On Whom Should I Perform this Lab Test Next? An Active Feature Elicitation Approach
Published in Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018
Recommended citation: S. Natarajan, S. Das, N. Ramanan, G. Kunapuli and P. Radivojac. On Whom Should I Perform this Lab Test Next? An Active Feature Elicitation Approach . Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden. http://gkunapuli.github.io/files/18afeIJCAI.pdf
We consider the problem of active feature elicitation in which, given some examples with all the features (say, the full Electronic Health Record), and many examples with some of the features (say, demographics), the goal is to identify the set of examples on which more information (say, lab tests) need to be collected. The observation is that some set of features may be more expensive, personal or cumbersome to collect. We propose a classifier independent, similarity metric-independent, general active learning approach which identifies examples that are dissimilar to the ones with the full set of data and acquire the complete set of features for these examples. Motivated by four real clinical tasks, our extensive evaluation demonstrates the effectiveness of this approach.