Automated learning of patients demographics can be seen as multi-label problem where a patient model is based on different race and gender groups. The resulting model can be further integrated into Privacy-Preserving Data Mining, where it can be used to assess risk of identification of different patient groups. Our project considers relations between diabetes and demographics of patients as a multi-labelled problem. Most research in this area has been done as binary classification, where the target class is finding if a person has diabetes or not. But very few, and maybe no work has been done in multi-labeled analysis of the demographics of patients who are likely to be diagnosed with diabetes. To identify such groups, we applied ensembles of several multi-label learning algorithms.
SweetDeep: A Wearable AI Solution for Real-Time Non-Invasive Diabetes Screening
Department of Electrical and Computer Engineering, Princeton University（普林斯顿大学电气与计算机工程系）；NeuTigers, Inc.（NeuTigers公司）；AP-HP Avicennes, Bobigny, France（法国AP-HP阿维尼翁医院）；Clinic International Constantine, Constantine, Algeria（阿尔及利亚康斯坦丁国际诊所）
Fairness in Multi-modal Medical Diagnosis with Demonstration Selection
Arizona State University（亚利桑那州立大学）；University of Rochester（罗切斯特大学）；University of Virginia（弗吉尼亚大学）；UCL（伦敦大学学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Central Florida（佛罗里达中央大学）
MCLPD:Multi-view Contrastive Learning for EEG-based PD Detection Across Datasets
Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, Shanghai, P. R. China（能源化学过程智能制造重点实验室，教育部，上海，中华人民共和国）；East China University of Science and Technology, Shanghai, P. R. China（东华大学）
Boosting multi-demographic federated learning for chest radiograph analysis using general-purpose self-supervised representations
University Hospital RWTH Aachen（亚琛工业大学附属医院）；Hamedan University of Technology（哈马丹理工大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；Stanford University（斯坦福大学）
