| An explainable and statistically validated ensemble clustering model applied to the identification of traumatic brain injury subgroups D Yeboah, L Steinmeister, DB Hier, B Hadi, DC Wunsch, GR Olbricht, ... IEEE Access 8, 180690-180705, 2020 | 22 | 2020 |
| Heterogeneity in blood biomarker trajectories after mild TBI revealed by unsupervised learning LA Bui, D Yeboah, L Steinmeister, S Azizi, DB Hier, DC Wunsch, ... IEEE/ACM transactions on computational biology and bioinformatics 19 (3 …, 2021 | 9 | 2021 |
| Statistical comparative analysis and evaluation of validation indices for clustering optimization T Nguyen, J Viehman, D Yeboah, GR Olbricht, T Obafemi-Ajayi 2020 IEEE symposium series on computational intelligence (SSCI), 3081-3090, 2020 | 5 | 2020 |
| Connecting phenotype to genotype: PheWAS-inspired analysis of autism spectrum disorder J Matta, D Dobrino, D Yeboah, S Howard, Y El-Manzalawy, ... Frontiers in Human Neuroscience 16, 960991, 2022 | 2 | 2022 |
| A deep learning model to predict traumatic brain injury severity and outcome from MR images D Yeboah, H Nguyen, DB Hier, GR Olbricht, T Obafemi-Ajayi 2021 IEEE Conference on Computational Intelligence in Bioinformatics and …, 2021 | 2 | 2021 |
| Handling missing data for unsupervised learning with an application on a FITBIR Traumatic Brain Injury (TBI) Dataset L Steinmeister, D Yeboah, G Olbricht, T Obafemi-Ajayi, B Hadi, D Hier, ... | 2 | 2020 |
| A pheWAS model of autism spectrum disorder J Matta, D Dobrino, S Howard, D Yeboah, J Kopel, Y El-Manzalawy, ... 2021 43rd Annual International Conference of the IEEE Engineering in …, 2021 | 1 | 2021 |
| Predicting Severity of Traumatic Brain Injury: A Residual Learning Model from Magnetic Resonance Images D Yeboah | | 2021 |
