Structural analysis and auditing of SNOMED hierarchies using abstraction networks

SNOMED is one of the leading healthcare terminologies being used worldwide. Due to its sheer volume and continuing expansion, it is inevitable that errors will make their way into SNOMED. Thus, quality assurance is an important part of its maintenance cycle. A structural approach is presented in this dissertation, aiming at developing automated techniques that can aid auditors in the discovery of terminology errors more effectively and efficiently. Large SNOMED hierarchies are partitioned, based primarily on their relationships patterns, into concept groups of more manageable sizes. Three related abstraction networks with respect to a SNOMED hierarchy, namely the area taxonomy, partial-area taxonomy, and disjoint partial-area taxonomy, are derived programmatically from the partitions. Altogether they afford high-level abstraction views of the underlying hierarchy, each with different granularity. The area taxonomy gives a global structural view of a SNOMED hierarchy, while the partial-area taxonomy focuses more on the semantic uniformity and hierarchical proximity of concepts. The disjoint partial-area taxonomy is devised as an enhancement of the partial-area taxonomy and is based on the partition of the entire collection of so-called overlapping concepts into singly-rooted groups. The taxonomies are exploited as the basis for a number of systematic auditing regimens, with a theme that complex concepts are more error-prone and require special attention in auditing activities. In general, group-based auditing is promoted to achieve a more efficient review within semantically uniform groups. Certain concept groups in the different taxonomies are deemed “complex” according to various criteria and thus deserve focused auditing. Examples of these include strict inheritance regions in the partial-area taxonomy and overlapping partial-areas in the disjoint partial-area taxonomy. Multiple hypotheses are formulated to characterize the error distributions and ratios with respect to different concept groups presented by the taxonomies, and thus further establish their efficacy as vehicles for auditing. The methodologies are demonstrated using SNOMED’s Specimen hierarchy as the test bed. Auditing results are reported and analyzed to assess the hypotheses. With the use of the double bootstrap and Fisher’s exact test (two-tailed), the aforementioned hypotheses are confirmed. Auditing on various complex concept groups based on the taxonomies is shown to yield a statistically significant higher proportion of errors

Auditing SNOMED CT Hierarchical Relations Based on Lexical Features of Concepts in Non-Lattice Subgraphs

Objective—We introduce a structural-lexical approach for auditing SNOMED CT using a combination of non-lattice subgraphs of the underlying hierarchical relations and enriched lexical attributes of fully specified concept names. Our goal is to develop a scalable and effective approach that automatically identifies missing hierarchical IS-A relations. Methods—Our approach involves 3 stages. In stage 1, all non-lattice subgraphs of SNOMED CT’s IS-A hierarchical relations are extracted. In stage 2, lexical attributes of fully-specified concept names in such non-lattice subgraphs are extracted. For each concept in a non-lattice subgraph, we enrich its set of attributes with attributes from its ancestor concepts within the non-lattice subgraph. In stage 3, subset inclusion relations between the lexical attribute sets of each pair of concepts in each non-lattice subgraph are compared to existing IS-A relations in SNOMED CT. For concept pairs within each non-lattice subgraph, if a subset relation is identified but an IS-A relation is not present in SNOMED CT IS-A transitive closure, then a missing IS-A relation is reported. The September 2017 release of SNOMED CT (US edition) was used in this investigation. Results—A total of 14,380 non-lattice subgraphs were extracted, from which we suggested a total of 41,357 missing IS-A relations. For evaluation purposes, 200 non-lattice subgraphs were randomly selected from 996 smaller subgraphs (of size 4, 5, or 6) within the “Clinical Finding” and “Procedure” sub-hierarchies. Two domain experts confirmed 185 (among 223) missing IS-A relations, a precision of 82.96%. Conclusions—Our results demonstrate that analyzing the lexical features of concepts in non-lattice subgraphs is an effective approach for auditing SNOMED CT

Relationship auditing of the FMA ontology

The Foundational Model of Anatomy (FMA) ontology is a domain reference ontology based on a disciplined modeling approach. Due to its large size, semantic complexity and manual data entry process, errors and inconsistencies are unavoidable and might remain within the FMA structure without detection. In this paper, we present computable methods to highlight candidate concepts for various relation- ship assignment errors. The process starts with locating structures formed by transitive structural relationships (part_of, tributary_of, branch_of) and examine their assignments in the context of the IS-A hierarchy. The algorithms were designed to detect five major categories of possible incorrect relationship assignments: circular, mutually exclusive, redundant, inconsistent, and missed entries. A domain expert reviewed samples of these presumptive errors to confirm the findings. Seven thousand and fifty-two presumptive errors were detected, the largest proportion related to part_of relationship assignments. The results highlight the fact that errors are unavoidable in complex ontologies and that well designed algorithms can help domain experts to focus on concepts with high likelihood of errors and maximize their effort to ensure consistency and reliability. In the future similar methods might be integrated with data entry processes to offer real-time error detection

Mining Non-Lattice Subgraphs for Detecting Missing Hierarchical Relations and Concepts in SNOMED CT

Objective: Quality assurance of large ontological systems such as SNOMED CT is an indispensable part of the terminology management lifecycle. We introduce a hybrid structural-lexical method for scalable and systematic discovery of missing hierarchical relations and concepts in SNOMED CT. Material and Methods: All non-lattice subgraphs (the structural part) in SNOMED CT are exhaustively extracted using a scalable MapReduce algorithm. Four lexical patterns (the lexical part) are identified among the extracted non-lattice subgraphs. Non-lattice subgraphs exhibiting such lexical patterns are often indicative of missing hierarchical relations or concepts. Each lexical pattern is associated with a potential specific type of error. Results: Applying the structural-lexical method to SNOMED CT (September 2015 US edition), we found 6801 non-lattice subgraphs that matched these lexical patterns, of which 2046 were amenable to visual inspection. We evaluated a random sample of 100 small subgraphs, of which 59 were reviewed in detail by domain experts. All the subgraphs reviewed contained errors confirmed by the experts. The most frequent type of error was missing is-a relations due to incomplete or inconsistent modeling of the concepts. Conclusions: Our hybrid structural-lexical method is innovative and proved effective not only in detecting errors in SNOMED CT, but also in suggesting remediation for these errors

Detecting Role Errors in the Gene Hierarchy of the NCI Thesaurus

Gene terminologies are playing an increasingly important role in the ever-growing field of genomic research. While errors in large, complex terminologies are inevitable, gene terminologies are even more susceptible to them due to the rapid growth of genomic knowledge and the nature of its discovery. It is therefore very important to establish quality-assurance protocols for such genomic-knowledge repositories. Different kinds of terminologies oftentimes require auditing methodologies adapted to their particular structures. In light of this, an auditing methodology tailored to the characteristics of the NCI Thesaurus’s (NCIT’s) Gene hierarchy is presented. The Gene hierarchy is of particular interest to the NCIT’s designers due to the primary role of genomics in current cancer research. This multiphase methodology focuses on detecting role-errors, such as missing roles or roles with incorrect or incomplete target structures, occurring within that hierarchy. The methodology is based on two kinds of abstraction networks, called taxonomies, that highlight the role distribution among concepts within the IS-A (subsumption) hierarchy. These abstract views tend to highlight portions of the hierarchy having a higher concentration of errors. The errors found during an application of the methodology are reported. Hypotheses pertaining to the efficacy of our methodology are investigated

Extensions of SNOMED taxonomy abstraction networks supporting auditing and complexity analysis

The Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT) has been widely used as a standard terminology in various biomedical domains. The enhancement of the quality of SNOMED contributes to the improvement of the medical systems that it supports. In previous work, the Structural Analysis of Biomedical Ontologies Center (SABOC) team has defined the partial-area taxonomy, a hierarchical abstraction network consisting of units called partial-areas. Each partial-area comprises a set of SNOMED concepts exhibiting a particular relationship structure and being distinguished by a unique root concept. In this dissertation, some extensions and applications of the taxonomy framework are considered. Some concepts appearing in multiple partial-areas have been designated as complex due to the fact that they constitute a tangled portion of a hierarchy and can be obstacles to users trying to gain an understanding of the hierarchy’s content. A methodology for partitioning the entire collection of these so-called overlapping complex concepts into singly-rooted groups was presented. A novel auditing methodology based on an enhanced abstraction network is described. In addition, the existing abstraction network relies heavily on the structure of the outgoing relationships of the concepts. But some of SNOMED hierarchies (or subhierarchies) serve only as targets of relationships, with few or no outgoing relationships of their own. This situation impedes the applicability of the abstraction network. To deal with this problem, a variation of the above abstraction network, called the converse abstraction network (CAN) is defined and derived automatically from a given SNOMED hierarchy. An auditing methodology based on the CAN is formulated. Furthermore, a preliminary study of the complementary use of the abstraction network in description logic (DL) for quality assurance purposes pertaining to SNOMED is presented. Two complexity measures, a structural complexity measure and a hierarchical complexity measure, based on the abstraction network are introduced to quantify the complexity of a SNOMED hierarchy. An extension of the two measures is also utilized specifically to track the complexity of the versions of the SNOMED hierarchies before and after a sequence of auditing processes

Structural indicators for effective quality assurance of snomed ct

The Standardized Nomenclature of Medicine -- Clinical Terms (SNOMED CT -- further abbreviated as SCT) has been endorsed as a premier clinical terminology by many national and international organizations. The US Government has chosen SCT to play a significant role in its initiative to promote Electronic Health Record (EH R) country-wide. However, there is evidence suggesting that, at the moment, SCT is not optimally modeled for its intended use by healthcare practitioners. There is a need to perform quality assurance (QA) of SCT to help expedite its use as a reference terminology for clinical purposes as planned for EH R use. The central theme of this dissertation is to define a group-based auditing methodology to effectively identify concepts of SCT that require QA. As such, similarity sets are introduced which are groups of concepts that are lexically identical except for one word. Concepts in a similarity set are expected to be modeled in a consistent way. If not, the set is considered to be inconsistent and submitted for review by an auditor. Initial studies found 38% of such sets to be inconsistent. The effectiveness of these sets is further improved through the use of three structural indicators. Using such indicators as the number of parents, relationships and role groups, up to 70% of the similarity sets and 32.6% of the concepts are found to exhibit inconsistencies. Furthermore, positional similarity sets, which are similarity sets with the same position of the differing word in the concept’s terms, are introduced to improve the likelihood of finding errors at the concept level. This strictness in the position of the differing word increases the lexical similarity between the concepts of a set thereby increasing the contrast between lexical similarities and modeling differences. This increase in contrast increases the likelihood of finding inconsistencies. The effectiveness of positional similarity sets in finding inconsistencies is further improved by using the same three structural indicators as discussed above in the generation of these sets. An analysis of 50 sample sets with differences in the number of relationships reveal 41.6% of the concepts to be inconsistent. Moreover, a study is performed to fully automate the process of suggesting attributes to enhance the modeling of SCT concepts using positional similarity sets. A technique is also used to automatically suggest the corresponding target values. An analysis of 50 sample concepts show that, of the 103 suggested attributes, 67 are manually confirmed to be correct. Finally, a study is conducted to examine the readiness of SCT problem list (PL) to support meaningful use of EHR. The results show that the concepts in PL suffer from the same issues as general SCT concepts, although to a slightly lesser extent, and do require further QA efforts. To support such efforts, structural indicators in the form of the number of parents and the number of words are shown to be effective in ferreting out potentially problematic concepts in which QA efforts should be focused. A structural indicator to find concepts with synonymy problems is also presented by finding pairs of SCT concepts that map to the same UMLS concept

STRUCTURAL AND LEXICAL METHODS FOR AUDITING BIOMEDICAL TERMINOLOGIES

Biomedical terminologies serve as knowledge sources for a wide variety of biomedical applications including information extraction and retrieval, data integration and management, and decision support. Quality issues of biomedical terminologies, if not addressed, could affect all downstream applications that use them as knowledge sources. Therefore, Terminology Quality Assurance (TQA) has become an integral part of the terminology management lifecycle. However, identification of potential quality issues is challenging due to the ever-growing size and complexity of biomedical terminologies. It is time-consuming and labor-intensive to manually audit them and hence, automated TQA methods are highly desirable. In this dissertation, systematic and scalable methods to audit biomedical terminologies utilizing their structural as well as lexical information are proposed. Two inference-based methods, two non-lattice-based methods and a deep learning-based method are developed to identify potentially missing hierarchical (or is-a) relations, erroneous is-a relations, and missing concepts in biomedical terminologies including the Gene Ontology (GO), the National Cancer Institute thesaurus (NCIt), and SNOMED CT. In the first inference-based method, the GO concept names are represented using set-of-words model and sequence-of-words model, respectively. Inconsistencies derived between hierarchical linked and unlinked concept pairs are leveraged to detect potentially missing or erroneous is-a relations. The set-of-words model detects a total of 5,359 potential inconsistencies in the 03/28/2017 release of GO and the sequence-of-words model detects 4,959. Domain experts’ evaluation shows that the set-of-words model achieves a precision of 53.78% (128 out of 238) and the sequence-of-words model achieves a precision of 57.55% (122 out of 212) in identifying inconsistencies. In the second inference-based method, a Subsumption-based Sub-term Inference Framework (SSIF) is developed by introducing a novel term-algebra on top of a sequence-based representation of GO concepts. The sequence-based representation utilizes the part of speech of concept names, sub-concepts (concept names appearing inside another concept name), and antonyms appearing in concept names. Three conditional rules (monotonicity, intersection, and sub-concept rules) are developed for backward subsumption inference. Applying SSIF to the 10/03/2018 release of GO suggests 1,938 potentially missing is-a relations. Domain experts’ evaluation of randomly selected 210 potentially missing is-a relations shows that SSIF achieves a precision of 60.61%, 60.49%, and 46.03% for the monotonicity, intersection, and sub-concept rules, respectively. In the first non-lattice-based method, lexical patterns of concepts in Non-Lattice Subgraphs (NLSs: graph fragments with a higher tendency to contain quality issues), are mined to detect potentially missing is-a relations and missing concepts in NCIt. Six lexical patterns: containment, union, intersection, union-intersection, inference-contradiction, and inference-union are leveraged. Each pattern indicates a potential specific type of error and suggests a potential type of remediation. This method identifies 809 NLSs exhibiting these patterns in the 16.12d version of NCIt, achieving a precision of 66% (33 out of 50). In the second non-lattice-based method, enriched lexical attributes from concept ancestors are leveraged to identify potentially missing is-a relations in NLSs. The lexical attributes of a concept are inherited in two ways: from ancestors within the NLS, and from all the ancestors. For a pair of concepts without a hierarchical relation, if the lexical attributes of one concept is a subset of that of the other, a potentially missing is-a relation between the two concepts is suggested. This method identifies a total of 1,022 potentially missing is-a relations in the 19.01d release of NCIt with a precision of 84.44% (76 out of 90) for inheriting lexical attributes from ancestors within the NLS and 89.02% (73 out of 82) for inheriting from all the ancestors. For the non-lattice-based methods, similar NLSs may contain similar quality issues, and thus exhaustive examination of NLSs would involve redundant work. A hybrid method is introduced to identify similar NLSs to avoid redundant analyses. Given an input NLS, a graph isomorphism algorithm is used to obtain its structurally identical NLSs. A similarity score between the input NLS and each of its structurally identical NLSs is computed based on semantic similarity between their corresponding concept names. To compute the similarity between concept names, the concept names are converted to vectors using the Doc2Vec document embedding model and then the cosine similarity of the two vectors is computed. All the structurally identical NLSs with a similarity score above 0.85 is considered to be similar to the input NLS. Applying this method to 10 different structures of NLSs in the 02/12/2018 release of GO reveals that 38.43% of these NLSs have at least one similar NLS. Finally, a deep learning-based method is explored to facilitate the suggestion of missing is-a relations in NCIt and SNOMED CT. Concept pairs exhibiting a containment pattern is the focus here. The problem is framed as a binary classification task, where given a pair of concepts, the deep learning model learns to predict whether the two concepts have an is-a relation or not. Positive training samples are existing is-a relations in the terminology exhibiting containment pattern. Negative training samples are concept-pairs without is-a relations that are also exhibiting containment pattern. A graph neural network model is constructed for this task and trained with subgraphs generated enclosing the pairs of concepts in the samples. To evaluate each model trained by the two terminologies, two evaluation sets are created considering newer releases of each terminology as a partial reference standard. The model trained on NCIt achieves a precision of 0.5, a recall of 0.75, and an F1 score of 0.6. The model trained on SNOMED CT achieves a precision of 0.51, a recall of 0.64 and an F1 score of 0.56

Reconocimiento de enfermedades en fichas técnicas de medicamentos y su anotación con SNOMED-CT

La interoperabilidad o habilidad para intercambiar información entre sistemas informáticos es una cuestión de gran importancia en la informática médica. La interoperabilidad influye directamente en la calidad de los sistemas médicos existentes en la práctica clínica, ya que permite que la información se trate de manera eficiente y consistente. Para la comunicación entre sistemas informáticos heterogéneos se necesitan terminologías o diccionarios que representen e identifiquen conceptos médicos de forma única, sin importar el idioma o la forma lingüística en la que aparezcan. Estas terminologías permiten a los sistemas informáticos tener la misma visión del mundo y que la información intercambiada sea entendible. Actualmente, los esfuerzos para la adopción de estas terminologías en la práctica clínica recaen en los profesionales del dominio médico. Los profesionales son los encargados de reconocer conceptos médicos manualmente en documentos del área de la medicina y anotarlos con el código del concepto asociado en la terminología. No existe ningún método automático que permita el reconocimiento de conceptos de un determinado dominio, como por ejemplo las enfermedades, y que posteriormente encuentre el concepto asociado dentro de una terminología con un grado de precisión suficientemente elevado para que pueda ser adoptado en la práctica clínica. En esta tesis de máster se propone un nuevo método para el reconocimiento de enfermedades en fichas técnicas de medicamentos y su posterior mapeo con la terminología médica SNOMED-CT en español. El método utiliza dos nuevas técnicas propuestas en la tesis para cada fase. La nueva técnica para el reconocimiento de enfermedades propuesta está basada en reglas y en diccionarios especializados en medicina. La nueva técnica de mapeo está basada en la generación de las posibles combinaciones lingüísticas en las que puede aparecer la enfermedad para realizar comparaciones exactas de palabras, utilizando las funciones sintácticas de las palabras como guía. El método propuesto se centra en la identificación de enfermedades dentro de la sección de indicaciones terapéuticas de las fichas técnicas de medicamentos

Enrichment of ontologies using machine learning and summarization

Biomedical ontologies are structured knowledge systems in biomedicine. They play a major role in enabling precise communications in support of healthcare applications, e.g., Electronic Healthcare Records (EHR) systems. Biomedical ontologies are used in many different contexts to facilitate information and knowledge management. The most widely used clinical ontology is the SNOMED CT. Placing a new concept into its proper position in an ontology is a fundamental task in its lifecycle of curation and enrichment. A large biomedical ontology, which typically consists of many tens of thousands of concepts and relationships, can be viewed as a complex network with concepts as nodes and relationships as links. This large-size node-link diagram can easily become overwhelming for humans to understand or work with. Adding concepts is a challenging and time-consuming task that requires domain knowledge and ontology skills. IS-A links (aka subclass links) are the most important relationships of an ontology, enabling the inheritance of other relationships. The position of a concept, represented by its IS-A links to other concepts, determines how accurately it is modeled. Therefore, considering as many parent candidate concepts as possible leads to better modeling of this concept. Traditionally, curators rely on classifiers to place concepts into ontologies. However, this assumes the accurate relationship modeling of the new concept as well as the existing concepts. Since many concepts in existing ontologies, are underspecified in terms of their relationships, the placement by classifiers may be wrong. In cases where the curator does not manually check the automatic placement by classifier programs, concepts may end up in wrong positions in the IS-A hierarchy. A user searching for a concept, without knowing its precise name, would not find it in its expected location. Automated or semi-automated techniques that can place a concept or narrow down the places where to insert it, are highly desirable. Hence, this dissertation is addressing the problem of concept placement by automatically identifying IS-A links and potential parent concepts correctly and effectively for new concepts, with the assistance of two powerful techniques, Machine Learning (ML) and Abstraction Networks (AbNs). Modern neural networks have revolutionized Machine Learning in vision and Natural Language Processing (NLP). They also show great promise for ontology-related tasks, including ontology enrichment, i.e., insertion of new concepts. This dissertation presents research using ML and AbNs to achieve knowledge enrichment of ontologies. Abstraction networks (AbNs), are compact summary networks that preserve a significant amount of the semantics and structure of the underlying ontologies. An Abstraction Network is automatically derived from the ontology itself. It consists of nodes, where each node represents a set of concepts that are similar in their structure and semantics. Various kinds of AbNs have been previously developed by the Structural Analysis of Biomedical Ontologies Center (SABOC) to support the summarization, visualization, and quality assurance (QA) of biomedical ontologies. Two basic kinds of AbNs are the Area Taxonomy and the Partial-area Taxonomy, which have been developed for various biomedical ontologies (e.g., SNOMED CT of SNOMED International and NCIt of the National Cancer Institute). This dissertation presents four enrichment studies of SNOMED CT, utilizing both ML and AbN-based techniques