Steven W Rust

US Patent:

20020164070, Nov 7, 2002

Filed:

Mar 13, 2002

Appl. No.:

10/097198

Inventors:

Mark Kuhner - Upper Arlington OH, US
David Burgoon - Columbus OH, US
Paul Keller - Richland WA, US
Steven Rust - Worthington OH, US
Jean Schelhorn - Granville Township OH, US
Loraine Sinnott - Columbus OH, US
Gregory Stark - Columbus OH, US
Kevin Taylor - Upper Arlington OH, US
Paul Whitney - Richland WA, US

International Classification:

G06K009/62

US Classification:

382/159000, 382/190000, 382/224000

Abstract:

Several approaches are provided for designing algorithms that allow for fast retrieval, classification, analysis or other processing of data, with minimal expert knowledge of the data being analyzed, and further, with minimal expert knowledge of the math and science involved in building classifications and performing other statistical data analysis. Further, methods of analyzing data are provided where the information being analyzed is not easily susceptible to quantitative description.

US Patent:

20030028504, Feb 6, 2003

Filed:

May 8, 2001

Appl. No.:

09/850998

Inventors:

David Burgoon - Columbus OH, US
Steven Rust - Worthington OH, US
Owen Chang - Columbus OH, US
Loraine Sinnott - Columbus OH, US
Stuart Rose - Richland WA, US
Elizabeth Hetzler - Kennewick WA, US
Lucille Nowell - Richland WA, US

International Classification:

G06F007/00

US Classification:

707/001000

Abstract:

A cluster isolation system includes a processor that is operatively coupled to an input device, an output device, and memory. The system determines feature/interval combinations that distinguish one cluster of data objects from other clusters. The processor calculates cluster isolation measurement values at selected cut-off values for each feature. The processor reports the features and feature score intervals that satisfy selected isolation measurement value thresholds.

US Patent:

20040111410, Jun 10, 2004

Filed:

Oct 14, 2003

Appl. No.:

10/684975

Inventors:

David Burgoon - Columbus OH, US
Mark Davis - Sunbury OH, US
Kevin Dorow - Kennewick WA, US
Todd Hitt - Worthington OH, US
Douglas Mooney - Columbus OH, US
Steven Rust - Worthington OH, US
Loraine Sinnott - Columbus OH, US

International Classification:

G06F007/00

US Classification:

707/004000

Abstract:

Approximate answers to queries are provided by executing queries against a representation of a data source in addition to, or in lieu of accessing the source data itself. A representation of a data source, referred to herein as an Information Reservoir, is constructed and maintained using probabilistic methodologies based upon a Poisson sampling approach. The Information Reservoir provides approximate answers to ad hoc queries, potentially in a small fraction of the time required to calculate an exact answer. Associated variances are also provided that may additionally be used to calculate confidence intervals bounding the exact answer. An Information Reservoir may be biased toward a subset of the information in the original data source and/or tailored to the anticipated query workload. Queries expressed as if directed to the original data source may be automatically translated to run against the Information Reservoir with little or no additional burden placed on the Information Reservoir user. Information Reservoir collections may be created that offer users approximate answers of varying levels of precision. Information Reservoirs may also be combined with non-sampling concise representations to increase the precision of approximate answers for certain classes of queries. For example, approximations to specific multidimensional histograms may be combined with an Information Reservoir to accommodate highly selective queries that sampling does not effectively address.

US Patent:

20210142915, May 13, 2021

Filed:

Jan 25, 2021

Appl. No.:

17/157218

Inventors:

- Columbus OH, US
Steven W. Rust - Worthington OH, US

International Classification:

G16H 50/50
G16H 50/30

Abstract:

Predictive models are built for the estimation of adverse health likelihood by identifying candidate model risk variables, constructing a model form for an outcome likelihood model that estimates the likelihood of an adverse outcome type using a group of risk variables selected from the set of candidate model risk variables and by classifying each selected risk variable into either a baseline group or a dynamic group. Additionally, predictive models are built by constructing separate baseline and dynamic outcome likelihood model forms and by fitting the constructed model forms to a training data set to produce final models to be used as scoring functions that compute a baseline outcome likelihood and a dynamic outcome likelihood for patient data that is not represented in the training data set. The predictive models can be used with alerting and attribution algorithms to predict the likelihood of an adverse outcome for individuals receiving care.

US Patent:

20150120623, Apr 30, 2015

Filed:

May 29, 2013

Appl. No.:

14/404734

Inventors:

- Columbus OH, US
Steven W. Rust - Worthington OH, US
Mark D. Davis - Sunbury OH, US
Joseph Regensburger - Grove City OH, US

International Classification:

G06N 5/02
G06N 99/00

US Classification:

706 12

Abstract:

A covariance-clustering algorithm for partitioning a graph into sub-graphs (clusters) using variations of the pseudo-inverse of the Laplacian matrix (A) associated with the graph. The algorithm does not require the number of clusters as an input parameter and, considering the covariance of the Markov field associated with the graph, algorithm finds sub-graphs characterized by a within-cluster covariance larger than an across-clusters covariance. The covariance-clustering algorithm is applied to a semantic graph representing the simulated evidence of multiple events.

US Patent:

20150112710, Apr 23, 2015

Filed:

Dec 20, 2014

Appl. No.:

14/578396

Inventors:

- Columbus OH, US
Steven W. Rust - Worthington OH, US

International Classification:

G06F 19/00

US Classification:

705 2

Abstract:

Predictive models are built for the estimation of adverse health likelihood by identifying candidate model risk variables, constructing a model form for an outcome likelihood model that estimates the likelihood of an adverse outcome type using a group of risk variables selected from the set of candidate model risk variables and by classifying each selected risk variable into either a baseline group or a dynamic group. Additionally, predictive models are built by constructing separate baseline and dynamic outcome likelihood model forms and by fitting the constructed model forms to a training data set to produce final models to be used as scoring functions that compute a baseline outcome likelihood and a dynamic outcome likelihood for patient data that is not represented in the training data set. The predictive models can be used with alerting and attribution algorithms to predict the likelihood of an adverse outcome for individuals receiving care.