Support vector regression for censored data
First Claim
1. A computer-implemented method of producing a model for use in predicting time to occurrence of a health-related condition, the method comprising:
- obtaining multi-dimensional, non-linear vectors of information indicative of status of multiple test subjects, at least one of the vectors being right-censored, lacking an indication of a time of occurrence of the health-related condition with respect to the corresponding test subject; and
performing regression using the vectors of information to produce a kernel-based model to provide an output value related to a prediction of time to the occurrence of the health-related condition based upon at least some of the information contained in the vectors of information;
wherein for each vector comprising right-censored data, a censored-data penalty function is used to affect the regression, the censored-data penalty function being different than a non-censored-data penalty function used for each vector comprising non-censored data;
wherein performing the regression includes using penalty functions that include linear functions of a difference between a predicted value of the model and a target value for the predicted value, and wherein a first slope of the linear function for positive differences between the predicted and target values for the censored-data penalty function is lower than a second slope of the linear function for positive differences between the predicted and target values for the non-censored-data penalty function.
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Abstract
A method of producing a model for use in predicting time to an event includes obtaining multi-dimensional, non-linear vectors of information indicative of status of multiple test subjects, at least one of the vectors being right-censored, lacking an indication of a time of occurrence of the event with respect to the corresponding test subject, and performing regression using the vectors of information to produce a kernel-based model to provide an output value related to a prediction of time to the event based upon at least some of the information contained in the vectors of information, where for each vector comprising right-censored data, a censored-data penalty function is used to affect the regression, the censored-data penalty function being different than a non-censored-data penalty function used for each vector comprising non-censored data.
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Citations
38 Claims
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1. A computer-implemented method of producing a model for use in predicting time to occurrence of a health-related condition, the method comprising:
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obtaining multi-dimensional, non-linear vectors of information indicative of status of multiple test subjects, at least one of the vectors being right-censored, lacking an indication of a time of occurrence of the health-related condition with respect to the corresponding test subject; and performing regression using the vectors of information to produce a kernel-based model to provide an output value related to a prediction of time to the occurrence of the health-related condition based upon at least some of the information contained in the vectors of information; wherein for each vector comprising right-censored data, a censored-data penalty function is used to affect the regression, the censored-data penalty function being different than a non-censored-data penalty function used for each vector comprising non-censored data; wherein performing the regression includes using penalty functions that include linear functions of a difference between a predicted value of the model and a target value for the predicted value, and wherein a first slope of the linear function for positive differences between the predicted and target values for the censored-data penalty function is lower than a second slope of the linear function for positive differences between the predicted and target values for the non-censored-data penalty function. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 21, 22, 23, 24, 25, 26, 27, 28, 29)
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12. A computer program product producing a model for use in predicting time to occurrence of a health-related condition, the computer program product residing on a computer readable medium, the computer program product comprising computer-readable, computer-executable instructions for causing a computer to:
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obtain multi-dimensional, non-linear vectors of information indicative of status of multiple test subjects, at least one of the vectors being right-censored, lacking an indication of a time of occurrence of the health-related condition with respect to the corresponding test subject; and perform regression using the vectors of information to produce a kernel-based model to provide an output value related to a prediction of time to the occurrence of the health-related condition based upon at least some of the information contained in the vectors of information; wherein for each vector comprising right-censored data, a censored-data penalty function is used to affect the regression, the censored-data penalty function being different than a non-censored-data penalty function used for each vector comprising non-censored data; wherein the instructions for causing the computer to perform the regression include instructions for causing the computer to use penalty functions that include linear functions of a difference between a predicted value of the model and a target value for the predicted value, and wherein a first slope of the linear function for positive differences between the predicted and target values for the censored-data penalty function is lower than a second slope of the linear function for positive differences between the predicted and target values for the non-censored-data penalty function. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 30, 31, 32, 33, 34, 35, 36, 37, 38)
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Specification