Automated Discovery of Multivariate Associations in Large Time-Varying data sets: a Healthcare Network Application
ANALYSIS AND MODELING OF QUALITY IMPROVEMENT ON CLINICAL FITNESS LANDSCAPES, , , 2014
Abstract: We introduce a new method for exploratory analysis of large data sets with time-varying
features, where the aim is to automatically discover novel relationships between features
(over some time period) that are predictive of any of a number of time-varying outcomes
(over some other time period). Using a genetic algorithm, we co-evolve (i) a subset of
predictive features, (ii) which attribute will be predicted, (iii) the time period over which to assess the predictive features, and (iv) the time period over which to assess the predicted
attribute. After validating the method on 15 synthetic test problems, we used the approach
for exploratory analysis of a large healthcare network data set. We discovered a strong association,
with 100% sensitivity, between hospital participation in multi-institutional quality
improvement collaboratives during or before 2002, and changes in the risk-adjusted rates of
mortality and morbidity observed after a 1-2 year lag. The results provide indirect evidence
that these quality improvement collaboratives may have had the desired effect of improving
health care practices at participating hospitals. The proposed approach is a potentially
powerful and general tool for exploratory analysis of a wide range of time-series data sets
[edit database entry]
Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).