No abstract available.
Embolism, Air ; Magnetic Resonance Imaging

A 48-year-old woman presented with a 1-day history of aggressive behavior. Hashimoto encephalopathy was first suspected based on elevated levels of serum anti-thyroid peroxidase antibody. Her clinical symptoms did not improve despite treatment with intravenous corticosteroid. Abdominal computed tomography revealed liver cirrhosis, and brain T2-weighted magnetic resonance imaging revealed midbrain hyperintensity, and she was finally diagnosed with Wilson’s disease. The Wilson’s disease should be considered in the differential diagnosis in adults presenting with unexplained hepatic, neurological, or psychiatric abnormalities.

No abstract available.
Astrocytoma* ; Encephalitis, Herpes Simplex* ; Herpes Simplex*

Objectives: The objective of this study was to develop models for predicting obstructive sleep apnea (OSA) based on easily obtainable clinical information of patients using various machine learning techniques. Methods: We used a data set that included the records of 1,368 patients, in which 1,074 patients were male (78.5 %), and 294 patients were female (21.5 %). We randomly divided the data into a training set (1,000) and test set (368). Five machine learning methods, i.e., support vector machine model, lasso logit model, naïve bayes, discriminant analysis, and K-nearest neighbor (KNN), with a 10-cross fold technique were used with the proposed model to predict OSA. We evaluated the accuracy, sensitivity, specificity, and precision of each model for three thresholds [Apnea-Hypopnea Index (AHI)≥5, AHI≥15, and AHI≥30]. Results: Among the machine learning techniques, KNN showed the best results compared to the other techniques. The accuracy, sensitivity, specificity, and precision of OSA prediction were 87.0%, 91.0%, 74.4%, and 91.9%, respectively, based on AHI≥5. When the threshold of OSA was AHI≥15 or AHI≥30, KNN provided lower accuracy (79.6% each) and precision (79.0% and 68.7%), which were still higher than those of the other techniques. Conclusions The model derived from the KNN technique exhibited the best performance based on its highest level of accuracy. We demonstrate that this model is a useful tool for predicting OSA.

Although genetic Creutzfeldt-Jakob disease (CJD) is a rare neurodegenerative disorder, cases of genetic CJD with E200K mutation are being increasingly reported in Korea. However, the clinical features and course of genetic CJD with E200K mutation in Korea remain unclear. We describe the clinical features and course of genetic CJD with E200K mutation in a patient who initially presented with rapid progressive memory impairment and myoclonus.

Objectives: The objective of this study was to develop models for predicting obstructive sleep apnea (OSA) based on easily obtainable clinical information of patients using various machine learning techniques. Methods: We used a data set that included the records of 1,368 patients, in which 1,074 patients were male (78.5 %), and 294 patients were female (21.5 %). We randomly divided the data into a training set (1,000) and test set (368). Five machine learning methods, i.e., support vector machine model, lasso logit model, naïve bayes, discriminant analysis, and K-nearest neighbor (KNN), with a 10-cross fold technique were used with the proposed model to predict OSA. We evaluated the accuracy, sensitivity, specificity, and precision of each model for three thresholds [Apnea-Hypopnea Index (AHI)≥5, AHI≥15, and AHI≥30]. Results: Among the machine learning techniques, KNN showed the best results compared to the other techniques. The accuracy, sensitivity, specificity, and precision of OSA prediction were 87.0%, 91.0%, 74.4%, and 91.9%, respectively, based on AHI≥5. When the threshold of OSA was AHI≥15 or AHI≥30, KNN provided lower accuracy (79.6% each) and precision (79.0% and 68.7%), which were still higher than those of the other techniques. Conclusions The model derived from the KNN technique exhibited the best performance based on its highest level of accuracy. We demonstrate that this model is a useful tool for predicting OSA.

Although genetic Creutzfeldt-Jakob disease (CJD) is a rare neurodegenerative disorder, cases of genetic CJD with E200K mutation are being increasingly reported in Korea. However, the clinical features and course of genetic CJD with E200K mutation in Korea remain unclear. We describe the clinical features and course of genetic CJD with E200K mutation in a patient who initially presented with rapid progressive memory impairment and myoclonus.