A visual analytics approach to understanding spatiotemporal hotspots.

As data sources become larger and more complex, the ability to effectively explore and analyze patterns among varying sources becomes a critical bottleneck in analytic reasoning. Incoming data contain multiple variables, high signal-to-noise ratio, and a degree of uncertainty, all of which hinder exploration, hypothesis generation/exploration, and decision making. To facilitate the exploration of such data, advanced tool sets are needed that allow the user to interact with their data in a visual environment that provides direct analytic capability for finding data aberrations or hotspots. In this paper, we present a suite of tools designed to facilitate the exploration of spatiotemporal data sets. Our system allows users to search for hotspots in both space and time, combining linked views and interactive filtering to provide users with contextual information about their data and allow the user to develop and explore their hypotheses. Statistical data models and alert detection algorithms are provided to help draw user attention to critical areas. Demographic filtering can then be further applied as hypotheses generated become fine tuned. This paper demonstrates the use of such tools on multiple geospatiotemporal data sets.

Patient-specific surgical outcomes assessment using population-based data analysis for risk model development.

Optimal surgical planning and decision making surrounding surgical interventions requires patient-specific risk assessment which incorporates patient pre-operative clinical assessment and clinical literature. In this paper, we utilized population-based data analysis to construct surgical outcome predictive models for spinal fusion surgery using hospital, patient and admission characteristics. We analyzed population data from the Nationwide Inpatient Sample (NIS) -a nationally representative database- to identify data elements affecting inpatient mortality, length of stay, and disposition status for patients receiving spinal fusion surgery in the years 2004-2008. In addition to outcomes assessment, we want to make the analytic model results available to clinicians and researchers for pre-operative surgical risk assessment, hospital resource allocation, and hypothesis generation for future research without an individual patient data management burden. Spinal fusion was the selected prototype procedure due to it being a high volume and typically inpatient procedure where patient risk factors will likely affect clinical outcomes.