Computer Science and Engineering Faculty Scholarship

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    Linked open data-based framework for automatic biomedical ontology generation
    (2018) Alobaidi, Mazen; Khalid, Mahmood Malik; Sabra, Susan
    Background: Fulfilling the vision of Semantic Web requires an accurate data model for organizing knowledge and sharing common understanding of the domain. Fitting this description, ontologies are the cornerstones of Semantic Web and can be used to solve many problems of clinical information and biomedical engineering, such as word sense disambiguation, semantic similarity, question answering, ontology alignment, etc. Manual construction of ontology is labor intensive and requires domain experts and ontology engineers. To downsize the labor-intensive nature of ontology generation and minimize the need for domain experts, we present a novel automated ontology generation framework, Linked Open Data approach for Automatic Biomedical Ontology Generation (LOD-ABOG), which is empowered by Linked Open Data (LOD). LOD-ABOG performs concept extraction using knowledge base mainly UMLS and LOD, along with Natural Language Processing (NLP) operations; and applies relation extraction using LOD, Breadth first Search (BSF) graph method, and Freepal repository patterns. Results: Our evaluation shows improved results in most of the tasks of ontology generation compared to those obtained by existing frameworks. We evaluated the performance of individual tasks (modules) of proposed framework using CDR and SemMedDB datasets. For concept extraction, evaluation shows an average F-measure of 58.12% for CDR corpus and 81.68% for SemMedDB; F-measure of 65.26% and 77.44% for biomedical taxonomic relation extraction using datasets of CDR and SemMedDB, respectively; and F-measure of 52.78% and 58.12% for biomedical non-taxonomic relation extraction using CDR corpus and SemMedDB, respectively. Additionally, the comparison with manually constructed baseline Alzheimer ontology shows F-measure of 72.48% in terms of concepts detection, 76.27% in relation extraction, and 83.28% in property extraction. Also, we compared our proposed framework with ontologylearning framework called “OntoGain” which shows that LOD-ABOG performs 14.76% better in terms of relation extraction. Conclusion: This paper has presented LOD-ABOG framework which shows that current LOD sources and technologies are a promising solution to automate the process of biomedical ontology generation and extract relations to a greater extent. In addition, unlike existing frameworks which require domain experts in ontology development process, the proposed approach requires involvement of them only for improvement purpose at the end of ontology life cycle
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    Prediction of venous thromboembolism using semantic and sentiment analyses of clinical narratives
    (2018) Sabra, Susan; Khalid, Mahmood Malik; Mazen, Alobaidi
    Venous thromboembolism (VTE) is the third most common cardiovascular disorder. It affects people of both genders at ages as young as 20 years. The increased number of VTE cases with a high fatality rate of 25% at first occurrence makes preventive measures essential. Clinical narratives are a rich source of knowledge and should be included in the diagnosis and treatment processes, as they may contain critical information on risk factors. It is very important to make such narrative blocks of information usable for searching, health analytics, and decisionmaking. This paper proposes a Semantic Extraction and Sentiment Assessment of Risk Factors (SESARF) framework. Unlike traditional machine-learning approaches, SESARF, which consists of two main algorithms, namely, ExtractRiskFactor and FindSeverity, prepares a feature vector as the input to a support vector machine (SVM) classifier to make a diagnosis. SESARF matches and maps the concepts of VTE risk factors and finds adjectives and adverbs that reflect their levels of severity. SESARF uses a semantic- and sentiment-based approach to analyze clinical narratives of electronic health records (EHR) and then predict a diagnosis of VTE. We use a dataset of 150 clinical narratives, 80% of which are used to train our prediction classifier support vector machine, with the remaining 20% used for testing. Semantic extraction and sentiment analysis results yielded precisions of 81% and 70%, respectively. Using a support vector machine, prediction of patients with VTE yielded precision and recall values of 54.5% and 85.7%, respectively