KDD 2024 Workshop AIDSH Submissions

Artificial Intelligence in Traditional Chinese Medicine: Past, Present and Future
Tian Xu, Liqiang Ni, Huaiqiong Zhang
- Published: 29 Jun 2024, Last Modified: 19 Jul 2024
- KDD-AIDSH 2024 Poster
- Readers: Everyone
Effectiveness of a digital quantitative assessment of protein-to-creatinine ratio using computer-vision technologies in proteinuria screening
Jingyi Wu, Yuewen Zheng, Chunmiao Zhou, Fei Wang, Qing Li, Pengfei Li, Luxia Zhang
- Published: 29 Jun 2024, Last Modified: 03 Jul 2024
- KDD-AIDSH 2024 Oral
- Readers: Everyone
$$Predict the Risk of Dyslipidemia via Deep Neural Networks for Survival Data$$
Jiahui Xiao
- Published: 29 Jun 2024, Last Modified: 03 Jul 2024
- KDD-AIDSH 2024 Poster
- Readers: Everyone
JSCDS: A Core Data Selection Method with Jason-Shannon Divergence for Caries RGB Images-Efficient Learning
Peiliang Zhang, Yujia Tong, Chenghu Du, Chao Che, Yongjun Zhu
- Published: 29 Jun 2024, Last Modified: 16 Jul 2024
- KDD-AIDSH 2024 Poster
- Readers: Everyone
Multi-Channel Masked Autoencoder and Comprehensive Evaluations for Reconstructing 12-Lead ECG from Arbitrary Single-Lead ECG
Jiarong Chen, Wanqing Wu, Shenda Hong
- Published: 29 Jun 2024, Last Modified: 16 Jul 2024
- KDD-AIDSH 2024 Poster
- Readers: Everyone
In Medical Claims Data, Enhancing Predictive Performance for Major Adverse Cardiovascular Events Using Cross Attention
Yuhei Fujioka, Daitaro Misawa, Tatsuyoshi Ikenoue, Shingo Fukuma
- Published: 29 Jun 2024, Last Modified: 10 Jul 2024
- KDD-AIDSH 2024 Oral
- Readers: Everyone
LINT: LLM Interaction Network for Clinical Trial Outcome Prediction
Chufan Gao, Tianfan Fu, Jimeng Sun
- Published: 29 Jun 2024, Last Modified: 09 Jul 2024
- KDD-AIDSH 2024 Poster
- Readers: Everyone
Development and Validation of an Explainable Machine Learning-Based Model for Predicting the Interval Growth of Pulmonary Subsolid Nodules: A Prospective Multicenter Cohort Study
Zhedong Zhang
- Published: 29 Jun 2024, Last Modified: 03 Jul 2024
- KDD-AIDSH 2024 Poster
- Readers: Everyone