Sequential minimal optimization algorithm for learning using partially available privileged information

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.