Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data. - Min Ju Kang; Sang Yun Kim; Duk L. Na; Byeong C. Kim; Dong Won Yang; Eun-Joo Kim; Hae Ri Na; Hyun Jeong Han; Jae-Hong Lee; Jong Hun Kim; Kee Hyung Park; Kyung Won Park; et al. (BMC Medical Informatics Decis. Mak., 2019)

Deep learning for EEG data analytics: A survey. - Gen Li; Chang Ha Lee; Jason J. Jung; Young Chul Youn; David Camacho (Concurr. Comput. Pract. Exp., 2020)

Prediction of amyloid PET positivity via machine learning algorithms trained with EDTA-based blood amyloid-β oligomerization data. - Young Chul Youn; Hye Ryoun Kim; Hae Won Shin; Hae-Bong Jeong; Sang-Won Han; Jung-Min Pyun; Nayoung Ryoo; Young Ho Park 004; Sangyun Kim (BMC Medical Informatics Decis. Mak., 2022)

Deep learning-based EEG analysis to classify normal, mild cognitive impairment, and dementia: Algorithms and dataset. - Min-jae Kim; Young Chul Youn; Joonki Paik (NeuroImage, 2023)