Methods and systems for predicting occurrence of an event

• US 7,321,881 B2
• Filed: 02/25/2005
• Issued: 01/22/2008
• Est. Priority Date: 02/27/2004
• Status: Active Grant

• Current Assignee: David Sans
• Original Assignee: Aureon Biosciences, Inc.
• Inventors: Verbel, David A., Saidi, Olivier, Yan, Lian
• Primary Examiner(s): Holmes; Michael B.
• Application Number:
  US11/067,066
• Publication Number:
  US 20050197982A1
• Time in Patent Office: 1,061 Days
• Field of Search: 706/21
• US Class Current: 706/21
• CPC Class Codes: G06N 3/084 Backpropagation, e.g. using gradient descent

Abstract

Embodiments of the present invention are directed to methods and systems for training a neural network having weighted connections for classification of data, as well as embodiments corresponding to the use of such a neural network for the classification of data, including, for example, prediction of an event (e.g., disease). The method may include inputting input training data into the neural network, processing, by the neural network, the input training data to produce an output, determining an error between the output and a desired output corresponding to the input training data, rating the performance neural network using an objective function, wherein the objective function comprises a function C substantially in accordance with an approximation of the concordance index and adapting the weighted connections of the neural network based upon results of the objective function.

Claims

1. A method for predicting recurrence of cancer in a patient comprising:

estimating the probability that cancer will recur within a shorter period of time in a patient with a higher prognostic score than a patient with a lower prognostic score, wherein estimating comprises conducting pair-wise comparisons between prognostic scores for patients i and j, using a neural network trained using an objective function comprising a function C substantially in accordance with an approximation of the concordance index.

2. The method according to claim 1, wherein using a neural network trained using an objective function comprises using a neural network trained using an objective function comprising a function C substantially in accordance with a derivative of the concordance index.

3. The method according to claim 1, wherein using a neural network trained using an objective function comprises using a neural network trained using an objective function comprising a function C that allows for the use of censored data in the training.

4. The method according to claim 1, wherein the function C comprises

5. The method according to claim 1, wherein the function C comprises

6. The method according to claim 4, wherein the predetermined conditions comprise at least:

both patients i and j have experienced recurrence and the recurrence time t_i of patient i is shorter than the recurrence time t_j of patient j; and

patient i has experienced recurrence and t_i is shorter than patient j'"'"'s follow-up visit time t_j.

7. The method according to claim 5, wherein the predetermined conditions comprise at least:

both patients i and j have experienced recurrence and the recurrence time t_i of patient i is shorter than the recurrence time t_j of patient j; and

patient i has experienced recurrence and t_i is shorter than patient j'"'"'s follow-up visit time t_j.

8. Computer readable media comprising computer instructions for allowing a computer system to perform a method for predicting recurrence of cancer in a patient, the method comprising estimating the probability that cancer will recur within a shorter period of time in a patient with a higher prognostic score than a patient with a lower prognostic score, wherein estimating comprises conducting pair-wise comparisons between prognostic scores for patients i and j, using a neural network trained using an objective function comprising a function C substantially in accordance with an approximation of the concordance index.

9. The computer readable media of claim 8, wherein using a neural network comprises using a neural network trained using an objective function comprising a function C substantially in accordance with a derivative of the concordance index.

10. The computer readable media of claim 8, wherein using a neural network comprises using a neural network trained using an objective function comprising a function C that allows for the use of censored data in the training.

11. The computer readable media according to claim 8, wherein the function C comprises

12. The computer readable media according to claim 8, wherein the function C comprises

13. The computer readable media according to claim 11, wherein the predetermined conditions comprise at least:

both patients i and j have experienced recurrence and the recurrence time t_i of patient i is shorter than the recurrence time t_j of patient j; and

patient i has experienced recurrence and t_i is shorter than patient j'"'"'s follow-up visit time t_j.

14. The computer readable media according to claim 12, wherein the predetermined conditions comprise at least:

both patients i and j have experienced recurrence and the recurrence time t_i of patient i is shorter than the recurrence time t_j of patient j; and

patient i has experienced recurrence and t_i is shorter than patient j'"'"'s follow-up visit time t_j.