Events

Learning of classification models often relies on data that are labeled/annotated by a human expert. In general, more expertise and time the labeling process requires, more costly it is to label the data.  In addition, there may be constraints on how many data instances one expert can feasibly label. Our goal is to find ways of reducing the number of labels and at the same time preserve or improve the quality of  the models based on such labels. In this talk, I present two solutions we have developed to  address the above problems.

Biomarkers are objectively measured characteristics which are commonly used across a range of scientific disciplines for diagnosis, prognosis, and prediction, and potentially as surrogate measures for the actual clinical outcome. The utility of biomarker studies, however, is typically limited to evaluating associations rather than causal relationships.

Learning health care systems (LHCS) propose to advance health care by taking advantage of increasing efficiencies in data collection and analysis and information dissemination. A major premise of LHCS is broad and continuous access to patient data, extracted at the point of care, and stored and used primarily in de-identified form.