The Impact of Directionality in Predications on Text Mining

The number of publications in biomedicine is increasing enormously each year. To help researchers digest the information in these documents, text mining tools are being developed that present co-occurrence relations between concepts. Statistical measures are used to mine interesting subsets of relations. We demonstrate how directionality of these relations affects interestingness. Support and confidence, simple data mining statistics, are used as proxies for interestingness metrics. We first built a test bed of 126,404 directional relations extracted from biomedical abstracts, which we represent as graphs containing a central starting concept and 2 rings of associated relations. We manipulated directionality in four ways and randomly selected 100 starting concepts as a test sample for each graph type. Finally, we calculated the number of relations and their support and confidence. Variation in directionality significantly affected the number of relations as well as the support and confidence of the four graph types

Comparing Attributional and Relational Similarity as a Means to Identify Clinically Relevant Drug-gene Relationships

In emerging domains, such as precision oncology, knowledge extracted from explicit assertions may be insufficient to identify relationships of interest. One solution to this problem involves drawing inference on the basis of similarity. Computational methods have been developed to estimate the semantic similarity and relatedness between terms and relationships that are distributed across corpora of literature such as Medline abstracts and other forms of human readable text. Most research on distributional similarity has focused on the notion of attributional similarity, which estimates the similarity between entities based on the contexts in which they occur across a large corpus. A relatively under-researched area concerns relational similarity, in which the similarity between pairs of entities is estimated from the contexts in which these entity pairs occur together. While it seems intuitive that models capturing the structure of the relationships between entities might mediate the identification of biologically important relationships, there is to date no comparison of the relative utility of attributional and relational models for this purpose. In this research, I compare the performance of a range of relational and attributional similarity methods, on the task of identifying drugs that may be therapeutically useful in the context of particular aberrant genes, as identified by a team of human experts. My hypothesis is that relational similarity will be of greater utility than attributional similarity as a means to identify biological relationships that may provide answers to clinical questions, (such as “which drugs INHIBIT gene x”?) in the context of rapidly evolving domains. My results show that models based on relational similarity outperformed models based on attributional similarity on this task. As the methods explained in this research can be applied to identify any sort of relationship for which cue pairs exist, my results suggest that relational similarity may be a suitable approach to apply to other biomedical problems. Furthermore, I found models based on neural word embeddings (NWE) to be particularly useful for this task, given their higher performance than Random Indexing-based models, and significantly less computational effort needed to create them. NWE methods (such as those produced by the popular word2vec tool) are a relatively recent development in the domain of distributional semantics, and are considered by many as the state-of-the-art when it comes to semantic language modeling. However, their application in identifying biologically important relationships from Medline in general, and specifically, in the domain of precision oncology has not been well studied. The results of this research can guide the design and implementation of biomedical question answering and other relationship extraction applications for precision medicine, precision oncology and other similar domains, where there is rapid emergence of novel knowledge. The methods developed and evaluated in this project can help NLP applications provide more accurate results by leveraging corpus based methods that are by design scalable and robust

Doctor of Philosophy

dissertationThe objective of this work is to examine the efficacy of natural language processing (NLP) in summarizing bibliographic text for multiple purposes. Researchers have noted the accelerating growth of bibliographic databases. Information seekers using traditional information retrieval techniques when searching large bibliographic databases are often overwhelmed by excessive, irrelevant data. Scientists have applied natural language processing technologies to improve retrieval. Text summarization, a natural language processing approach, simplifies bibliographic data while filtering it to address a user's need. Traditional text summarization can necessitate the use of multiple software applications to accommodate diverse processing refinements known as "points-of-view." A new, statistical approach to text summarization can transform this process. Combo, a statistical algorithm comprised of three individual metrics, determines which elements within input data are relevant to a user's specified information need, thus enabling a single software application to summarize text for many points-of-view. In this dissertation, I describe this algorithm, and the research process used in developing and testing it. Four studies comprised the research process. The goal of the first study was to create a conventional schema accommodating a genetic disease etiology point-of-view, and an evaluative reference standard. This was accomplished through simulating the task of secondary genetic database curation. The second study addressed the development iv and initial evaluation of the algorithm, comparing its performance to the conventional schema using the previously established reference standard, again within the task of secondary genetic database curation. The third and fourth studies evaluated the algorithm's performance in accommodating additional points-of-view in a simulated clinical decision support task. The third study explored prevention, while the fourth evaluated performance for prevention and drug treatment, comparing results to a conventional treatment schema's output. Both summarization methods identified data that were salient to their tasks. The conventional genetic disease etiology and treatment schemas located salient information for database curation and decision support, respectively. The Combo algorithm located salient genetic disease etiology, treatment, and prevention data, for the associated tasks. Dynamic text summarization could potentially serve additional purposes, such as consumer health information delivery, systematic review creation, and primary research. This technology may benefit many user groups

Doctor of Philosophy

dissertationThe use of the various complementary and alternative medicine (CAM) modalities for the management of chronic illnesses is widespread, and still on the rise. Unfortunately, tools to support consumers in seeking information on the efficacy of these treatments are sparse and incomplete. The goals of this work were to understand CAM information needs in acquiring CAM information, assess currently available information resources, and investigate informatics methods to provide a foundation for the development of CAM information resources. This dissertation consists of four studies. The first was a quantitative study that aimed to assess the feasibility of delivering CAM-drug interaction information through a web-based application. This study resulted in an 85% participation rate and 33% of those patients reported the use of CAMs that had potential interactions with their conventional treatments. The next study aimed to assess online CAM information resources that provide information on drug-herb interactions to consumers. None of the sites scored high on the combination of completeness and accuracy and all sites were beyond the recommended reading level per the US Department of Health and Human Services. The third study investigated information-seeking behaviors for CAM information using an existing cohort of cancer survivors. The study showed that patients in the cohort continued to use CAM well into survivorship. Patients felt very much on their own in dealing with issues outside of direct treatment, which often resulted in a search for options and CAM use. Finally, a study was conducted to investigate two methods to semi-automatically extract CAM treatment relations from the biomedical literature. The methods rely on a database (SemMedDB) of semantic relations extracted from PubMed abstracts. This study demonstrated that SemMedDB can be used to reduce manual efforts, but review of the extracted sentences is still necessary due to a low mean precision of 23.7% and 26.4%. In summary, this dissertation provided greater insight into consumer information needs for CAM. Our findings provide an opportunity to leverage existing resources to improve the information-seeking experience for consumers through high-quality online tools, potentially moving them beyond the reliance on anecdotal evidence in the decision-making process for CAM