Publications by authors named "Marco Benjumeda"
Article Synopsis
- The study focuses on improving predictions for seizure freedom after surgery for drug-resistant temporal lobe epilepsy (TLE) using multidimensional Bayesian network classifiers (MBCs), which are advanced probabilistic models.
- Data from 231 TLE patients across two institutions were analyzed, and the MBC model showed modest predictive performance, achieving an area under the curve (AUC) of 0.67 to 0.72 at various post-surgery time points, outperforming traditional methods like logistic regression.
- The MBC's ability to capture complex relationships between clinical data and surgical outcomes suggests it may enhance pre-operative counseling for TLE surgery, with potential for further improvement using additional data.
