IJCAI : proceedings of the conference最新文献

Predictive Modeling with Temporal Graphical Representation on Electronic Health Records. 利用电子健康记录的时态图形表示进行预测建模。

IJCAI : proceedings of the conference Pub Date : 2024-08-01 DOI: 10.24963/ijcai.2024/637

Jiayuan Chen, Changchang Yin, Yuanlong Wang, Ping Zhang

{"title":"Predictive Modeling with Temporal Graphical Representation on Electronic Health Records.","authors":"Jiayuan Chen, Changchang Yin, Yuanlong Wang, Ping Zhang","doi":"10.24963/ijcai.2024/637","DOIUrl":"10.24963/ijcai.2024/637","url":null,"abstract":"Deep learning-based predictive models, leveraging Electronic Health Records (EHR), are receiving increasing attention in healthcare. An effective representation of a patient's EHR should hierarchically encompass both the temporal relationships between historical visits and medical events, and the inherent structural information within these elements. Existing patient representation methods can be roughly categorized into sequential representation and graphical representation. The sequential representation methods focus only on the temporal relationships among longitudinal visits. On the other hand, the graphical representation approaches, while adept at extracting the graph-structured relationships between various medical events, fall short in effectively integrate temporal information. To capture both types of information, we model a patient's EHR as a novel temporal heterogeneous graph. This graph includes historical visits nodes and medical events nodes. It propagates structured information from medical event nodes to visit nodes and utilizes time-aware visit nodes to capture changes in the patient's health status. Furthermore, we introduce a novel temporal graph transformer (TRANS) that integrates temporal edge features, global positional encoding, and local structural encoding into heterogeneous graph convolution, capturing both temporal and structural information. We validate the effectiveness of TRANS through extensive experiments on three real-world datasets. The results show that our proposed approach achieves state-of-the-art performance.","PeriodicalId":73334,"journal":{"name":"IJCAI : proceedings of the conference","volume":"2024 ","pages":"5763-5771"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11446542/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142367720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recent Advances in Predictive Modeling with Electronic Health Records. 电子健康记录预测建模的最新进展。

IJCAI : proceedings of the conference Pub Date : 2024-08-01 DOI: 10.24963/ijcai.2024/914

Jiaqi Wang, Junyu Luo, Muchao Ye, Xiaochen Wang, Yuan Zhong, Aofei Chang, Guanjie Huang, Ziyi Yin, Cao Xiao, Jimeng Sun, Fenglong Ma

引用次数: 0

ReBandit: Random Effects Based Online RL Algorithm for Reducing Cannabis Use. ReBandit：基于随机效应的在线RL算法减少大麻使用。

IJCAI : proceedings of the conference Pub Date : 2024-08-01

Susobhan Ghosh, Yongyi Guo, Pei-Yao Hung, Lara Coughlin, Erin Bonar, Inbal Nahum-Shani, Maureen Walton, Susan Murphy

{"title":"ReBandit: Random Effects Based Online RL Algorithm for Reducing Cannabis Use.","authors":"Susobhan Ghosh, Yongyi Guo, Pei-Yao Hung, Lara Coughlin, Erin Bonar, Inbal Nahum-Shani, Maureen Walton, Susan Murphy","doi":"","DOIUrl":"","url":null,"abstract":"The escalating prevalence of cannabis use, and associated cannabis-use disorder (CUD), poses a significant public health challenge globally. With a notably wide treatment gap, especially among emerging adults (EAs; ages 18-25), addressing cannabis use and CUD remains a pivotal objective within the 2030 United Nations Agenda for Sustainable Development Goals (SDG). In this work, we develop an online reinforcement learning (RL) algorithm called reBandit which will be utilized in a mobile health study to deliver personalized mobile health interventions aimed at reducing cannabis use among EAs. reBandit utilizes random effects and informative Bayesian priors to learn quickly and efficiently in noisy mobile health environments. Moreover, reBandit employs Empirical Bayes and optimization techniques to autonomously update its hyper-parameters online. To evaluate the performance of our algorithm, we construct a simulation testbed using data from a prior study, and compare against commonly used algorithms in mobile health studies. We show that reBandit performs equally well or better than all the baseline algorithms, and the performance gap widens as population heterogeneity increases in the simulation environment, proving its adeptness to adapt to diverse population of study participants.","PeriodicalId":73334,"journal":{"name":"IJCAI : proceedings of the conference","volume":"2024 ","pages":"7278-7286"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11671148/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142904166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stabilizing and Enhancing Link Prediction through Deepened Graph Auto-Encoders. 通过深化图自动编码器稳定并增强链接预测。

IJCAI : proceedings of the conference Pub Date : 2022-07-01 DOI: 10.24963/ijcai.2022/498

Xinxing Wu, Qiang Cheng

引用次数: 0

Human Gaze Assisted Artificial Intelligence: A Review. 人类凝视辅助人工智能：综述。

IJCAI : proceedings of the conference Pub Date : 2020-07-01 DOI: 10.24963/ijcai.2020/689

Ruohan Zhang, Akanksha Saran, Bo Liu, Yifeng Zhu, Sihang Guo, Scott Niekum, Dana Ballard, Mary Hayhoe

引用次数: 43

Mixed-Variable Bayesian Optimization 混合变量贝叶斯优化

IJCAI : proceedings of the conference Pub Date : 2020-01-01 DOI: 10.24963/ijcai.2020/365

Erik A. Daxberger, Anastasia Makarova, M. Turchetta, Andreas Krause

{"title":"Mixed-Variable Bayesian Optimization","authors":"Erik A. Daxberger, Anastasia Makarova, M. Turchetta, Andreas Krause","doi":"10.24963/ijcai.2020/365","DOIUrl":"https://doi.org/10.24963/ijcai.2020/365","url":null,"abstract":"The optimization of expensive to evaluate, black-box, mixed-variable functions, i.e. functions that have continuous and discrete inputs, is a difficult and yet pervasive problem in science and engineering. In Bayesian optimization (BO), special cases of this problem that consider fully continuous or fully discrete domains have been widely studied. However, few methods exist for mixed-variable domains and none of them can handle discrete constraints that arise in many real-world applications. In this paper, we introduce MiVaBo, a novel BO algorithm for the efficient optimization of mixed-variable functions combining a linear surrogate model based on expressive feature representations with Thompson sampling. We propose an effective method to optimize its acquisition function, a challenging problem for mixed-variable domains, making MiVaBo the first BO method that can handle complex constraints over the discrete variables. Moreover, we provide the first convergence analysis of a mixed-variable BO algorithm. Finally, we show that MiVaBo is significantly more sample efficient than state-of-the-art mixed-variable BO algorithms on several hyperparameter tuning tasks, including the tuning of deep generative models.","PeriodicalId":73334,"journal":{"name":"IJCAI : proceedings of the conference","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86161869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 35

DDL: Deep Dictionary Learning for Predictive Phenotyping. DDL：用于预测表型的深度字典学习。

IJCAI : proceedings of the conference Pub Date : 2019-08-01 DOI: 10.24963/ijcai.2019/812

Tianfan Fu, Trong Nghia Hoang, Cao Xiao, Jimeng Sun

引用次数: 0

Learning Disentangled Semantic Representation for Domain Adaptation 面向领域自适应的解纠缠语义表示学习

IJCAI : proceedings of the conference Pub Date : 2019-08-01 DOI: 10.24963/ijcai.2019/285

Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, Z. Hao

引用次数: 86

Learning Disentangled Semantic Representation for Domain Adaptation. 学习用于领域适应的分离语义表征

IJCAI : proceedings of the conference Pub Date : 2019-08-01

Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, Zhifeng Hao

引用次数: 0

Exploring Computational User Models for Agent Policy Summarization. 探索用于Agent策略总结的计算用户模型。

IJCAI : proceedings of the conference Pub Date : 2019-08-01

Isaac Lage, Daphna Lifschitz, Finale Doshi-Velez, Ofra Amir

{"title":"Exploring Computational User Models for Agent Policy Summarization.","authors":"Isaac Lage, Daphna Lifschitz, Finale Doshi-Velez, Ofra Amir","doi":"","DOIUrl":"","url":null,"abstract":"AI agents support high stakes decision-making processes from driving cars to prescribing drugs, making it increasingly important for human users to understand their behavior. Policy summarization methods aim to convey strengths and weaknesses of such agents by demonstrating their behavior in a subset of informative states. Some policy summarization methods extract a summary that optimizes the ability to reconstruct the agent's policy under the assumption that users will deploy inverse reinforcement learning. In this paper, we explore the use of different models for extracting summaries. We introduce an imitation learning-based approach to policy summarization; we demonstrate through computational simulations that a mismatch between the model used to extract a summary and the model used to reconstruct the policy results in worse reconstruction quality; and we demonstrate through a human-subject study that people use different models to reconstruct policies in different contexts, and that matching the summary extraction model to these can improve performance. Together, our results suggest that it is important to carefully consider user models in policy summarization.","PeriodicalId":73334,"journal":{"name":"IJCAI : proceedings of the conference","volume":"28 ","pages":"1401-1407"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901848/pdf/nihms-1067306.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25402383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0