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:
Oct. 24, 2017

Filed:

Aug. 03, 2016
Applicants:

Singapore Health Services Pte Ltd, Singapore, SG;

Nanyang Technological University, Singapore, SG;

Inventors:

Marcus Eng Hock Ong, Singapore, SG;

Zhiping Lin, Singapore, SG;

Wee Ser, Singapore, SG;

Guangbin Huang, Singapore, SG;

Assignees:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/00 (2006.01); A61B 5/145 (2006.01); G06N 3/08 (2006.01); G06F 19/00 (2011.01); G06N 3/04 (2006.01); A61B 5/0205 (2006.01); A61B 5/0402 (2006.01); A61B 5/01 (2006.01); A61B 5/04 (2006.01); A61B 5/0456 (2006.01); A61B 5/1455 (2006.01); A61B 5/024 (2006.01); A61B 5/021 (2006.01); A61B 5/08 (2006.01);
U.S. Cl.
CPC ...
A61B 5/7275 (2013.01); A61B 5/01 (2013.01); A61B 5/0205 (2013.01); A61B 5/02055 (2013.01); A61B 5/02405 (2013.01); A61B 5/0402 (2013.01); A61B 5/04014 (2013.01); A61B 5/0456 (2013.01); A61B 5/14542 (2013.01); A61B 5/14551 (2013.01); A61B 5/4824 (2013.01); A61B 5/4836 (2013.01); A61B 5/6801 (2013.01); A61B 5/7264 (2013.01); A61B 5/7267 (2013.01); A61B 5/742 (2013.01); G06F 19/345 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); A61B 5/021 (2013.01); A61B 5/0816 (2013.01);
Abstract

A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.


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