Displaying publications 1 to 7 of 7 publications associated with the Machine Learning Group in 2015:
Authors: Bilal Ahmed, Carla E. Brodley, Karen E. Blackmon, Ruben Kuzniecky, Gilad Barash, Chad Carlson, Brian T. Quinn, Werner Doyle, Jacqueline French, Orrin Devinsky, Thomas Thesen
Epilepsy & Behavior
Abstract: Focal cortical dysplasia (FCD) is the most common cause of pediatric epilepsy and the third most common lesion in adults with treatment-resistant epilepsy. Advances in MRI have revolutionized the diagnosis of FCD, resulting in higher success rates for resective epilepsy surgery. However, many patients with histologically confirmed FCD have normal presurgical MRI studies (‘MRI-negative’), making presurgical diagnosis difficult. The purpose of this study was to test whether a novel MRI postprocessing method successfully detects histopathologically verified FCD in a sample of patients without visually appreciable lesions. We applied an automated quantitative morphometry approach which computed five surface-based MRI features and combined them in a machine learning model to classify lesional and nonlesional vertices. Accuracy was defined by classifying contiguous vertices as “lesional” when they fell within the surgical resection region. Our multivariate method correctly detected the lesion in 6 of 7 MRI-positive patients, which is comparable with the detection rates that have been reported in univariate vertex-based morphometry studies. More significantly, in patients that were MRI-negative, machine learning correctly identified 14 out of 24 FCD lesions (58%). This was achieved after separating abnormal thickness and thinness into distinct classifiers, as well as separating sulcal and gyral regions. Results demonstrate that MRI-negative images contain sufficient information to aid in the in vivo detection of visually elusive FCD lesions.
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: H. Cui, R. Khardon, A. Fern, and P. Tadepalli.
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)
Authors: M. Issakkimuthu, A. Fern, R. Khardon, P. Tadepalli, and S. Xue
Proceedings of the International Conference on Automated Planning and
Authors: A. Raghavan, R. Khardon, P. Tadepalli, and A. Fern
Proceedings of the International Conference on
Uncertainty in Artificial Intelligence (UAI)
Authors: R. Sheth, Y. Wang and R. Khardon
Proceedings of the International Conference on Machine Learning (ICML)
Authors: Benjamin J. Hescott and Roni Khardon
Abstract: Journal version is at http://dx.doi.org/10.1016/j.artint.2015.08.005