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Displaying 1-8 out of 8 total
Using Artificial Intelligence to Improve Hospital Inpatient Care
Found in: IEEE Intelligent Systems
By Daniel B. Neill
Issue Date:March 2013
pp. 92-95
AI can be used to address many challenges facing America's healthcare system—from disease detection to building predictive models for treatment—thereby improving the quality and lowering the cost of patient care.
 
Information Visualization for Chronic Disease Risk Assessment
Found in: IEEE Intelligent Systems
By Christopher A. Harle,Daniel B. Neill,Rema Padman
Issue Date:November 2012
pp. 81-85
Here, the authors describe and evaluate a new information-visualization method and prototype software tool that support risk assessment for negative health outcomes. Their framework uses principal component analysis and linear discriminant analysis to plot...
 
New Directions in Artificial Intelligence for Public Health Surveillance
Found in: IEEE Intelligent Systems
By Daniel B. Neill
Issue Date:January 2012
pp. 56-59
The next decade of disease surveillance research will require novel methods to effectively use massive quantities of complex, high-dimensional data. We summarize two recent approaches which deal with the increasing complexity and scale of health data, incl...
 
Dynamic Pattern Detection with Temporal Consistency and Connectivity Constraints
Found in: 2013 IEEE International Conference on Data Mining (ICDM)
By Skyler Speakman,Yating Zhang,Daniel B. Neill
Issue Date:December 2013
pp. 697-706
We explore scalable and accurate dynamic pattern detection methods in graph-based data sets. We apply our proposed Dynamic Subset Scan method to the task of detecting, tracking, and source-tracing contaminant plumes spreading through a water distribution s...
 
A Generalized Fast Subset Sums Framework for Bayesian Event Detection
Found in: Data Mining, IEEE International Conference on
By Kan Shao,Yandong Liu,Daniel B. Neill
Issue Date:December 2011
pp. 617-625
We present Generalized Fast Subset Sums (GFSS), a new Bayesian framework for scalable and accurate detection of irregularly shaped spatial clusters using multiple data streams. GFSS extends the previously proposed Multivariate Bayesian Scan Statistic (MBSS...
 
Detection of emerging space-time clusters
Found in: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining (KDD '05)
By Andrew W. Moore, Daniel B. Neill, Kenny Daniel, Maheshkumar Sabhnani
Issue Date:August 2005
pp. 218-227
We propose a new class of spatio-temporal cluster detection methods designed for the rapid detection of emerging space-time clusters. We focus on the motivating application of prospective disease surveillance: detecting space-time clusters of disease cases...
     
Anomaly pattern detection in categorical datasets
Found in: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '08)
By Daniel B. Neill, Jeff Schneider, Kaustav Das
Issue Date:August 2008
pp. 5-6
We propose a new method for detecting patterns of anomalies in categorical datasets. We assume that anomalies are generated by some underlying process which affects only a particular subset of the data. Our method consists of two steps: we first use a "loc...
     
Rapid detection of significant spatial clusters
Found in: Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '04)
By Andrew W. Moore, Daniel B. Neill
Issue Date:August 2004
pp. 256-265
Given an N x N grid of squares, where each square has a count cij and an underlying population pij, our goal is to find the rectangular region with the highest density, and to calculate its significance by randomization. An arbitrary density function D, de...
     
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