Integrating heterogeneous data into electronic medical record analysis

Electronic medical records (EMRs) are the digital equivalent of paper records at a clinician's office. They contain patient information such as treatment and medical history, and have been shown to have a wide variety of benefits. However, EMRs typically contain a multitude of diverse data, including images, doctor notes, medical test results, and genomic data. This heterogeneity generates high dimensionality and data sparsity, which are two of the most prevalent culprits that exacerbate already difficult computational problems. Additionally, domain-specific characteristics, such as the existence of synonyms in the medical vocabulary, introduce ambiguity. This can further reduce the data mining potential of EMRs. This thesis is a systematic study that addresses these issues associated with EMRs. In particular, I utilized heterogeneous data sources that are typically incompatible, and then developed frameworks in which these data sources complement one another. As a result, these methods have the potential for direct clinical translation, paving the way for improving healthcare from a data-driven perspective. To improve a variety of downstream healthcare applications, such as patient subcategorization, survival analysis, and visualization, I used external networks of domain knowledge consisting of drug-symptom relationships, protein-protein interactions, and genetic information to enhance patient records. I found that this enhancement process increased the data mining capabilities as well as the interpretability of the EMRs. To improve EMR retrieval systems, I developed a query expansion method that frames symptoms and treatments as two different languages. I found that a topic modeling method that follows this dual-language framework yielded the highest performance. Lastly, I showed that due to pathological similarities, jointly studying Alzheimer's disease and Parkinson's disease resulted in higher computational power by effectively increasing the size of the training datasets. This allowed for the accurate prediction of the onset of dementia in both diseases. Each of these results can lay the groundwork for applications that have the potential to be implemented directly in clinical practice, improving the safety and quality of patient care

Identifying Relevant Evidence for Systematic Reviews and Review Updates

Systematic reviews identify, assess and synthesise the evidence available to answer complex research questions. They are essential in healthcare, where the volume of evidence in scientific research publications is vast and cannot feasibly be identified or analysed by individual clinicians or decision makers. However, the process of creating a systematic review is time consuming and expensive. The pace of scientific publication in medicine and related fields also means that evidence bases are continually changing and review conclusions can quickly become out of date. Therefore, developing methods to support the creating and updating of reviews is essential to reduce the workload required and thereby ensure that reviews remain up to date. This research aims to support systematic reviews, thus improving healthcare through natural language processing and information retrieval techniques. More specifically, this thesis aims to support the process of identifying relevant evidence for systematic reviews and review updates to reduce the workload required from researchers. This research proposes methods to improve studies ranking for systematic reviews. In addition, this thesis describes a dataset of systematic review updates in the field of medicine created using 25 Cochrane reviews. Moreover, this thesis develops an algorithm to automatically refine the Boolean query to improve the identification of relevant studies for review updates. The research demonstrates that automating the process of identifying relevant evidence can reduce the workload of conducting and updating systematic reviews

Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

Peer reviewe

Public Relations: Diaspora, Media, and the State(s) of American Literature

Like any good public relations campaign, this dissertation aims to offer a persuasive interpretation of certain key facts. The facts, as I see them, are as follows: first, a great number of contemporary novels and poems explore the personal and social consequences of diasporic migration. Second, these texts, along with their print and electronic paratexts, share a pervasive interest in media. And third, these works are rarely read in conversation with one another, despite their mutual concern for migration and media. Owing to this last point in particular, scholarship has failed to fully address the broader media theories developed in and across these works, and failed to fully pursue how these media theories respond to, and critically comment on, the prospects for deliberative democracy in an age of globalization. In response, my project argues that diasporic media practices advance a transnational critique of public sphere theories. And yet, I claim this critique seeks to recover the resources of such theories and redeploy them in a global context. The four chapters of this dissertation are arranged in a communications circuit that treats (in order) media production, circulation, reception, and survival. Together, these chapters observe how diasporic populations shift from invisible anomalies to visible publics through their highly stylized and politicized use of media technologies. Ultimately, I emphasize that contemporary American literature cannot be understood without engaging reading and writing publics from the Dominican Republic, Canada, Nigeria, Korea, and more

The Palgrave Handbook of Digital Russia Studies

This open access handbook presents a multidisciplinary and multifaceted perspective on how the ‘digital’ is simultaneously changing Russia and the research methods scholars use to study Russia. It provides a critical update on how Russian society, politics, economy, and culture are reconfigured in the context of ubiquitous connectivity and accounts for the political and societal responses to digitalization. In addition, it answers practical and methodological questions in handling Russian data and a wide array of digital methods. The volume makes a timely intervention in our understanding of the changing field of Russian Studies and is an essential guide for scholars, advanced undergraduate and graduate students studying Russia today