Methods and systems for predicting occurrence of an event
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Accused Products
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.
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Citations
53 Claims
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1-35. -35. (canceled)
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36. Apparatus for predicting occurrence of a medical condition in a patient under consideration comprising:
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a neural network having weighted connections, an input and an output, said weighted connections resulting from training said neural network; wherein said input is configured to receive data for said patient under consideration and, based on said weighted connections, said neural network is configured to provide at said output a prognostic indicator of the risk of occurrence of the medical condition in said patient; and wherein said neural network is trained with an objective function C for providing a rating of the performance of the neural network, wherein the objective function C is a differentiable approximation of the concordance index, said training of said neural network with the objective function C comprising conducting pair-wise comparisons between prognostic indicators from said neural network of pairs of patients i and j from a training dataset comprising both censored and non-censored data and adapting said weighted connections of said neural network as a result of said comparisons, said pairs of patients from said training dataset comprising; patients i and j who have both experienced the medical condition, and the time ti to occurrence of the medical condition of patient i is shorter than the time tj to occurrence of the medical condition of patient j; and patients i and j where only patient i has experienced the medical condition, and the time ti to occurrence of the medical condition in patient i is shorter than a follow-up visit time tj for patient j. - View Dependent Claims (37, 38, 39, 40, 41)
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42. A method for predicting occurrence of a medical condition in a patient under consideration:
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inputting data into a neural network having weighted connections in order to produce an output, said weighted connections resulting from training said neural network; wherein said inputting data comprises inputting data for said patient under consideration and said output comprises a prognostic indicator indicative of the risk of occurrence of the medical condition in said patient; and wherein said training said neural network comprises training said neural network with an objective function C that provides a rating of the performance of the neural network, wherein the objective function C is a differentiable approximation of the concordance index, said training of said neural network with the objective function C comprising conducting pair-wise comparisons between prognostic indicators from said neural network of pairs of patients i and j from a training dataset comprising both censored and non-censored data and adapting said weighted connections of said neural network as a result of said comparisons, said pairs of patients from said training dataset comprising; patients i and j who have both experienced the medical condition and the time ti to occurrence of the medical condition of patient i is shorter than the time tj to occurrence of the medical condition of patient j; and patients i and j where only patient i has experienced the medical condition and the time ti to occurrence of the medical condition in patient i is shorter than a follow-up visit time tj for patient j. - View Dependent Claims (43, 44, 45, 46, 47)
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48. Computer readable media comprising computer instructions for causing a computer to perform the method comprising:
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inputting data into a neural network having weighted connections in order to produce an output, said weighted connections resulting from training said neural network; wherein said inputting data comprises inputting data for said patient under consideration and said output comprises a prognostic indicator indicative of the risk of occurrence of the medical condition in said patient; and wherein said training said neural network comprises training said neural network with an objective function C that provides a rating of the performance of the neural network, wherein the objective function C is an differentiable approximation of the concordance index, said training of said neural network with the objective function C comprising conducting pair-wise comparisons between prognostic indicators from said neural network of pairs of patients i and j from a training dataset comprising both censored and non-censored data and adapting said weighted connections of said neural network as a result of said comparisons, said pairs of patients from said training dataset comprising; patients i and j who have both experienced the medical condition and the time ti to occurrence of the medical condition of patient i is shorter than the time tj to occurrence of the medical condition of patient j; and patients i and j where only patient i has experienced the medical condition and the time ti to occurrence of the medical condition in patient i is shorter than a follow-up visit time tj for patient j. - View Dependent Claims (49, 50, 51, 52, 53)
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Specification