by MGD and RGD demonstrates DO's usability across human data, MGD, RGD and the rest of the model organism database community.

Matching Medical Websites to Medical Guidelines through Clinical Vocabularies in View of Website Quality Assessment

The quality of medical texts provided to general public on the Internet is a serious issue nowadays. Unfortunately the only feasible way to approve the adequacy of the medical information content is human verification today. Best practices in medicine are systematically captured by medical guidelines (MGLs), which are provided by renowned medical societies. We propose a simple approach to exploiting MGL content as ’gold standard’ for the assessment of content quality in medical web sites (WS), based on the idea that the information content is reflected in the domain terminology used. Concept candidates discovered in a MGL and in the tested web pages are matched to the UMLS terminological system. In a small case study, MGLs and WSs have been analyzed for similarity at term and concept level. The research is a step towards automated evaluation of WS content on the basis of MGLs as the quality standard.

Frequency analysis of medical concepts in clinical trials and their coverage in MeSH and SNOMED-CT.

Eligibility criteria (EC) of clinical trials play a key role in selecting appropriate study candidates and the validity of the outcome of a clinical trial. However, in most cases EC are provided in unstandardised ways such as free text, which raises significant challenges for machine-readability. To establish a list of most frequent medical concepts in clinical trials with semantic annotations. This concept list contributes to standardisation of EC and identifies relevant data items in electronic health records (EHRs) for clinical research. The coverage of the list in two major clinical vocabularies, MeSH and SNOMED-CT, will be assessed. Four hundred and twenty-five clinical trials conducted between 2000 and 2011 at a German university hospital were analysed. 6671 EC were manually annotated by a medical coder using Concept Unique Identifiers (CUIs) provided by the Unified Medical Language System. Two physicians performed a semi-automatic CUI code revision. Concept frequency was analysed and clusters of concepts were manually identified.A binomial significance test was applied to quantify coverage differences of the most frequent concepts in MeSH and SNOMED-CT. Based on manual medical coding of 425 clinical trials, 7588 concepts were identified, of which 5236 were distinct. A top 100 list containing 101 most frequent medical concepts was established. The concepts of this list cover 25 % of all concept occurrences in all analysed clinical trials. This list reveals six missing entries in SNOMED-CT, 12 in MeSH. The median of EC frequency per trial has increased throughout the trial years (2000 -2005: 8 EC/trial, 2011: 14 EC/trial). Relatively few concepts cover one quarter of concept occurrences that represent EC in recent studies. Therefore, these concepts can serve as candidate data elements for integration into EHRs to optimise patient recruitment in clinical research.

Enrichment/Population of Customized CPR (Computer-Based Patient Record) Ontology from Free-Text Reports for CSI (Computer Semantic Interoperability)

CSI (Computer Semantic Interoperability) is a very important issue in healthcare. Ways for heterogeneous computer systems to “understand” important facts from the clinical process for clinical decision support are now beginning to be addressed. The authors present here comprehensive contributions to achieve CSI. EHR (Electronic Health Record) systems provide a way to extract reports of the clinicians activity. In order to formalize an automated acquisition from semi-structured, free-form, natural language texts in Portuguese into a Clinical Practice Ontology an important step is to develop the ability of decoding all the nicknames, acronyms and short-hand forms that each clinician tend to write down in their reports. The authors present the steps to develop clinical vocabularies extracting directly from clinical reports in Portuguese available in the SAM (Sistema de Apoio ao Médico) system. The presented techniques are easily further developed for any other natural language or knowledge representation framework with due adaptations.

Enrichment/Population of Customized CPR (Computer-based Patient Record) Ontology from Free-text Reports for CSI (Computer Semantic Interoperability)

CSI (Computer Semantic Interoperability) is a very important issue in healthcare. Ways for heterogeneous computer systems to “understand” important facts from the clinical process for clinical decision support are now beginning to be addressed. We present here comprehensive contributions to achieve CSI. EHR (Electronic Health Record) systems provide a way to extract reports of the clinicians activity. In order to formalize an automated acquisition from semi-structured, free-form, natural language texts in Portuguese into a Clinical Practice Ontology an important step is to develop the ability of decoding all the nicknames, acronyms and short-hand forms that each clinician tend to write down in their reports. We present the steps to develop clinical vocabularies extracting directly from clinical reports in Portuguese available in the SAM (Sistema de Apoio ao Médico) system. The presented techniques are easily further developed for any other natural language or knowledge representation framework with due adaptations.

Matching Medical Websites to Medical Guidelines through Clinical Vocabularies in the Context of Website Quality Assessment

Modern technology offers a wide array of possibilities to publish almost any content freely on the Internet. Because of the importance and delicacy of medical information, the quality of such texts provided to general public seems to be a serious issue nowadays. Unfortunately, the only feasible way to approve the adequacy of the medical information content is human verification. Best practices in medicine are systematically captured by medical guidelines (MGL), which are provided by renowned medical societies and based on results of Evidence-Based Medicine (EBM). We propose a simple approach exploiting MGL content as a benchmark for the assessment of a content quality in medical web sites (WS). It is based on the idea that the information content or at least the scope of a medical text is reflected in the domain terminology used. We discuss a possible use of this approach in semiautomatic human-based quality verification and various aspects related to its application. Concept candidates discovered in a MGL and in the tested web pages are matched to UMLS, yielding sets of used medical terms and corresponding concepts. Several aggregation techniques for MGLs were proposed and tested. The two sets are analyzed for overall similarity at term and concept level. The method was applied on a selected medical topic employing relevant MGL and 100 WS. All the analyzed web pages fell into five distinct categories (corresponding to the target audience). Aggregations for the MGLs were proposed and tested. The average cosine similarity to MGL across all tested WS reached 0.69 whereas the average similarity calculated per each category varied up to 7,6% against the overall number. The research done is the first step towards automated evaluation of a medical web page content on the basis of MGLs as the quality standard. We describe further tasks which would improve the outputs of comparison and the possibility of its common application.

The National Cancer Institute’s Thésaurus and Ontology

The NCI Thysaurus is a public domain description logic-based terminology produced by the National Cancer Institute, and distributed as a component of the NCI Center for Bioinformatics caCORE distribution. It is deep and complex compared to most broad clinical vocabularies, implementing rich semantic interrelationships between the nodes of its taxonomies. The semantic relationships in the Thysaurus are intended to facilitate translational research and to support the bioinformatics infrastructure of the Institute.

Moving toward a United States strategic plan in primary care informatics: a White Paper of the Primary Care Informatics Working Group, American Medical Informatics Association.

The Primary Care Informatics Working Group (PCIWG) of the American Medical Informatics Association (AMIA) has identified the absence of a national strategy for primary care informatics. Under PCIWG leadership, major national and international societies have come together to create the National Alliance for Primary Care Informatics (NAPCI), to promote a connection between the informatics community and the organisations that support primary care. The PCIWG clinical practice subcommittee has recognised the necessity of a global needs assessment, and proposed work in point-of-care technology, clinical vocabularies, and ambulatory electronic medical record development. Educational needs include a consensus statement on informatics competencies, recommendations for curriculum and teaching methods, and methodologies to evaluate their effectiveness. The research subcommittee seeks to define a primary care informatics research agenda, and to support and disseminate informatics research throughout the primary care community. The AMIA board of directors has enthusiastically endorsed the conceptual basis for this White Paper.

The National Cancer Institute's Thesaurus and Ontology

The NCI Thysaurus is a public domain description logic-based terminology produced by the National Cancer Institute, and distributed as a component of the NCI Center for Bioinformatics caCORE distribution. It is deep and complex compared to most broad clinical vocabularies, implementing rich semantic interrelationships between the nodes of its taxonomies. The semantic relationships in the Thysaurus are intended to facilitate translational research and to support the bioinformatics infrastructure of the Institute.

Concept-oriented Standardization and Statistics-oriented Classification: Continuing the Classification versus Nomenclature Controversy

Nowadays, most activities on controlled medical vocabularies focus on the provision of a sufficient atomic-level granularity for representing clinical data. Amongst others, clinical vocabularies should be concept oriented, compositional and should also reject "Not Elsewhere Classified". We strongly share the opinion that there is a need to deal with serious deficits of existing manually created vocabularies and with new demands for computer-based advanced processing and exchange of medical language data. However, we do not share the opinion that methodological requirements like observational and structural comparability needed for sound statistics should not be included in desiderata of controlled medical vocabularies. Statistical-oriented classifications are not developed for representing detailed clinical data but for providing purpose-dependent classes where cases of interest are assigned uniquely. Either statistical classifications are not included into the set of controlled medical vocabularies in the sense of Cimino, or his desiderata are misleading. We argue that statistical classifications should be linked to (formal) concept systems, but again this linkage does not change their different natures. With this article we continue the "classification versus nomenclature" controversy referring to Coté.

Modeling Concepts in Medicine for Medical Language Understanding

Over the past two decades, the construction of models for medical concept representation and for understanding of the deep meaning of medical narrative texts have been challenging areas of medical informatics research. This review highlights how these two inter-related domains have evolved, emphasizing aspects of medical modeling as a tool for medical language understanding. A representation schema, which balances partially but accurately with complete but complex representations of domain-specific knowledge, must be developed to facilitate language understanding. Representative examples are drawn from two major independent efforts undertaken by the authors: the elaboration and the subsequent adjustment of the RECIT multilingual analyzer to include a robust medical concept model, and the recasting of a frame-based interlingua system, originally developed to map equivalent concepts between controlled clinical vocabularies, to invoke a similar concept model.

Read Code quality assurance: from simple syntax to semantic stability.

As controlled clinical vocabularies assume an increasing role in modern clinical information systems, so the issue of their quality demands greater attention. In order to meet the resulting stringent criteria for completeness and correctness, a quality assurance system comprising a database of more than 500 rules is being developed and applied to the Read Thesaurus. The authors discuss the requirement to apply quality assurance processes to their dynamic editing database in order to ensure the quality of exported products. Sources of errors include human, hardware, and software factors as well as new rules and transactions. The overall quality strategy includes prevention, detection, and correction of errors. The quality assurance process encompasses simple data specification, internal consistency, inspection procedures and, eventually, field testing. The quality assurance system is driven by a small number of tables and UNIX scripts, with "business rules" declared explicitly as Structured Query Language (SQL) statements. Concurrent authorship, client-server technology, and an initial failure to implement robust transaction control have all provided valuable lessons. The feedback loop for error management needs to be short.

An interlingua for electronic interchange of medical information: Using frames to map between clinical vocabularies

The proliferation of medical knowledge has led to the development of extensive dictionaries for electronically accessing information resources. The task of standardizing terminology used for electronic hospital records and for knowledge bases for medical expert systems and indexing the medical literature cannot easily be met by developing a single, monolithic “official” medical vocabulary. Developing a monolithic vocabulary would require a massive effort, and its existence would not guarantee its use by third-party payors, by practicing clinicians, or by developers of electronic medical information systems. Recognizing this, the National Library of Medicine (NLM) has begun to develop the Unified Medical Language System (UMLS) as a means of promoting electronic information exchange among systems with controlled vocabularies. The authors describe a frame-based system developed as an experimental approach to mapping between controlled clinical vocabularies.