The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 30, 2011

Filed:

May. 29, 2008
Applicants:

Glenn Fung, Madison, WI (US);

Phan Hong Giang, Downingtown, PA (US);

Harald Steck, Phoenixville, PA (US);

R. Bharat Rao, Berwyn, PA (US);

Inventors:

Glenn Fung, Madison, WI (US);

Phan Hong Giang, Downingtown, PA (US);

Harald Steck, Phoenixville, PA (US);

R. Bharat Rao, Berwyn, PA (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 17/00 (2006.01); G06N 5/02 (2006.01);
U.S. Cl.
CPC ...
Abstract

A method for predicting survival rates of medical patients includes providing a set D of survival data for a plurality of medical patients, providing a regression model having an associated parameter vector β, providing an example xof a medical patient whose survival probability is to be classified, calculating a parameter vector {circumflex over (β)} that maximizes a log-likelihood function of β over the set of survival data, l(β|D), wherein the log likelihood l(β|D) is a strictly concave function of β and is a function of the scalar xβ, calculating a weight wfor example x, calculating an updated parameter vector β* that maximizes a function l(β|D∪{(y,x,w)}), wherein data points (y,x,w) augment set D, calculating a fair log likelihood ratio λfrom {circumflex over (β)} and β* using λ=λ(β*|x)+sign(λ({circumflex over (β)}|x)){l({circumflex over (β)}|D)−l(β*|D)}, and mapping the fair log likelihood ratio λto a fair price y, wherein said fair price is a probability that class label yfor example xhas a value of 1.


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