| Lead-agnostic self-supervised learning for local and global representations of electrocardiogram J Oh, H Chung, J Kwon, D Hong, E Choi Conference on Health, Inference, and Learning, 338-353, 2022 | 33 | 2022 |
| Unifying heterogeneous electronic health records systems via text-based code embedding K Hur, J Lee, J Oh, W Price, Y Kim, E Choi Conference on Health, Inference, and Learning, 183-203, 2022 | 14 | 2022 |
| EHRXQA: A multi-modal question answering dataset for electronic health records with chest x-ray images S Bae, D Kyung, J Ryu, E Cho, G Lee, S Kweon, J Oh, L Ji, E Chang, ... Advances in Neural Information Processing Systems 36, 2024 | 6 | 2024 |
| GenHPF: General Healthcare Predictive Framework for Multi-task Multi-source Learning K Hur, J Oh, J Kim, J Kim, MJ Lee, E Cho, SE Moon, YH Kim, L Atallah, ... IEEE Journal of Biomedical and Health Informatics, 2023 | 5 | 2023 |
| Unihpf: Universal healthcare predictive framework with zero domain knowledge K Hur, J Oh, J Kim, J Kim, MJ Lee, E Cho, SE Moon, YH Kim, E Choi arXiv preprint arXiv:2211.08082, 2022 | 5 | 2022 |
| Gradually Applying Weakly Supervised and Active Learning for Mass Detection in Breast Ultrasound Images JY Yun, JW Oh, ID Yun Applied Sciences 10 (13), 4519, 2020 | 4 | 2020 |
| ECG-QA: A comprehensive question answering dataset combined with electrocardiogram J Oh, S Bae, G Lee, J Kwon, E Choi arXiv preprint arXiv:2306.15681, 2023 | 3 | 2023 |
| Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes S Kweon, J Kim, J Kim, S Im, E Cho, S Bae, J Oh, G Lee, JH Moon, ... arXiv preprint arXiv:2309.00237, 2023 | 2 | 2023 |
| Learning under Label Noise through Few-Shot Human-in-the-Loop Refinement A Saeed, D Spathis, J Oh, E Choi, A Etemad arXiv preprint arXiv:2401.14107, 2024 | 1 | 2024 |
| Medical Event Data Standard (MEDS): Facilitating Machine Learning for Health B Arnrich, E Choi, JA Fries, MBA McDermott, J Oh, TJ Pollard, N Shah, ... | | |
