A Study Into the Feasibility of Using Natural Language Processing and Machine Learning for the Identification of Alcohol Misuse in Trauma Patients

Alcohol misuse is a leading cause of premature death in the United States, with nearly a third of trauma patients found to have elevated blood alcohol levels upon admission. However, timely intervention has been shown to reduce this. It is thus important to be able to quickly screen patients to identify alcohol misuse. Many medical centers use standardized questionnaires to identify alcohol misuse, but since these instruments are not usually a part of routine care, there are many cases where it is not done. In this study, large quantities of notes were processed with natural language processing and machine learning methods to identify important social and behavioral determinants for health. It resulted in the creation of a system that provides good discrimination of patients with and without alcohol misuse

Text Mining Infrastructure in R

During the last decade text mining has become a widely used discipline utilizing statistical and machine learning methods. We present the tm package which provides a framework for text mining applications within R. We give a survey on text mining facilities in R and explain how typical application tasks can be carried out using our framework. We present techniques for count-based analysis methods, text clustering, text classification and string kernels.

Interactive Style Transfer for Data Visualization and Data Art

This thesis discusses Data Brushes, an interactive web application to explore neural style transfer using models trained on artistic data visualizations. The application invites casual creators to engage with deep convolutional neural networks to co-create custom artworks with a focus on style transfer networks created from canonical and contemporary works of data visualization and data art to demonstrate the versatility and flexibility of the algorithm. In addition to enabling a novel creative workflow, the process of interactively modifying an image via multiple style transfer networks reveals meaningful features encoded within the networks, and provides insight into the effects particular networks have on different images, or different regions within a single image. To evaluate Data Brushes, we gathered expert feedback from participants of a data science symposium and ran an observational study, finding that our application facilitates the creative exploration of neural style transfer for data art and enhances user intuition regarding the expressive range of style transfer features. This thesis explores both the practical uses of such tools for artists as Data Brushes and the interpretive uses of creating such venues for accessibility to computational art, remixing the purpose of data visualizations to be more than just graphical representations of information

Creating Data from Unstructured Text with Context Rule Assisted Machine Learning (CRAML)

Popular approaches to building data from unstructured text come with limitations, such as scalability, interpretability, replicability, and real-world applicability. These can be overcome with Context Rule Assisted Machine Learning (CRAML), a method and no-code suite of software tools that builds structured, labeled datasets which are accurate and reproducible. CRAML enables domain experts to access uncommon constructs within a document corpus in a low-resource, transparent, and flexible manner. CRAML produces document-level datasets for quantitative research and makes qualitative classification schemes scalable over large volumes of text. We demonstrate that the method is useful for bibliographic analysis, transparent analysis of proprietary data, and expert classification of any documents with any scheme. To demonstrate this process for building data from text with Machine Learning, we publish open-source resources: the software, a new public document corpus, and a replicable analysis to build an interpretable classifier of suspected “no poach” clauses in franchise documents