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Background: The digitisation of healthcare records has generated vast amounts of unstructured data, presenting opportunities for improvements in disease diagnosis when clinical coding falls short, such as in the recording of patient symptoms. This study presents an approach using natural language processing to extract clinical concepts from free-text which are used to automatically form diagnostic criteria for lung cancer from unstructured secondary-care data. Methods: Patients aged 40 and above who underwent a chest x-ray (CXR) between 2016 and 2022 were included. ICD-10 and unstructured data were pulled from their electronic health records (EHRs) over the preceding 12 months to the CXR. The unstructured data were processed using named entity recognition to extract symptoms, which were mapped to SNOMED-CT codes. Subsumption of features up the SNOMED-CT hierarchy was used to mitigate against sparse features and a frequency-based criteria, combined with univariate logarithmic probabilities, was applied to select candidate features to take forward to the model development phase. A genetic algorithm was employed to identify the most discriminating features to form the diagnostic criteria. Results: 75002 patients were included, with 1012 lung cancer diagnoses made within 12 months of the CXR. The best-performing model achieved an AUROC of 0.72. Results showed that an existing 'disorder of the lung', such as pneumonia, and a 'cough' increased the probability of a lung cancer diagnosis. 'Anomalies of great vessel', 'disorder of the retroperitoneal compartment' and 'context-dependent findings', such as pain, statistically reduced the risk of lung cancer, making other diagnoses more likely. The performance of the developed model was compared to the existing cancer risk scores, demonstrating superior performance. Conclusions: The proposed methods demonstrated success in leveraging unstructured secondary-care data to derive diagnostic criteria for lung cancer, outperforming existing risk tools. These advancements show potential for enhancing patient care and results. However, it is essential to tackle specific limitations by integrating primary care data to ensure a more thorough and unbiased development of diagnostic criteria. Moreover, the study highlights the importance of contextualising SNOMED-CT concepts into meaningful terminology that resonates with clinicians, facilitating a clearer and more tangible understanding of the criteria applied. [ABSTRACT FROM AUTHOR]

Abstract Background The digitisation of healthcare records has generated vast amounts of unstructured data, presenting opportunities for improvements in disease diagnosis when clinical coding falls short, such as in the recording of patient symptoms. This study presents an approach using natural language processing to extract clinical concepts from free-text which are used to automatically form diagnostic criteria for lung cancer from unstructured secondary-care data. Methods Patients aged 40 and above who underwent a chest x-ray (CXR) between 2016 and 2022 were included. ICD-10 and unstructured data were pulled from their electronic health records (EHRs) over the preceding 12 months to the CXR. The unstructured data were processed using named entity recognition to extract symptoms, which were mapped to SNOMED-CT codes. Subsumption of features up the SNOMED-CT hierarchy was used to mitigate against sparse features and a frequency-based criteria, combined with univariate logarithmic probabilities, was applied to select candidate features to take forward to the model development phase. A genetic algorithm was employed to identify the most discriminating features to form the diagnostic criteria. Results 75002 patients were included, with 1012 lung cancer diagnoses made within 12 months of the CXR. The best-performing model achieved an AUROC of 0.72. Results showed that an existing ‘disorder of the lung’, such as pneumonia, and a ‘cough’ increased the probability of a lung cancer diagnosis. ‘Anomalies of great vessel’, ‘disorder of the retroperitoneal compartment’ and ‘context-dependent findings’, such as pain, statistically reduced the risk of lung cancer, making other diagnoses more likely. The performance of the developed model was compared to the existing cancer risk scores, demonstrating superior performance. Conclusions The proposed methods demonstrated success in leveraging unstructured secondary-care data to derive diagnostic criteria for lung cancer, outperforming existing risk tools. These advancements show potential for enhancing patient care and results. However, it is essential to tackle specific limitations by integrating primary care data to ensure a more thorough and unbiased development of diagnostic criteria. Moreover, the study highlights the importance of contextualising SNOMED-CT concepts into meaningful terminology that resonates with clinicians, facilitating a clearer and more tangible understanding of the criteria applied.

Semantic web technologies (SWT) represent data in a format that is easier for machines to understand. Validating the knowledge in data graphs created using SWT is critical to ensure that the axioms accurately represent the so-called “real” world. However, data graph validation is a significant challenge in the semantic web domain. The Shapes Constraint Language (SHACL) is the latest W3C standard developed with the goal of validating data-graphs. SHACL (pronounced as shackle) is a relatively new standard and hitherto has predominantly been employed to validate generic data graphs like WikiData and DBPedia. In generic data graphs, the name of a class does not affect the shape of a class, but this is not the case with biomedical ontology data graphs. The shapes of classes in biomedical ontology data graphs are highly influenced by the names of the classes, and the SHACL shape creation methods developed for generic data graphs fail to consider this characteristic difference. Thus, the existing SHACL shape creation methods do not perform well for domain-specific biomedical ontology data graphs. Maintaining the quality of biomedical ontology data graphs is crucial to ensure accurate analysis in safety-critical applications like Electronic Health Record (EHR) systems referencing such data graphs. Thus, in this work, we present a novel method to create enhanced SHACL shapes that consider the aforementioned characteristic difference to better validate biomedical ontology data graphs. We leverage the knowledge available from lexical auditing techniques for biomedical ontologies and incorporate this knowledge to create smart SHACL shapes. We also create SHACL shapes (baseline SHACL graph) without incorporating the lexical knowledge of the class names, as is performed by existing methods, and compare the performance of our enhanced SHACL shapes with the baseline SHACL shapes. The results demonstrate that the enhanced SHACL shapes augmented with lexical knowledge of the class names identified 176 violations which the baseline SHACL shapes, void of this lexical knowledge, failed to detect. Thus, the enhanced SHACL shapes presented in this work significantly improve the validation performance of biomedical ontology data graphs, thereby reducing the errors present in such data graphs and ensuring safe use in the life-critical applications referencing them.

Semantic web technologies (SWT) represent data in a format that is easier for machines to understand. Validating the knowledge in data graphs created using SWT is critical to ensure that the axioms accurately represent the so-called "real" world. However, data graph validation is a significant challenge in the semantic web domain. The Shapes Constraint Language (SHACL) is the latest W3C standard developed with the goal of validating data-graphs. SHACL (pronounced as shackle) is a relatively new standard and hitherto has predominantly been employed to validate generic data graphs like WikiData and DBPedia. In generic data graphs, the name of a class does not affect the shape of a class, but this is not the case with biomedical ontology data graphs. The shapes of classes in biomedical ontology data graphs are highly influenced by the names of the classes, and the SHACL shape creation methods developed for generic data graphs fail to consider this characteristic difference. Thus, the existing SHACL shape creation methods do not perform well for domain-specific biomedical ontology data graphs. Maintaining the quality of biomedical ontology data graphs is crucial to ensure accurate analysis in safety-critical applications like Electronic Health Record (EHR) systems referencing such data graphs. Thus, in this work, we present a novel method to create enhanced SHACL shapes that consider the aforementioned characteristic difference to better validate biomedical ontology data graphs. We leverage the knowledge available from lexical auditing techniques for biomedical ontologies and incorporate this knowledge to create smart SHACL shapes. We also create SHACL shapes (baseline SHACL graph) without incorporating the lexical knowledge of the class names, as is performed by existing methods, and compare the performance of our enhanced SHACL shapes with the baseline SHACL shapes. The results demonstrate that the enhanced SHACL shapes augmented with lexical knowledge of the class names identified 176 violations which the baseline SHACL shapes, void of this lexical knowledge, failed to detect. Thus, the enhanced SHACL shapes presented in this work significantly improve the validation performance of biomedical ontology data graphs, thereby reducing the errors present in such data graphs and ensuring safe use in the life-critical applications referencing them. [ABSTRACT FROM AUTHOR]

Objective Outcomes are important clinical study information. Despite progress in automated extraction of PICO (P opulation, I ntervention, C omparison, and O utcome) entities from PubMed, rarely are these entities encoded by standard terminology to achieve semantic interoperability. This study aims to evaluate the suitability of the Unified Medical Language System (UMLS) and SNOMED-CT in encoding outcome concepts in randomized controlled trial (RCT) abstracts. Materials and Methods We iteratively developed and validated an outcome annotation guideline and manually annotated clinically significant outcome entities in the Results and Conclusions sections of 500 randomly selected RCT abstracts on PubMed. The extracted outcomes were fully, partially, or not mapped to the UMLS via MetaMap based on established heuristics. Manual UMLS browser search was performed for select unmapped outcome entities to further differentiate between UMLS and MetaMap errors. Results Only 44% of 2617 outcome concepts were fully covered in the UMLS, among which 67% were complex concepts that required the combination of 2 or more UMLS concepts to represent them. SNOMED-CT was present as a source in 61% of the fully mapped outcomes. Discussion Domains such as Metabolism and Nutrition, and Infections and Infectious Diseases need expanded outcome concept coverage in the UMLS and MetaMap. Future work is warranted to similarly assess the terminology coverage for P, I, C entities. Conclusion Computational representation of clinical outcomes is important for clinical evidence extraction and appraisal and yet faces challenges from the inherent complexity and lack of coverage of these concepts in UMLS and SNOMED-CT, as demonstrated in this study. [ABSTRACT FROM AUTHOR]

Abstract Background Ontology-based semantic similarity measures based on SNOMED-CT, MeSH, and Gene Ontology are being extensively used in many applications in biomedical text mining and genomics respectively, which has encouraged the development of semantic measures libraries based on the aforementioned ontologies. However, current state-of-the-art semantic measures libraries have some performance and scalability drawbacks derived from their ontology representations based on relational databases, or naive in-memory graph representations. Likewise, a recent reproducible survey on word similarity shows that one hybrid IC-based measure which integrates a shortest-path computation sets the state of the art in the family of ontology-based semantic measures. However, the lack of an efficient shortest-path algorithm for their real-time computation prevents both their practical use in any application and the use of any other path-based semantic similarity measure. Results To bridge the two aforementioned gaps, this work introduces for the first time an updated version of the HESML Java software library especially designed for the biomedical domain, which implements the most efficient and scalable ontology representation reported in the literature, together with a new method for the approximation of the Dijkstra’s algorithm for taxonomies, called Ancestors-based Shortest-Path Length (AncSPL), which allows the real-time computation of any path-based semantic similarity measure. Conclusions We introduce a set of reproducible benchmarks showing that HESML outperforms by several orders of magnitude the current state-of-the-art libraries in the three aforementioned biomedical ontologies, as well as the real-time performance and approximation quality of the new AncSPL shortest-path algorithm. Likewise, we show that AncSPL linearly scales regarding the dimension of the common ancestor subgraph regardless of the ontology size. Path-based measures based on the new AncSPL algorithm are up to six orders of magnitude faster than their exact implementation in large ontologies like SNOMED-CT and GO. Finally, we provide a detailed reproducibility protocol and dataset as supplementary material to allow the exact replication of all our experiments and results.

Background: Allergy code systems are essential for safety and medical information interoperability. However, current terminology systems lack allergens unique to Japan., Objective: This study established a code system encompassing Japanese food and non-food/non-medication allergens (JFAGY), and developed a meta-code system for integration with existing drug code systems. The practicality and limitations of the JFAGY were assessed by profiling HL7 FHIR allergy intolerance., Methods: Allergen terms were selected based on the Standard Commodity Classification of Japan. Additional terms were extracted from clinical guidelines and public documents. For non-food, non-medication allergens, terms from the clinical guidelines were manually compiled to conform to a classification hierarchy. To validate the coverage of the developed food allergen code system, we extracted 823 unique food allergens, totaling 12,027 entries, from two years of electronic health records (EHRs) and performed manual mapping to the code system., Results: In total, 1,123 food and 607 non-food/non-medication allergen terms were included. The three-digit meta-code system comprises an identifier for coding systems, code length, and allergen categories. The codes allowed the determination of hierarchical relationships between any two terms. The Japanese allergy intolerance value set was developed and bound to the allergy intolerance code. Of the food allergens extracted from EHRs, 62.9% corresponded to unique codes, 6.1% to multiple codes, and 31.0% were unmapped, accounting for 91.5%, 1.9%, and 6.6% of entries, respectively., Conclusions: The JFAGY encompasses Japanese-specific food and non-food/non-medication allergens, enabling hierarchy determination between two terms, and playing a critical role in medical safety. When utilizing the JFAGY with the FHIR allergy intolerance resource, an FHIR extension must be included to denote a denied allergy., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Yoshimasa Kawazoe reports financial support was provided by EM Systems, Inc. Yoshimasa Kawazoe reports financial support was provided by EPNextS, Inc. Yoshimasa Kawazoe reports financial support was provided by MRP CO., LTD. Yoshimasa Kawazoe reports financial support was provided by SHIP HEALTHCARE HOLDINGS, INC. Yoshimasa Kawazoe reports financial support was provided by SoftBank Corp. Yoshimasa Kawazoe reports financial support was provided by NEC Corporation. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Abstract Background Natural language processing (NLP) and text mining technologies for the extraction and indexing of chemical and drug entities are key to improving the access and integration of information from unstructured data such as biomedical literature. Methods In this paper we evaluate two important tasks in NLP: the named entity recognition (NER) and Entity indexing using the SNOMED-CT terminology. For this purpose, we propose a combination of word embeddings in order to improve the results obtained in the PharmaCoNER challenge. Results For the NER task we present a neural network composed of BiLSTM with a CRF sequential layer where different word embeddings are combined as an input to the architecture. A hybrid method combining supervised and unsupervised models is used for the concept indexing task. In the supervised model, we use the training set to find previously trained concepts, and the unsupervised model is based on a 6-step architecture. This architecture uses a dictionary of synonyms and the Levenshtein distance to assign the correct SNOMED-CT code. Conclusion On the one hand, the combination of word embeddings helps to improve the recognition of chemicals and drugs in the biomedical literature. We achieved results of 91.41% for precision, 90.14% for recall, and 90.77% for F1-score using micro-averaging. On the other hand, our indexing system achieves a 92.67% F1-score, 92.44% for recall, and 92.91% for precision. With these results in a final ranking, we would be in the first position.

Heterogeneity is a problem in storing and exchanging data in a digital health information system (HIS) following semantic and structural integrity. The existing literature shows different methods to overcome this problem. Fast healthcare interoperable resources (FHIR) as a structural standard may explain other information models, (e.g., personal, physiological, and behavioral data from heterogeneous sources, such as activity sensors, questionnaires, and interviews) with semantic vocabularies, (e.g., Systematized Nomenclature of Medicine—Clinical Terms (SNOMED-CT)) to connect personal health data to an electronic health record (EHR). We design and develop an intuitive health coaching (eCoach) smartphone application to prove the concept. We combine HL7 FHIR and SNOMED-CT vocabularies to exchange personal health data in JavaScript object notion (JSON). This study explores and analyzes our attempt to design and implement a structurally and logically compatible tethered personal health record (PHR) that allows bidirectional communication with an EHR. Our eCoach prototype implements most PHR-S FM functions as an interoperability quality standard. Its end-to-end (E2E) data are protected with a TSD (Services for Sensitive Data) security mechanism. We achieve 0% data loss and 0% unreliable performances during data transfer between PHR and EHR. Furthermore, this experimental study shows the effectiveness of FHIR modular resources toward flexible management of data components in the PHR (eCoach) prototype. [ABSTRACT FROM AUTHOR]

In this study representation of chemical substances in IDMP is reviewed, with an exploration of aggregation levels for substance used in the virtual drug data models of RxNorm, SNOMED-CT, ATC/INN, and the Belgian SAM database, for products with a single substance and combinations of substances. Active moiety and available solid states forms are explored for diclofenac, amoxicillin, carbamazepine, amlodipine, with regard to their representation in coding systems such as WHODrug, SMS, UNII, CAS, and SNOMED-CT. By counting the number of medicinal products in Belgium for amlodipine in each level of aggregation, concepts for grouper of substances and two levels of grouper of medicinal products are illustrated. Recommendations are made for the further development of IDMP and its link to international drug classifications. [ABSTRACT FROM AUTHOR]

Background: Ontology-based semantic similarity measures based on SNOMED-CT, MeSH, and Gene Ontology are being extensively used in many applications in biomedical text mining and genomics respectively, which has encouraged the development of semantic measures libraries based on the aforementioned ontologies. However, current state-of-the-art semantic measures libraries have some performance and scalability drawbacks derived from their ontology representations based on relational databases, or naive in-memory graph representations. Likewise, a recent reproducible survey on word similarity shows that one hybrid IC-based measure which integrates a shortest-path computation sets the state of the art in the family of ontology-based semantic measures. However, the lack of an efficient shortest-path algorithm for their real-time computation prevents both their practical use in any application and the use of any other path-based semantic similarity measure. Results: To bridge the two aforementioned gaps, this work introduces for the first time an updated version of the HESML Java software library especially designed for the biomedical domain, which implements the most efficient and scalable ontology representation reported in the literature, together with a new method for the approximation of the Dijkstra's algorithm for taxonomies, called Ancestors-based Shortest-Path Length (AncSPL), which allows the real-time computation of any path-based semantic similarity measure. Conclusions: We introduce a set of reproducible benchmarks showing that HESML outperforms by several orders of magnitude the current state-of-the-art libraries in the three aforementioned biomedical ontologies, as well as the real-time performance and approximation quality of the new AncSPL shortest-path algorithm. Likewise, we show that AncSPL linearly scales regarding the dimension of the common ancestor subgraph regardless of the ontology size. Path-based measures based on the new AncSPL algorithm are up to six orders of magnitude faster than their exact implementation in large ontologies like SNOMED-CT and GO. Finally, we provide a detailed reproducibility protocol and dataset as supplementary material to allow the exact replication of all our experiments and results. [ABSTRACT FROM AUTHOR]

Background: Natural language processing (NLP) and text mining technologies for the extraction and indexing of chemical and drug entities are key to improving the access and integration of information from unstructured data such as biomedical literature. Methods: In this paper we evaluate two important tasks in NLP: the named entity recognition (NER) and Entity indexing using the SNOMED-CT terminology. For this purpose, we propose a combination of word embeddings in order to improve the results obtained in the PharmaCoNER challenge. Results: For the NER task we present a neural network composed of BiLSTM with a CRF sequential layer where different word embeddings are combined as an input to the architecture. A hybrid method combining supervised and unsupervised models is used for the concept indexing task. In the supervised model, we use the training set to find previously trained concepts, and the unsupervised model is based on a 6-step architecture. This architecture uses a dictionary of synonyms and the Levenshtein distance to assign the correct SNOMED-CT code. Conclusion: On the one hand, the combination of word embeddings helps to improve the recognition of chemicals and drugs in the biomedical literature. We achieved results of 91.41% for precision, 90.14% for recall, and 90.77% for F1-score using micro-averaging. On the other hand, our indexing system achieves a 92.67% F1-score, 92.44% for recall, and 92.91% for precision. With these results in a final ranking, we would be in the first position. [ABSTRACT FROM AUTHOR]

Clinical information system (CIS) whose core component is the electronic medical record (EMR), is critical to efficient health care delivery. However, patient mobility and CIS heterogeneity have strengthened the interoperability problem of EMRs. Although ontology is a panacea to this problem, creating interoperable EMR ontology is still esoteric. This paper explicates how an interoperable EMR ontology was crafted for a tertiary health facility in Nigeria. The ontology named EMRONT was created using the Noy and McGuiness' methodology and Protégé. EMRONT has been evaluated for quality using competency questions and OntoQA and subsequently refined for interoperability by manual mapping to both Health Level 7 (HL7) standard and SNOMED-CT. EMRONT has been further refined for interoperability by semi-automatic mapping to SNOMED-CT using Snoggle. This paper has made exoteric the hitherto esoteric craft of building interoperable EMR ontology and exposed how semiautomatic mapping of EMR ontology to SNOMED is done in practice. [ABSTRACT FROM AUTHOR]

Abstract Background A Cardiac-centered Frailty Ontology can be an important foundation for using NLP to assess patient frailty. Frailty is an important consideration when making patient treatment decisions, particularly in older adults, those with a cardiac diagnosis, or when major surgery is a consideration. Clinicians often report patient’s frailty in progress notes and other documentation. Frailty is recorded in many different ways in patient records and many different validated frailty-measuring instruments are available, with little consistency across instruments. We specifically explored concepts relevant to decisions regarding cardiac interventions. We based our work on text found in a large corpus of clinical notes from the Department of Veterans Affairs (VA) national Electronic Health Record (EHR) database. Results The full ontology has 156 concepts, with 246 terms. It includes 86 concepts we expect to find in clinical documents, with 12 qualifier values. The remaining 58 concepts represent hierarchical groups (e.g., physical function findings). Our top-level class is clinical finding, which has children clinical history finding, instrument finding, and physical examination finding, reflecting the OGMS definition of clinical finding. Instrument finding is any score found for the existing frailty instruments. Within our ontology, we used SNOMED-CT concepts where possible. Some of the 86 concepts we expect to find in clinical documents are associated with the properties like ability interpretation. The concept ability to walk can either be able, assisted or unable. Each concept-property level pairing gets a different frailty score. Each scored concept received three scores: a frailty score, a relevance to cardiac decisions score, and a likelihood of resolving after the recommended intervention score. The ontology includes the relationship between scores from ten frailty instruments and frailty as assessed using ontology concepts. It also included rules for mapping ontology elements to instrument items for three common frailty assessment instruments. Ontology elements are used in two clinical NLP systems. Conclusions We developed and validated a Cardiac-centered Frailty Ontology, which is a machine-interoperable description of frailty that reflects all the areas that clinicians consider when deciding which cardiac intervention will best serve the patient as well as frailty indications generally relevant to medical decisions. The ontology owl file is available on Bioportal at http://bioportal.bioontology.org/ontologies/CCFO.

Heterogeneity is a problem in storing and exchanging data in a digital health information system (HIS) following semantic and structural integrity. The existing literature shows different methods to overcome this problem. Fast healthcare interoperable resources (FHIR) as a structural standard may explain other information models, (e.g., personal, physiological, and behavioral data from heterogeneous sources, such as activity sensors, questionnaires, and interviews) with semantic vocabularies, (e.g., Systematized Nomenclature of Medicine—Clinical Terms (SNOMED-CT)) to connect personal health data to an electronic health record (EHR). We design and develop an intuitive health coaching (eCoach) smartphone application to prove the concept. We combine HL7 FHIR and SNOMED-CT vocabularies to exchange personal health data in JavaScript object notion (JSON). This study explores and analyzes our attempt to design and implement a structurally and logically compatible tethered personal health record (PHR) that allows bidirectional communication with an EHR. Our eCoach prototype implements most PHR-S FM functions as an interoperability quality standard. Its end-to-end (E2E) data are protected with a TSD (Services for Sensitive Data) security mechanism. We achieve 0% data loss and 0% unreliable performances during data transfer between PHR and EHR. Furthermore, this experimental study shows the effectiveness of FHIR modular resources toward flexible management of data components in the PHR (eCoach) prototype.

Abstract Background In recent years, Twitter has been applied to monitor diseases through its facility to monitor users’ comments and concerns in real-time. The analysis of tweets for disease mentions should reflect not only user specific concerns but also disease outbreaks. This requires the use of standard terminological resources and can be focused on selected geographic locations. In our study, we differentiate between hospital and airport locations to better distinguish disease outbreaks from background mentions of disease concerns. Results Our analysis covers all geolocated tweets over a 6 months time period, uses SNOMED-CT as a standard medical terminology, and explores language patterns (as well as MetaMap) to identify mentions of diseases in reference to the geolocation of tweets. Contrary to our expectation, hospital and airport geolocations are not suitable to collect significant portions of tweets concerned with disease outcomes. Overall, geolocated tweets exposed a large number of messages commenting on disease-related news articles. Furthermore, the geolocated messages exposed an over-representation of non-communicable diseases in contrast to infectious diseases. Conclusions Our findings suggest that disease mentions on Twitter not only serve the purpose to share personal statements but also to share concerns about news articles. In particular, our assumption about the relevance of hospital and airport geolocations for an increased frequency of diseases mentions has not been met. To further address the linguistic cues, we propose the study of health forums to understand how a change in medium affects the language applied by the users. Finally, our research on the language use may provide essential clues to distinguish complementary trends in the use of language in Twitter when analysing health-related topics.

Data integration is an important task in medical informatics and highly impacts the gain out of existing health information data. These tasks are using implemented as extract transform and load processes. By introducing HL7 FHIR as an intermediate format, our aim was to integrate heterogeneous data from a German pulmonary hypertension registry into an OMOP Common Data Model. First, domain knowledge experts defined a common parameter set, which was subsequently mapped to standardized terminologies like LOINC or SNOMED-CT. Data was extracted as HL7 FHIR Bundle to be transformed to OMOP CDM by using XSLT. We successfully transformed the majority of data elements to the OMOP CDM in a feasible time. [ABSTRACT FROM AUTHOR]

The present work provides a real-world case of the connection process of a hospital, 12 de Octubre University Hospital in Spain, to the TriNetX research network, transforming a compilation of disparate sources into a single harmonized repository which is automatically refreshed every day. It describes the different integration phases: terminology core datasets, specialized sources and eventually automatic refreshment. It also explains the work performed on semantic normalization of the involved clinical terminologies; as well as the resulting benefits the InSite platform services have enabled in the form of research opportunities for the hospital. [ABSTRACT FROM AUTHOR]

SNODENT, a dental diagnostic terminology, or any of its subsets, should be evaluated for content coverage and completeness. The objective of this study was to determine how well a subset of SNODENT designed for general dentistry met the needs of dental practitioners. Participants were asked to locate their written diagnosis for tooth conditions among a subset of SNODENT terms uploaded into an electronic dental record. Investigators found that 65 percent of providers' original written diagnoses were in "agreement" with their selected SNODENT dental diagnostic subset concept(s), highlighting the effectiveness of using terminology subsets at the point of care. Additional education and training for dental providers on appropriate selection and use of SNODENT terms to document patient care is needed. Results provide valuable insights on useful revisions, updates and expansions to the SNODENT terminology. [ABSTRACT FROM AUTHOR]

In an increasingly interconnected health care system, laboratory medicine can facilitate diagnosis and treatment of patients effectively. This article describes necessary changes and points to potential challenges on a technical, content, and organizational level. As a technical precondition, electronic laboratory reports have to become machine-readable and interpretable. Terminologies such as Logical Observation Identifiers Names and Codes (LOINC), Nomenclature for Properties and Units (NPU), Unified Code for Units of Measure (UCUM), and SNOMED-CT can lead to the necessary semantic interoperability. Even if only single “atomized” results of the whole report are extracted, the necessary information for correct interpretation must be available. Therefore, interpretive comments, e.g., concerns about an increased measurement uncertainty must be electronically attached to every affected measurement result. Standardization of laboratory analyses with traceable standards and reference materials will enable knowledge transfer and safe interpretation of laboratory analyses from multiple laboratories. In an interconnected health care system, laboratories should strive to transform themselves into a data hub that not only receives samples but also extensive information about the patient. On that basis, they can return measurement results enriched with high-quality interpretive comments tailored to the individual patient and unlock the full potential of laboratory medicine.

Objective: The use of standardized structured reports (SSR) and suitable terminologies like SNOMED-CT can enhance data retrieval and analysis, fostering large-scale studies and collaboration. However, the still large prevalence of narrative reports in our laboratories warrants alternative and automated labeling approaches. In this project, natural language processing (NLP) methods were used to associate SNOMED-CT codes to structured and unstructured reports from an Italian Digital Pathology Department., Methods: Two NLP-based automatic coding systems (support vector machine, SVM, and long-short term memory, LSTM) were trained and applied to a series of narrative reports., Results: The 1163 cases were tested with both algorithms, showing good performances in terms of accuracy, precision, recall, and F1 score, with SVM showing slightly better performances as compared to LSTM (0.84, 0.87, 0.83, 0.82 vs 0.83, 0.85, 0.83, 0.82, respectively). The integration of an explainability allowed identification of terms and groups of words of importance, enabling fine-tuning, balancing semantic meaning and model performance., Conclusions: AI tools allow the automatic SNOMED-CT labeling of the pathology archives, providing a retrospective fix to the large lack of organization of narrative reports., (Copyright © 2023 Società Italiana di Anatomia Patologica e Citopatologia Diagnostica, Divisione Italiana della International Academy of Pathology.)

The shortest path between two concepts in a taxonomic ontology is commonly used to represent the semantic distance between concepts in edge-based semantic similarity measures. In the past, edge counting, which is simple and intuitive and has low computational complexity, was considered the default method for path computation. However, a large lexical taxonomy, such as WordNet, has irregular link densities between concepts due to its broad domain, but edge counting-based path computation is powerless for this non-uniformity problem. In this paper, we advocate that the path computation can be separated from edge-based similarity measures and can form various general computing models. Therefore, to solve the problem of the non-uniformity of concept density in a large taxonomic ontology, we propose a new path computing model based on the compensation of local area density of concepts, which is equal to the number of direct hyponyms of the subsumers for concepts in the shortest path. This path model considers the local area density of concepts as an extension of the edge counting-based path according to the information theory. This model is a general path computing model and can be applied in various edge-based similarity approaches. The experimental results show that the proposed path model improves the average optimal correlation between edge-based measures and human judgments on the Miller and Charles benchmark for WordNet from less than 0.79 to more than 0.86, on the Pedersenet al. benchmark (average of both Physician and Coder) for SNOMED-CT from less than 0.75 to more than 0.82, and it has a large advantage in efficiency compared with information content computation in a dynamic ontology, thereby successfully improving the edge-based similarity measure as an excellent method with high performance and high efficiency. [ABSTRACT FROM AUTHOR]

With the widespread dissemination of picture archiving and communication systems (PACSs) in hospitals, the amount of imaging data is rapidly increasing. Effective image retrieval systems are required to manage these complex and large image databases. The authors reviewed the past development and the present state of medical image retrieval systems including text-based and content-based systems. In order to provide a more effective image retrieval service, the intelligent content-based retrieval systems combined with semantic systems are required.

The relevance of health data research on real world data (RWD) is increasing. To prepare national RWD for international research, harmonization with standard terminologies is required. In this paper, we evaluate to what extent the German OPS vocabulary in OHDSI covers codes present in RWD and mappings to SNOMED-CT. The evaluation identified a mapping gap of 21.1% in the RWD set. [ABSTRACT FROM AUTHOR]

The appropriate categorisation of written information by health professionals is very important to guarantee its accessibility. Unfortunately, the information technology tools that support professionals on that task imply a heavy workload, so that the responsibility for categorising the written content is often delegated to administrative staff. Well-known health ontologies such as SNOMED-CT or MeSH provide a representation of the clinical contents to be used by the information systems. This research proposes a computer based method to automatically extract and code the diagnostics, procedures and treatments according to health ontologies. A Knowledge Management System based on an extended version of Drupal is used to implement and evaluate this proposal. Results provide a positive evidence on the application of the method to support medical professionals.

In order to provide more effective and personalized healthcare services to patients and healthcare professionals, intelligent active knowledge management and reasoning systems with semantic interoperability are needed. Technological developments have changed ubiquitous healthcare making it more semantically interoperable and individual patient-based; however, there are also limitations to these methodologies. Based upon an extensive review of international literature, this paper describes two technological approaches to semantically interoperable electronic health records for ubiquitous healthcare data management: the ontology-based model and the information, or openEHR archetype model, and the link to standard terminologies such as SNOMED-CT.

Background Rich Site Summary (RSS) feeds are a method for disseminating and syndicating the contents of a website using extensible mark-up language (XML). The Primary Care Electronic Library (PCEL) distributes recent additions to the site in the form of an RSS feed. When new resources are added to PCEL, they are manually assigned medical subject headings (MeSH terms), which are then automatically mapped to SNOMED-CT terms using the Unified Medical Language System (UMLS) Metathesaurus. The library is thus searchable using MeSH or SNOMED-CT. Our syndicate partner wished to have remote access to PCEL coronary heart disease (CHD) information resources based on SNOMED-CT search terms. Objective To pilot the supply of relevant information resources in response to clinically coded requests, using RSS syndication for transmission between web servers. Method Our syndicate partner provided a list of CHD SNOMED-CTterms to its end-users, a list which was coded according toUMLS specifications. When the end-user requested relevant information resources, this request was relayed from our syndicate partner's web server to the PCEL web server. The relevant resources were retrieved from the PCEL MySQL database. This database is accessed using a server side scripting language (PHP), which enables the production of dynamic RSS feeds on the basis of Source Asserted Identifiers (CODEs) contained in UMLS. Results Retrieving resources using SNOMED-CT terms using syndication can be used to build a functioning application. The process from request to display of syndicated resources took less than one second. Conclusion The results of the pilot illustrate that it is possible to exchange data between servers using RSS syndication. This method could be utilised dynamically to supply digital library resources to a clinical system with SNOMED-CTdata used as the standard of reference.

Objective: We assess the potential of exploiting stopwords in biomedical concept names to complete the logical definitions of concepts that are not sufficiently defined.Methods: Concepts containing stopwords are selected from the Disorder hierarchy of Systematized NOmenclature of MEDicine (SNOMED-CT). SNOMED-CT consists of two types of concepts: Fully Defined (FD) concepts which are sufficiently defined and Partially Defined (PD) concepts which are not sufficiently defined. In this work, FD concepts containing stopwords are treated as a source of ground truth to complete the definitions of, lexically and semantically similar, PD concepts. FD and PD concepts are lexically and semantically analysed to create sample-sets. Mandatory attribute-relationships are calculated by using an intersection-set logic for each FD sample-set. PD sample-sets are audited against this mandatory attribute-relationship template to identify inconsistencies in modelling styles and potentially missing attribute-relationships.Results: Lexical and semantic patterns around 11 stopwords were analysed. 26 sample-sets were extracted for the 11 stopwords. Mandatory attribute-relationships were identified for 24 of the 26 sample-sets. The method identified 62.5% - 72.22% of the PD concepts, containing the stopwords in and due to, to be inconsistent in their modelling style and potentially missing at least one attribute-relationship according to the created template. [ABSTRACT FROM AUTHOR]

The determination of semantic similarity between word pairs is an important task in text understanding that supports the processing, classification and structuring of textual resources. In the field of biomedical, semantic similarity measures have been the focus of much research by exploiting knowledge sources such as domain ontologies. SNOMED-CT as a main biomedical ontology provides a global and broad hierarchical terminology for clinical data storage, encoding, and the retrieval of health and diseases information. In this study, we classified the measures proposed in biomedical domain and used SNOMED-CT as an input ontology. We also examined the studies that evaluated these methods using biomedical benchmarks. Regarding this, three major databases, including Science Direct, Springer and IEEE were selected to extract studies which proposed similarity measures and used SNOMED-CT as a knowledge source. The purpose of this study is to provide the reader with the understanding about the application of semantic similarity measures in biomedical domain using SNOMED-CT, and to gain a clear insight about the performance of these methods. This study also supports researchers and practitioners in effectively adapting semantic similarity measures in SNOMED-CT and provides an insight into its state-of-the-art. [ABSTRACT FROM AUTHOR]

Clinical Decision Support Systems (CDSS) help to improve health care and reduce costs. However, the lack of knowledge management and modelling hampers their maintenance and reuse. Current EHR standards and terminologies can allow the semantic representation of the data and knowledge of CDSS systems boosting their interoperability, reuse and maintenance. This paper presents the modelling process of respiratory conditions' symptoms and signs by a multidisciplinary team of clinicians and information architects with the help of openEHR, SNOMED and clinical information modelling tools for a CDSS. The information model of the CDSS was defined by means of an archetype and the knowledge model was implemented by means of an SNOMED-CT based ontology. [ABSTRACT FROM AUTHOR]