Sequential minimal optimization algorithm for learning using partially available privileged information

1. A computer-implemented method of developing a classifier model for identifying a medical condition of a subject, the method comprising:

receiving training data for a plurality of subjects, where the training data includes sensed physiological data, imaging data, demographic data, and/or other clinical data, where at least some of the training data is for subjects having the medical condition and at least some of the training data is for subjects not having the medical condition, wherein the training data includes privileged information where at least a portion of the privileged information is label uncertain privileged information;

identifying the label uncertain privileged information;

applying a penalty parameter to each label uncertain privileged information, wherein the penalty parameter establishes a soft-margin decisional boundary for the label uncertain privileged information;

performing an optimization on (i) the label uncertain privileged information having the penalty parameters, (ii) the privileged information in the training data that has certain labels, and (iii) non-privileged information in training data, to develop a mapping to an outcome determination of the medical condition; and

generating a classifier model based on the mapping to classify testing data as corresponding to the presence of the medical condition or to the absence of the medical condition.

2. The computer-implemented method of claim 1 , further comprising:

applying the training data to a support vector machine (SVM) framework configured to map training data to a vector space and generate a decision hyperplane classifier model for identifying the presence of the medical condition and for identifying the absence of the medical condition.

3. The computer-implemented method of claim 2 , further comprising:

configuring the support vector machine (SVM) framework to map privileged information in the training data and non-privileged information in the training data to the vector space for generating the decision hyperplane classifier model.

4. The computer-implemented method of claim 2 , wherein the training data is expressed as:

( x 1 ,x* 1 ,y 1 ,π 1 ), . . . ,( x m ,x* m ,y m ,π m ),( x m+1 ,y m+1 ,π m+1 ),( x m+2 ,y m+2 ,π m+2 ), . . . ,( x n ,y n ,π n )

x i ∈X,x* i ∈X*,y i ∈{−1,1},π i ∈

5. The computer-implemented method of claim 4 , further comprising:

applying the penalty parameter by performing a correction function for identifying hyperplane parameters; and

generating the classifier model by simultaneous optimization of the following expression:

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6. The computer-implemented method of claim 4 , further comprising:

applying the penalty parameter by performing a correction function for identifying hyperplane parameters; and

generating the classifier model by simultaneous optimization of the following expression:

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7. The computer-implemented method of claim 4 , further comprising:

applying the penalty parameter by performing a correction function for identifying hyperplane parameters; and

generating the classifier model by simultaneous optimization of the following expression:

min

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,

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8. The computer-implemented method of claim 1 , further comprising performing the optimization by applying a sequential minimal optimization to (i) the label uncertain privileged information having the penalty parameters, (ii) the privileged information in the training data that has certain labels, and (iii) non-privileged information in training data, to develop a mapping to an outcome determination of the medical condition.

9. The computer-implemented method of claim 1 , wherein the medical condition is acute respiratory distress syndrome.

10. The computer-implemented method of claim 8 , wherein the physiological data comprises continuous electrocardiogram (ECG) and/or photoplethysmography (PPG) data.

11. The computer-implemented method of claim 8 , wherein the image data comprises: chest x-ray image data.