Shang-Tse Chen

TimeStitch: Interactive multi-focus cohort discovery and comparison

Whereas event-based timelines for healthcare enable users to visualize the chronology of events surrounding events of interest, they are often not designed to aid the discovery, construction, or comparison of associated cohorts. We present TimeStitch, a system that helps health researchers discover and understand events that may cause abstinent smokers to lapse. TimeStitch extracts common sequences of events performed by abstinent smokers from large amounts of mobile health sensor data, and offers a suite of interactive and visualization techniques to enable cohort discovery, construction, and comparison, using extracted sequences as interactive elements. We are extending TimeStitch to support more complex health conditions with high mortality risk, such as reducing hospital readmission in congestive heart failure.

Exploratory Visual Analytics of Mobile Health Data: Sensemaking Challenges and Opportunities

With every advancement in mHealth sensing technology, we are presented with an abundance of data streams and models that enable us to make sense of health information we record. To distill this diverse and ever-growing data into meaningful information, we must first develop tools that can represent data intuitively and are flexible enough to handle the special characteristics of mHealth records. For example, whereas traditional health data such as electronic health records (EHR) often consist of discrete events that may be readily analyzed and visualized, mHealth entails sensor ensembles that generate continuous, multivariate data streams of high-resolution and often noisy measurements. Drawing from methodologies in machine learning and visualization, interactive visual analytics tools are an increasingly important aid to making sense of this complexity. Still, these computational and visual techniques must be employed attentively to represent this data not only intuitively, but also accurately, transparently, and in a way that is driven by user needs. Acknowledging these challenges, we review existing visual analytic tools to identify design solutions that are both useful for and adaptable to the demands of mHealth data analysis tasks. In doing so, we identify open problems for representing and understanding mHealth data, suggesting future research directions for developers in the field.

Chronodes: Interactive Multifocus Exploration of Event Sequences

The advent of mobile health (mHealth) technologies challenges the capabilities of current visualizations, interactive tools, and algorithms. We present Chronodes, an interactive system that unifies data mining and human-centric visualization techniques to support explorative analysis of longitudinal mHealth data. Chronodes extracts and visualizes frequent event sequences that reveal chronological patterns across multiple participant timelines of mHealth data. It then combines novel interaction and visualization techniques to enable multifocus event sequence analysis, which allows health researchers to interactively define, explore, and compare groups of participant behaviors using event sequence combinations. Through summarizing insights gained from a pilot study with 20 behavioral and biomedical health experts, we discuss Chronodess efficacy and potential impact in the mHealth domain. Ultimately, we outline important open challenges in mHealth, and offer recommendations and design guidelines for future research.

Predicting Cyber Threats with Virtual Security Products

Cybersecurity analysts are often presented suspicious machine activity that does not conclusively indicate compromise, resulting in undetected incidents or costly investigations into the most appropriate remediation actions. There are many reasons for this: deficiencies in the number and quality of security products that are deployed, poor configuration of those security products, and incomplete reporting of product-security telemetry. Managed Security Service Providers (MSSPs), which are tasked with detecting security incidents on behalf of multiple customers, are confronted with these data quality issues, but also possess a wealth of cross-product security data that enables innovative solutions. We use MSSP data to develop Virtual Product, which addresses the aforementioned data challenges by predicting what security events would have been triggered by a security product if it had been present. This benefits the analysts by providing more context into existing security incidents (albeit probabilistic) and by making questionable security incidents more conclusive. We achieve up to 99% AUC in predicting the incidents that some products would have detected had they been present.