Reid A Johnson

• PhD in Computer Science and Engineering
• University of Notre Dame

Coupled with the rise of data science and machine learning, the increasing availability of digitized health and wellness data has provided an exciting opportunity for complex analyses of problems throughout the healthcare domain. Whereas many early works focused on a particular aspect of patient care, often drawing on data from a specific clinical...

To seek answers to health queries, we often find ourselves on a quest to assimilate information from varied online sources. This information search and fusion from different sources elicits user preferences, which can be driven by demographics, context, and socio-economic factors. To that end, we study these factors as part of health-information se...

Nonstandard insurers suffer from a peculiar variant of fraud wherein an overwhelming majority of claims have the semblance of fraud. We show that state-of-the-art fraud detection performs poorly when deployed at underwriting. Our proposed framework "FraudBuster" represents a new paradigm in predicting segments of fraud at underwriting in an interpr...

A widely recognized organizing principle of networks is structural homophily, which suggests that people with more common neighbors are more likely to connect with each other. However, what influence the diverse structures embedded in common neighbors have on link formation is much less well-understood. To explore this problem, we begin by characte...

Medication non-adherence is a pressing concern among seniors, leading to a lower quality of life and higher healthcare costs. While mobile applications provide a viable medium for medication management, their utility can be limited without tackling the specific needs of seniors and facilitating the active involvement of care providers. To address t...

A widely used measure of scientific impact is citations. However, due to their heavy-tailed distribution, citations are fundamentally difficult to predict. Instead, to characterize scientific impact, we address two analogous questions asked by many scientific researchers: “How will my h -index evolve over time, and which of my previously or newly...

A widely used measure of scientific impact is citations. However, due to their heavy-tailed distribution, citations are fundamentally difficult to predict. Instead, to characterize scientific impact, we address two analogous questions asked by many scientific researchers: "How will my h-index evolve over time, and which of my previously or newly pu...

Understanding the ways in which local network structures are formed and organized is a fundamental problem in network science. A widely recognized organizing principle is structural homophily, which suggests that people with more common neighbors are more likely to connect with each other. However, what influence the diverse structures formed by co...

Undersampling is a popular technique for unbalanced datasets to reduce the skew in class distributions. However, it is well-known that undersampling one class modifies the priors of the training set and consequently biases the posterior probabilities of a classifier [9]. In this paper, we study analytically and experimentally how undersampling affe...

It is well-known that many networks follow a power-law degree distribution; however, the factors that influence the formation of their distributions are still unclear. How can one model the connection between individual actions and network distributions? How can one explain the formation of group phenomena and their evolutionary patterns? In this p...

A widely used measure of scientific impact is citations. However, due to their power-law distribution, citations are fundamentally difficult to predict. Instead, to characterize scientific impact, we address two analogous questions asked by many scientific researchers: “How will my h-index evolve over time, and which of my previously or newly publi...

Collaboration is an integral element of the scientific process that often leads to findings with significant impact. While extensive efforts have been devoted to quantifying and predicting research impact, the question of how collaborative behavior influences scientific impact remains unaddressed. In this work, we study the interplay between scient...

The deployment of classification models is an integral component of many modern data mining and machine learning applications. A typical classification model is built with the tacit assumption that the deployment scenario by which it is evaluated is fixed and fully characterized. Yet, in the practical deployment of classification methods, important...

Scientific impact plays a central role in the evaluation of the output of scholars, departments, and institutions. A widely used measure of scientific impact is citations, with a growing body of literature focused on predicting the number of citations obtained by any given publication. The effectiveness of such predictions, however, is fundamentall...

Hellinger Distance Decision Trees (HDDT) has been previously used for static datasets with skewed distributions. In unbalanced data streams, state-of-the-art techniques use instance propagation and standard decision trees (e.g. C4.5) to cope with the unbalanced problem. However it is not always possible to revisit/store old instances of a stream. I...

The concept of a negative class does not apply to many problems for which classification is increasingly utilized. In this study we investigate the reliability of evaluation metrics when the negative class contains an unknown proportion of mislabeled positive class instances. We examine how evaluation metrics can inform us about potential systemati...

Predicting the distributions of species is central to a variety of applications in ecology and conservation biology. With increasing interest in using electronic occurrence records, many modeling techniques have been developed to utilize this data and compute the potential distribution of species as a proxy for actual observations. As the actual ob...

An underlying assumption of biomedical informatics is that decisions can be more informed when professionals are assisted by analytical systems. For this purpose, we propose ALIVE, a multi-relational link prediction and visualization environment for the healthcare domain. ALIVE combines novel link prediction methods with a simple user interface and...