Methods and systems for predicting occurrence of an event

• US 20050197982A1
• Filed: 02/25/2005
• Published: 09/08/2005
• Est. Priority Date: 02/27/2004
• Status: Active Grant

• Current Assignee: David Sans
• Original Assignee: ATC Technology Corporation (FedEx Corporation)
• Inventors: Verbel, David A., Saidi, Olivier, Yan, Lian
• Granted Patent: US 7,321,881 B2
• Time in Patent Office: Days
• 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 supervised prognostic model for a neural network comprising:

a neural network having weighted connections, an input for receiving training data and an output;

an error module for determining an error between output of the neural network and a desired output from the training data;

an objective function for providing a rating of the performance of the learning model, the objective function comprising a function C substantially in accordance with an approximation of the concordance index; and

a training algorithm for adapting the weighted connections of the neural network in accordance with the results of the objective function.

2. The supervised model according to claim 1, wherein the function C is substantially in accordance with a derivative of the concordance index.

3. The supervised model according to claim 1, wherein the function C allows for the use of censored training data.

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

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

6. The supervised model according to claim 4, wherein the model is used to predict recurrence of cancer, and wherein the pairs of data comprise patients {i, j } and 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 supervised model according to claim 5, wherein the model is used to predict recurrence of cancer and wherein the pairs of data comprise patients {i, j } and 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. 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.

9. The method according to claim 8, 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.

10. The method according to claim 8, 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.

11. The method according to claim 8, wherein the function C comprises

12. The method according to claim 8, wherein the function C comprises

13. The method 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 method 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.

15. A method for training a neural network having weighted connections for classification of data comprising:

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 of the 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.

16. The method according to claim 15, wherein rating the performance of the neural network using an objective function comprises rating the performance of the neural network using an objective function comprising a function C substantially in accordance with a derivative of the concordance index.

17. The method according to claim 15, wherein rating the performance of the neural network using an objective function comprises rating the performance of the neural network using an objective function comprising a function C that allows for the use of censored data in the training.

18. The method according to claim 15, wherein the function C comprises

19. The method according to claim 15, wherein the function C comprises:

20. The method according to claim 18, wherein the neural network is used to predict the recurrence of cancer and wherein the pairs of data comprise patients {i, j } and 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.

21. The method according to claim 19, wherein the neural network is used to predict the recurrence of cancer and wherein the pairs of data comprise patients {i, j } and 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.

22. 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.

23. The computer readable media of claim 22, 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.

24. The computer readable media of claim 22, 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.

25. The computer readable media according to claim 22, wherein the function C comprises

26. The computer readable media according to claim 22, wherein the function C comprises

27. The computer readable media according to claim 25, 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.

28. The computer readable media according to claim 26, 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 i'"'"'s follow-up visit time t_j.

29. Computer readable media comprising computer instructions for training a neural network having weighted connections, the method comprising:

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 of the 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.

30. The computer readable media according to claim 29, wherein rating the performance of the neural network comprises rating the performance of the neural network using an objective function, wherein the objective function comprises a function C substantially in accordance with a derivative of the concordance index.

31. The computer readable media according to claim 29, wherein rating the performance of the neural network comprises rating the performance of the neural network using an objective function, wherein the objective function comprises a function C that allows for the use of censored data in the training data.

32. The computer readable media according to claim 29, wherein the function C comprises

33. The computer readable media according to claim 29, wherein the function C comprises:

34. The computer readable media according to claim 32, wherein the neural network is trained to predict recurrence of cancer, the pairs of data comprise patients {i, j } and 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 experienced recurrence and t_i is shorter than patient j'"'"'s follow-up visit time t_j.

35. The computer readable media according to claim 33, wherein the neural network is trained to predict recurrence of cancer, the pairs of data comprises patients {i, j} and 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 experienced recurrence and t_i is shorter than patient j'"'"'s follow-up visit time t_j.