Multi-Ontology Refined Embeddings (MORE): A Hybrid Multi-Ontology and Corpus-based Semantic Representation for Biomedical Concepts

Objective: Currently, a major limitation for natural language processing (NLP) analyses in clinical applications is that a concept can be referenced in various forms across different texts. This paper introduces Multi-Ontology Refined Embeddings (MORE), a novel hybrid framework for incorporating domain knowledge from various ontologies into a distributional semantic model, learned from a corpus of clinical text. This approach generates word embeddings that are more accurate and extensible for computing the semantic similarity of biomedical concepts than previous methods. Materials and Methods: We use the RadCore and MIMIC-III free-text datasets for the corpus-based component of MORE. For the ontology-based component, we use the Medical Subject Headings (MeSH) ontology and two state-of-the-art ontology-based similarity measures. In our approach, we propose a new learning objective, modified from the Sigmoid cross-entropy objective function, to incorporate domain knowledge into the process for generating the word embeddings. Results and Discussion: We evaluate the quality of the generated word embeddings using an established dataset of semantic similarities among biomedical concept pairs. We show that the similarity scores produced by MORE have the highest average correlation (60.2%), with the similarity scores being established by multiple physicians and domain experts, which is 4.3% higher than that of the word2vec baseline model and 6.8% higher than that of the best ontology-based similarity measure. Conclusion: MORE incorporates knowledge from biomedical ontologies into an existing distributional semantics model (i.e. word2vec), improving both the flexibility and accuracy of the learned word embeddings. We demonstrate that MORE outperforms the baseline word2vec model, as well as the individual UMLS-Similarity ontology similarity measures

WEB SERVICES COMPOSITION METHODS AND TECHNIQUES: A REVIEW

ABSTRAC

Hybrid Query Expansion on Ontology Graph in Biomedical Information Retrieval

Nowadays, biomedical researchers publish thousands of papers and journals every day. Searching through biomedical literature to keep up with the state of the art is a task of increasing difficulty for many individual researchers. The continuously increasing amount of biomedical text data has resulted in high demands for an efficient and effective biomedical information retrieval (BIR) system. Though many existing information retrieval techniques can be directly applied in BIR, BIR distinguishes itself in the extensive use of biomedical terms and abbreviations which present high ambiguity. First of all, we studied a fundamental yet simpler problem of word semantic similarity. We proposed a novel semantic word similarity algorithm and related tools called Weighted Edge Similarity Tools (WEST). WEST was motivated by our discovery that humans are more sensitive to the semantic difference due to the categorization than that due to the generalization/specification. Unlike most existing methods which model the semantic similarity of words based on either the depth of their Lowest Common Ancestor (LCA) or the traversal distance of between the word pair in WordNet, WEST also considers the joint contribution of the weighted distance between two words and the weighted depth of their LCA in WordNet. Experiments show that weighted edge based word similarity method has achieved 83.5% accuracy to human judgments. Query expansion problem can be viewed as selecting top k words which have the maximum accumulated similarity to a given word set. It has been proved as an effective method in BIR and has been studied for over two decades. However, most of the previous researches focus on only one controlled vocabulary: MeSH. In addition, early studies find that applying ontology won\u27t necessarily improve searching performance. In this dissertation, we propose a novel graph based query expansion approach which is able to take advantage of the global information from multiple controlled vocabularies via building a biomedical ontology graph from selected vocabularies in Metathesaurus. We apply Personalized PageRank algorithm on the ontology graph to rank and identify top terms which are highly relevant to the original user query, yet not presented in that query. Those new terms are reordered by a weighted scheme to prioritize specialized concepts. We multiply a scaling factor to those final selected terms to prevent query drifting and append them to the original query in the search. Experiments show that our approach achieves 17.7% improvement in 11 points average precision and recall value against Lucene\u27s default indexing and searching strategy and by 24.8% better against all the other strategies on average. Furthermore, we observe that expanding with specialized concepts rather than generalized concepts can substantially improve the recall-precision performance. Furthermore, we have successfully applied WEST from the underlying WordNet graph to biomedical ontology graph constructed by multiple controlled vocabularies in Metathesaurus. Experiments indicate that WEST further improve the recall-precision performance. Finally, we have developed a Graph-based Biomedical Search Engine (G-Bean) for retrieving and visualizing information from literature using our proposed query expansion algorithm. G-Bean accepts any medical related user query and processes them with expanded medical query to search for the MEDLINE database

Métodos baseados em grafos para desambiguação de conceitos biomédicos

Mestrado em Engenharia de Computadores e TelemáticaDesambiguação do sentido das palavras é a tarefa de atribuir um significado inequívoco a uma palavra ou termo ambíguo, tendo em conta o contexto em que este está inserido. O domínio da biomedicina contem um grande número de termos ambíguos, não identificar corretamente o sentido associado a cada termo tem um impacto negativo na performance de aplicações biomédicas tais como as de anotação automática e indexação, as quais são cada vez mais de extrema importância no contexto biomédico e clinico, dado o rápido crescimen-to da informação digital disponível para os investigadores. Este tese foca-se na desambiguação de termos biomédicos e apresenta uma solução que atribui identificadores únicos a palavras ambíguas baseando-se, para isso, no Unified Medical Language System (UMLS). O método proposto é uma aproximação baseada em fontes de conhecimento a qual não necessita de dados de treino, sendo assim uma solução generalizada que pode ser am-plamente aplicada para resolver ambiguidades no domínio biomédico. Este método baseia-se em grafos obtidos a partir do UMLS, tendo em consideração os conceitos presentes no contexto da palavra ambígua, e utiliza um algoritmo de PageRank para atribuir pontuações aos grafos. Adicionalmente foi desen-volvido e disponibilizado um web-service para uma fácil integração em aplica-ções de terceiros, com o objetivo de munir essas aplicações com um módulo fácil de usar e com grande potencial. O sistema foi testado e avaliado utilizando uma coleção de testes de desambi-guação de conceitos, desenvolvido pelo U.S. National Library of Medicine, especificamente o MSH WSD Test Collection, um conjunto de dados que con-tém mais de 37 mil ocorrências de 203 termos ambíguos. Os melhores resultados obtidos pelo sistema proposto alcançaram uma preci-são de 63.3% no subset do MSH WSD Test Collection.Word Sense Disambiguation (WSD) is the task of assigning a unique meaning to an ambiguous word or term, given the specific context it is inserted in. The biomedical field contains a large number of ambiguous terms, and not being able to correctly identify the correct sense associated to a term has a negative impact on the accuracy of biomedical applications such as automatic annota-tion and indexing, which are becoming of utmost importance in the biomedical and clinical world given the fast growing amount of digital information available to researchers. This thesis focuses on disambiguation of biomedical terms and presents a solu-tion that can assign unique identifiers to target words based on Unified Medical Language System (UMLS). The method proposed is a knowledge-based ap-proach where no training data is required, thus being a more general solution that can be widely applied to solve ambiguities in the biomedical domain. This method relies on graphs obtained from the UMLS, taking into consideration the concepts from the context of the ambiguous word, and uses a PageRank algo-rithm to score such graphs. Furthermore a web-service was developed and made available for an easy integration in third-party applications, in order to provide such applications with a powerful and easy to use module. The system was tested and evaluated using a WSD test collection provided by the U.S. National Library of Medicine, specifically the MSH WSD Test Collec-tion, a dataset containing over 37 thousand occurrences of 203 ambiguous terms. The best performing results of the proposed system achieve an accuracy of 63.3% for a subset of the MSH WSD Test Collection

Automated methods to extract patient new information from clinical notes in electronic health record systems

University of Minnesota Ph.D. dissertation. November 2013. Major: Health Informatics. Advisor: Serguei Pakhomov. 1 computer file (PDF); xii, 102 pages.The widespread adoption of Electronic Health Record (EHR) has resulted in rapid text proliferation within clinical care. Clinicians' use of copying and pasting functions in EHR systems further compounds this by creating a large amount of redundant clinical information in clinical documents. A mixture of redundant information (especially outdated and incorrect information) and new information in a single clinical note increases clinicians' cognitive burden and results in decision-making difficulties. Moreover, replicated erroneous information can potentially cause risks to patient safety. However, automated methods to identify redundant or relevant new information in clinical texts have not been extensively investigated. The overarching goal of this research is to develop and evaluate automated methods to identify new and clinically relevant information in clinical notes using expert-derived reference standards. Modified global alignment methods were adapted to investigate the pattern of redundancy in individual longitudinal clinical notes as well as a larger group of patient clinical notes. Statistical language models were also developed to identify new and clinically relevant information in clinical notes. Relevant new information identified by automated methods will be highlighted in clinical notes to provide visualization cues to clinicians. New information proportion (NIP) was used to indicate the quantity of new information in each note and also navigate clinician notes with more new information. Classifying semantic types of new information further provides clinicians with specific types of new information that they are interested in finding. The techniques developed in this research can be incorporated into production EHR systems and could potentially aid clinicians in finding and synthesizing new information in a note more purposely, and could finally improve the efficiency of healthcare delivery

COHORT IDENTIFICATION FROM FREE-TEXT CLINICAL NOTES USING SNOMED CT’S SEMANTIC RELATIONS

In this paper, a new cohort identification framework that exploits the semantic hierarchy of SNOMED CT is proposed to overcome the limitations of supervised machine learning-based approaches. Eligibility criteria descriptions and free-text clinical notes from the 2018 National NLP Clinical Challenge (n2c2) were processed to map to relevant SNOMED CT concepts and to measure semantic similarity between the eligibility criteria and patients. The eligibility of a patient was determined if the patient had a similarity score higher than a threshold cut-off value, which was established where the best F1 score could be achieved. The performance of the proposed system was evaluated for three eligibility criteria. The current framework’s macro-average F1 score across three eligibility criteria was higher than the previously reported results of the 2018 n2c2 (0.933 vs. 0.889). This study demonstrated that SNOMED CT alone can be leveraged for cohort identification tasks without referring to external textual sources for training.Doctor of Philosoph