machine learning

To determine whether anomaly-driven approaches to discovery produce more accurate models than the standard approaches, we built a program called Kalpana. We also used Kalpana to explore means for identifying those anomaly resolutions that are acceptable to domain experts.

An explanation generator implementing (part of) John Stuart Mill’s Method of Induction was constructed that divides the available data into meaningful subsets to better resolve the anomalies. We found that using relevant subsets of data can provide plausible explanations not generated when using all the data and that identifying plausible explanations can help select among equally possible revisions.

Addressing the challenge of extracting negated terms from medical records.