Extraction and Classification of Drug-Drug Interaction from Biomedical Text Using a Two-Stage Classifier

One of the critical causes of medical errors is Drug-Drug interaction (DDI), which occurs when one drug increases or decreases the effect of another drug. We propose a machine learning system to extract and classify drug-drug interactions from the biomedical literature, using the annotated corpus from the DDIExtraction-2013 shared task challenge. Our approach applies a two-stage classifier to handle the highly unbalanced class distribution in the corpus. The first stage is designed for binary classification of drug pairs as interacting or non-interacting, and the second stage for further classification of interacting pairs into one of four interacting types: advise, effect, mechanism, and int. To find the set of best features for classification, we explored many features, including stemmed words, bigrams, part of speech tags, verb lists, parse tree information, mutual information, and similarity measures, among others. As the system faced two different classification tasks, binary and multi-class, we also explored various classifiers in each stage. Our results show that the best performing classifier in both stages was Support Vector Machines, and the best performing features were 1000 top informative words and part of speech tags between two main drugs. We obtained an F-Measure of 0.64, showing a 12% improvement over our submitted system to the DDIExtraction 2013 competition

Applications of big knowledge summarization

Advanced technologies have resulted in the generation of large amounts of data ( Big Data ). The Big Knowledge derived from Big Data could be beyond humans\u27 ability of comprehension, which will limit the effective and innovative use of Big Knowledge repository. Biomedical ontologies, which play important roles in biomedical information systems, constitute one kind of Big Knowledge repository. Biomedical ontologies typically consist of domain knowledge assertions expressed by the semantic connections between tens of thousands of concepts. Without some high-level visual representation of Big Knowledge in biomedical ontologies, humans cannot grasp the big picture of those ontologies. Such Big Knowledge orientation is required for the proper maintenance of ontologies and their effective use. This dissertation is addressing the Big Knowledge challenge - How to enable humans to use Big Knowledge correctly and effectively (referred to as the Big Knowledge to Use (BK2U) problem) - with a focus on biomedical ontologies. In previous work, Abstraction Networks (AbNs) have been demonstrated successful for the summarization, visualization and quality assurance (QA) of biomedical ontologies. Based on the previous research, this dissertation introduces new AbNs of various granularities for Big Knowledge summarization and extends the applications of AbNs. This dissertation consists of three main parts. The first part introduces two advanced AbNs. One is the weighted aggregate partial-area taxonomy with a parameter to flexibly control the summarization granularity. The second is the Ingredient Abstraction Network (IAbN) for the National Drug File - Reference Terminology (NDF-RT) Chemical Ingredients hierarchy, for which the previously developed AbNs for hierarchies with outgoing relationships, are not applicable. Since NDF-RT\u27s Chemical Ingredients hierarchy has no outgoing relationships. The second part describes applications of the two advanced AbNs. A study utilizing the weighted aggregate partial-area taxonomy for the identification of major topics in SNOMED CT\u27s Specimen hierarchy is reported. A multi-layer interactive visualization system of required granularity for ontology comprehension, based on the weighted aggregate partial-area taxonomy, is demonstrated to comprehend the Neoplasm subhierarchy of National Cancer Institute thesaurus (NCIt). The IAbN is applied for drug-drug interaction (DDI) discovery. The third part reports eight family-based QA studies on NCIt\u27s Neoplasm, Gene, and Biological Process hierarchies, SNOMED CT\u27s Infectious disease hierarchy, the Chemical Entities of Biological Interest ontology, and the Chemical Ingredients hierarchy in NDF-RT. There is no one-size-fits-all QA method and it is impossible to find a QA method for each individual ontology. Hence, family-based QA is an effective way, i.e., one QA technique could be applicable to a whole family of structurally similar ontologies. The results of these studies demonstrate that complex concepts and uncommonly modeled concepts are more likely to have errors. Furthermore, the three studies on overlapping concepts in partial-area taxonomies reported in this dissertation combined with previous three studies prove the success of overlapping concepts as a QA methodology for a whole family of 76 similar ontologies in BioPortal