Extending import detection algorithms for concept import from two to three biomedical terminologies

Background: While enrichment of terminologies can be achieved in different ways, filling gaps in the IS-A hierarchy backbone of a terminology appears especially promising. To avoid difficult manual inspection, we started a research program in 2014, investigating terminology densities, where the comparison of terminologies leads to the algorithmic discovery of potentially missing concepts in a target terminology. While candidate concepts have to be approved for import by an expert, the human effort is greatly reduced by algorithmic generation of candidates. In previous studies, a single source terminology was used with one target terminology. Methods: In this paper, we are extending the algorithmic detection of “candidate concepts for import” from one source terminology to two source terminologies used in tandem. We show that the combination of two source terminologies relative to one target terminology leads to the discovery of candidate concepts for import that could not be found with the same “reliability” when comparing one source terminology alone to the target terminology. We investigate which triples of UMLS terminologies can be gainfully used for the described purpose and how many candidate concepts can be found for each individual triple of terminologies. Results: The analysis revealed a specific configuration of concepts, overlapping two source and one target terminology, for which we coined the name “fire ladder” pattern. The three terminologies in this pattern are tied together by a kind of “transitivity.” We provide a quantitative analysis of the discovered fire ladder patterns and we report on the inter-rater agreement concerning the decision of importing candidate concepts from source terminologies into the target terminology. We algorithmically identified 55 instances of the fire ladder pattern and two domain experts agreed on import for 39 instances. In total, 48 concepts were approved by at least one expert. In addition, 105 import candidate concepts from a single source terminology into the target terminology were also detected, as a “beneficial side-effect” of this method, increasing the cardinality of the result. Conclusion: We showed that pairs of biomedical source terminologies can be transitively chained to suggest possible imports of concepts into a target terminology

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

Biomedical concept association and clustering using word embeddings

Indiana University-Purdue University Indianapolis (IUPUI)Biomedical data exists in the form of journal articles, research studies, electronic health records, care guidelines, etc. While text mining and natural language processing tools have been widely employed across various domains, these are just taking off in the healthcare space. A primary hurdle that makes it difficult to build artificial intelligence models that use biomedical data, is the limited amount of labelled data available. Since most models rely on supervised or semi-supervised methods, generating large amounts of pre-processed labelled data that can be used for training purposes becomes extremely costly. Even for datasets that are labelled, the lack of normalization of biomedical concepts further affects the quality of results produced and limits the application to a restricted dataset. This affects reproducibility of the results and techniques across datasets, making it difficult to deploy research solutions to improve healthcare services. The research presented in this thesis focuses on reducing the need to create labels for biomedical text mining by using unsupervised recurrent neural networks. The proposed method utilizes word embeddings to generate vector representations of biomedical concepts based on semantics and context. Experiments with unsupervised clustering of these biomedical concepts show that concepts that are similar to each other are clustered together. While this clustering captures different synonyms of the same concept, it also captures the similarities between various diseases and the symptoms that those diseases are symptomatic of. To test the performance of the concept vectors on corpora of documents, a document vector generation method that utilizes these concept vectors is also proposed. The document vectors thus generated are used as an input to clustering algorithms, and the results show that across multiple corpora, the proposed methods of concept and document vector generation outperform the baselines and provide more meaningful clustering. The applications of this document clustering are huge, especially in the search and retrieval space, providing clinicians, researchers and patients more holistic and comprehensive results than relying on the exclusive term that they search for. At the end, a framework for extracting clinical information that can be mapped to electronic health records from preventive care guidelines is presented. The extracted information can be integrated with the clinical decision support system of an electronic health record. A visualization tool to better understand and observe patient trajectories is also explored. Both these methods have potential to improve the preventive care services provided to patients

A framework for analyzing changes in health care lexicons and nomenclatures

Ontologies play a crucial role in current web-based biomedical applications for capturing contextual knowledge in the domain of life sciences. Many of the so-called bio-ontologies and controlled vocabularies are known to be seriously defective from both terminological and ontological perspectives, and do not sufficiently comply with the standards to be considered formai ontologies. Therefore, they are continuously evolving in order to fix the problems and provide valid knowledge. Moreover, many problems in ontology evolution often originate from incomplete knowledge about the given domain. As our knowledge improves, the related definitions in the ontologies will be altered. This problem is inadequately addressed by available tools and algorithms, mostly due to the lack of suitable knowledge representation formalisms to deal with temporal abstract notations, and the overreliance on human factors. Also most of the current approaches have been focused on changes within the internal structure of ontologies, and interactions with other existing ontologies have been widely neglected. In this research, alter revealing and classifying some of the common alterations in a number of popular biomedical ontologies, we present a novel agent-based framework, RLR (Represent, Legitimate, and Reproduce), to semi-automatically manage the evolution of bio-ontologies, with emphasis on the FungalWeb Ontology, with minimal human intervention. RLR assists and guides ontology engineers through the change management process in general, and aids in tracking and representing the changes, particularly through the use of category theory. Category theory has been used as a mathematical vehicle for modeling changes in ontologies and representing agents' interactions, independent of any specific choice of ontology language or particular implementation. We have also employed rule-based hierarchical graph transformation techniques to propose a more specific semantics for analyzing ontological changes and transformations between different versions of an ontology, as well as tracking the effects of a change in different levels of abstractions. Thus, the RLR framework enables one to manage changes in ontologies, not as standalone artifacts in isolation, but in contact with other ontologies in an openly distributed semantic web environment. The emphasis upon the generality and abstractness makes RLR more feasible in the multi-disciplinary domain of biomedical Ontology change management

Information Extraction from Text for Improving Research on Small Molecules and Histone Modifications

The cumulative number of publications, in particular in the life sciences, requires efficient methods for the automated extraction of information and semantic information retrieval. The recognition and identification of information-carrying units in text – concept denominations and named entities – relevant to a certain domain is a fundamental step. The focus of this thesis lies on the recognition of chemical entities and the new biological named entity type histone modifications, which are both important in the field of drug discovery. As the emergence of new research fields as well as the discovery and generation of novel entities goes along with the coinage of new terms, the perpetual adaptation of respective named entity recognition approaches to new domains is an important step for information extraction. Two methodologies have been investigated in this concern: the state-of-the-art machine learning method, Conditional Random Fields (CRF), and an approximate string search method based on dictionaries. Recognition methods that rely on dictionaries are strongly dependent on the availability of entity terminology collections as well as on its quality. In the case of chemical entities the terminology is distributed over more than 7 publicly available data sources. The join of entries and accompanied terminology from selected resources enables the generation of a new dictionary comprising chemical named entities. Combined with the automatic processing of respective terminology – the dictionary curation – the recognition performance reached an F1 measure of 0.54. That is an improvement by 29 % in comparison to the raw dictionary. The highest recall was achieved for the class of TRIVIAL-names with 0.79. The recognition and identification of chemical named entities provides a prerequisite for the extraction of related pharmacological relevant information from literature data. Therefore, lexico-syntactic patterns were defined that support the automated extraction of hypernymic phrases comprising pharmacological function terminology related to chemical compounds. It was shown that 29-50 % of the automatically extracted terms can be proposed for novel functional annotation of chemical entities provided by the reference database DrugBank. Furthermore, they are a basis for building up concept hierarchies and ontologies or for extending existing ones. Successively, the pharmacological function and biological activity concepts obtained from text were included into a novel descriptor for chemical compounds. Its successful application for the prediction of pharmacological function of molecules and the extension of chemical classification schemes, such as the the Anatomical Therapeutic Chemical (ATC), is demonstrated. In contrast to chemical entities, no comprehensive terminology resource has been available for histone modifications. Thus, histone modification concept terminology was primary recognized in text via CRFs with a F1 measure of 0.86. Subsequent, linguistic variants of extracted histone modification terms were mapped to standard representations that were organized into a newly assembled histone modification hierarchy. The mapping was accomplished by a novel developed term mapping approach described in the thesis. The combination of term recognition and term variant resolution builds up a new procedure for the assembly of novel terminology collections. It supports the generation of a term list that is applicable in dictionary-based methods. For the recognition of histone modification in text it could be shown that the named entity recognition method based on dictionaries is superior to the used machine learning approach. In conclusion, the present thesis provides techniques which enable an enhanced utilization of textual data, hence, supporting research in epigenomics and drug discovery