Description: We are working with researchers from Harvard Medical School to predict outcomes for multiple sclerosis patients. A focus of the research is how best to interact with physicians to use both human expertise and machine learning methods.
Collaborators: Dr. Tanuja Chitnis, Neurology, Harvard Medical School and Dr. Brian Healy, Biostatistic, Partners MS.
Authors: Yijun Zhao, Tanuja Chitnis, Brian C. Healy, Jennifer G. Dy, Carla E. Brodley
IEEE International Conference on Data Mining (ICDM)
Abstract: Predicting disease course is critical in chronic progressive diseases such as multiple sclerosis (MS) for determining treatment. Forming an accurate predictive model based on clinical data is particularly challenging when data is gathered from multiple clinics/physicians as the labels vary with physicians’ subjective judgment about clinical tests and further we have no a priori knowledge of the various types of physician subjectivity. At the same time, we often have some (limited) domain knowledge on how to group patients into disease progression subgroups. In this paper, we first present our rationale for choosing a Dirichlet mixture of Gaussian processes (DPMGP) model to address the subjectivity in our data. We then introduce a new approach to incorporating domain knowledge into the non-parametric mixture model. We demonstrate the efficacy of our model by applying it to two medical datasets to predict disease progression in MS patients and disability levels in early Parkinson’s patients.
Authors: Yijun Zhao, Carla E. Brodley, Tanuja Chitnis, Brian C. Healy
Proceedings of the 2014 SIAM International Conference on Data Mining
Authors: Liu, J., Brodley, C. E., Healy, B., Chitnis, T.
Data Mining for Healthcare Workshop at ICHI