Confident Surgical Decision Making in Temporal Lobe Epilepsy by Heterogeneous Classifier Ensembles

  title={Confident Surgical Decision Making in Temporal Lobe Epilepsy by Heterogeneous Classifier Ensembles},
  author={Shobeir Fakhraei and H. Soltanian-Zadeh and K. Jafari-Khouzani and K. Elisevich and F. Fotouhi},
  journal={2011 IEEE 11th International Conference on Data Mining Workshops},
In medical domains with low tolerance for invalid predictions, classification confidence is highly important and traditional performance measures such as overall accuracy cannot provide adequate insight into classifications reliability. In this paper, a confident-prediction rate (CPR) which measures the upper limit of confident predictions has been proposed based on receiver operating characteristic (ROC) curves. It has been shown that heterogeneous ensemble of classifiers improves this measure… Expand
3 Citations
Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy
The main advantage of the present work over previous studies is the inclusion of the extent of ipsilateral neocortical gray matter atrophy and spatiotemporal properties of depth electrode-recorded seizures as training features for individual patient surgery planning. Expand
Title Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy
Background: This study sought to predict postsurgical seizure freedom from pre-operative diagnostic test results and clinical information using a rapid automated approach, based on supervisedExpand
Quantitative analysis of structural neuroimaging of mesial temporal lobe epilepsy.
The results suggest that structural imaging and sophisticated imaging analysis could provide important information to identify networks capable of generating spontaneous seizures and ultimately help guide surgical therapy that improves postsurgical seizure-freedom outcomes. Expand


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