Motivated by applications in digital health, this work studies the novel problem of online uniform sampling (OUS), where the goal is to distribute a sampling budget uniformly across unknown decision times. In the OUS problem, the algorithm is given a budget $b$ and a time horizon $T$, and an adversary then chooses a value $τ^* \in [b,T]$, which is revealed to the algorithm online. At each decision time $i \in [τ^*]$, the algorithm must determine a sampling probability that maximizes the budget spent throughout the horizon, respecting budget constraint $b$, while achieving as uniform a distribution as possible over $τ^*$. We present the first randomized algorithm designed for this problem and subsequently extend it to incorporate learning augmentation. We provide worst-case approximation guarantees for both algorithms, and illustrate the utility of the algorithms through both synthetic experiments and a real-world case study involving the HeartSteps mobile application. Our numerical results show strong empirical average performance of our proposed randomized algorithms against previously proposed heuristic solutions.
Constrained Optimization via Exact Augmented Lagrangian and Randomized Iterative Sketching
University of Chicago（芝加哥大学）；University of California, Berkeley（加州大学伯克利分校）；International Computer Science Institute（国际计算机科学研究所）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
On Curating Responsible and Representative Healthcare Video Recommendations for Patient Education and Health Literacy: An Augmented Intelligence Approach
研究表明，三分之一的美国成年人使用互联网来诊断或了解健康问题。然而，在线获取保健信息可能会加剧保健信息提供和使用方面的差距。健康信息寻求行为（Health Information Seeking Behavior，HISB）是指个体寻求有关其健康、风险、疾病和健康保护行为的信息的方式。对于在数字媒体平台上搜索健康信息的患者来说，健康素养的鸿沟可能会因为他们自己缺乏知识和算法建议而加剧，结果会对弱势群体，少数民族和低健康素养用户产生不成比例的影响。这项研究报告了对上述挑战的探索性调查，通过研究是否可以使用先进的分析方法生成负责任和有代表性的建议，这些方法适用于YouTube社交媒体平台上的慢性疾病（糖尿病）的大型视频及其元数据。该文件的重点是与使用糖尿病视频的演员的人口特征相关的偏见，这些视频是根据多个标准检索和策划的，例如编码的医疗内容及其可理解性，以满足患者教育和人口健康素养需求。这种方法通过将卫生专业人员和患者的观点结合到一个可扩展和可推广的机器学习框架中，为患者赋权和改善健康结果，为人类在环，增强智能，偏见意识和负责任的算法建议的创新提供了巨大的机会。
An evaluation of randomized machine learning methods for redundant data: Predicting short and medium-term suicide risk from administrative records and risk assessments
KAT-GNN: A Knowledge-Augmented Temporal Graph Neural Network for Risk Prediction in Electronic Health Records
Institute of Computer Science and Engineering, National Yang Ming Chiao Tung University（计算机科学与工程研究所，国立阳明交通大学）；School of Medicine, National Tsing Hua University（医学院，国立清华大学）；Department of Computer Science, National Yang Ming Chiao Tung University（计算机科学系，国立阳明交通大学）；Computational Health Informatics Program, Boston Children’s Hospital（计算健康信息学计划，波士顿儿童医院）
Augmented Risk Prediction for the Onset of Alzheimer's Disease from Electronic Health Records with Large Language Models
Michigan State University（密歇根州立大学）；Weill Cornell Medicine, Cornell University（康奈尔大学威尔医学院）；Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院）
