Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence最新文献

A Deployed Online Reinforcement Learning Algorithm In An Oral Health Clinical Trial. 在口腔健康临床试验中部署在线强化学习算法。

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2025-01-01 Epub Date: 2025-04-11 DOI: 10.1609/aaai.v39i28.35143

Anna L Trella, Kelly W Zhang, Hinal Jajal, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez, Susan A Murphy

{"title":"A Deployed Online Reinforcement Learning Algorithm In An Oral Health Clinical Trial.","authors":"Anna L Trella, Kelly W Zhang, Hinal Jajal, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez, Susan A Murphy","doi":"10.1609/aaai.v39i28.35143","DOIUrl":"10.1609/aaai.v39i28.35143","url":null,"abstract":"Dental disease is a prevalent chronic condition associated with substantial financial burden, personal suffering, and increased risk of systemic diseases. Despite widespread recommendations for twice-daily tooth brushing, adherence to recommended oral self-care behaviors remains sub-optimal due to factors such as forgetfulness and disengagement. To address this, we developed Oralytics, a mHealth intervention system designed to complement clinician-delivered preventative care for marginalized individuals at risk for dental disease. Oralytics incorporates an online reinforcement learning algorithm to determine optimal times to deliver intervention prompts that encourage oral self-care behaviors. We have deployed Oralytics in a registered clinical trial. The deployment required careful design to manage challenges specific to the clinical trials setting in the U.S. In this paper, we (1) highlight key design decisions of the RL algorithm that address these challenges and (2) conduct a re-sampling analysis to evaluate algorithm design decisions. A second phase (randomized control trial) of Oralytics is planned to start in spring 2025.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"39 28","pages":"28792-28800"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12122013/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144201029","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

Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness. 在具有无限过时性的联邦学习中处理交织的数据和设备异构性。

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2025-01-01 Epub Date: 2025-04-11 DOI: 10.1609/aaai.v39i20.35405

Haoming Wang, Wei Gao

{"title":"Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness.","authors":"Haoming Wang, Wei Gao","doi":"10.1609/aaai.v39i20.35405","DOIUrl":"10.1609/aaai.v39i20.35405","url":null,"abstract":"Federated Learning (FL) can be affected by data and device heterogeneities, caused by clients' different local data distributions and latencies in uploading model updates (i.e., staleness). Traditional schemes consider these heterogeneities as two separate and independent aspects, but this assumption is unrealistic in practical FL scenarios where these heterogeneities are intertwined. In these cases, traditional FL schemes are ineffective, and a better approach is to convert a stale model update into a unstale one. In this paper, we present a new FL framework that ensures the accuracy and computational efficiency of this conversion, hence effectively tackling the intertwined heterogeneities that may cause unlimited staleness in model updates. Our basic idea is to estimate the distributions of clients' local training data from their uploaded stale model updates, and use these estimations to compute unstale client model updates. In this way, our approach does not require any auxiliary dataset nor the clients' local models to be fully trained, and does not incur any additional computation or communication overhead at client devices. We compared our approach with the existing FL strategies on mainstream datasets and models, and showed that our approach can improve the trained model accuracy by up to 25% and reduce the number of required training epochs by up to 35%. Source codes can be found at: https://github.com/pittisl/FL-with-intertwined-heterogeneity.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"39 20","pages":"21080-21089"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12116981/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176001","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

Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health. 利用适应性匪徒实验来提高和调查心理健康方面的参与度。

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2024-03-25 Epub Date: 2024-03-24 DOI: 10.1609/aaai.v38i21.30328

Harsh Kumar, Tong Li, Jiakai Shi, Ilya Musabirov, Rachel Kornfield, Jonah Meyerhoff, Ananya Bhattacharjee, Chris Karr, Theresa Nguyen, David Mohr, Anna Rafferty, Sofia Villar, Nina Deliu, Joseph Jay Williams

{"title":"Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health.","authors":"Harsh Kumar, Tong Li, Jiakai Shi, Ilya Musabirov, Rachel Kornfield, Jonah Meyerhoff, Ananya Bhattacharjee, Chris Karr, Theresa Nguyen, David Mohr, Anna Rafferty, Sofia Villar, Nina Deliu, Joseph Jay Williams","doi":"10.1609/aaai.v38i21.30328","DOIUrl":"https://doi.org/10.1609/aaai.v38i21.30328","url":null,"abstract":"Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support. While these interventions can be effective, real-world experimental testing can further enhance their design and impact. Adaptive experimentation, utilizing algorithms like Thompson Sampling for (contextual) multi-armed bandit (MAB) problems, can lead to continuous improvement and personalization. However, it remains unclear when these algorithms can simultaneously increase user experience rewards and facilitate appropriate data collection for social-behavioral scientists to analyze with sufficient statistical confidence. Although a growing body of research addresses the practical and statistical aspects of MAB and other adaptive algorithms, further exploration is needed to assess their impact across diverse real-world contexts. This paper presents a software system developed over two years that allows text-messaging intervention components to be adapted using bandit and other algorithms while collecting data for side-by-side comparison with traditional uniform random non-adaptive experiments. We evaluate the system by deploying a text-message-based DMH intervention to 1100 users, recruited through a large mental health non-profit organization, and share the path forward for deploying this system at scale. This system not only enables applications in mental health but could also serve as a model testbed for adaptive experimentation algorithms in other domains.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"38 21","pages":"22906-22912"},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11044947/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140866976","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

Pharmacokinetics-Informed Neural Network for Predicting Opioid Administration Moments with Wearable Sensors. 利用可穿戴传感器预测阿片类药物用药时刻的药代动力学神经网络。

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2024-02-01 Epub Date: 2024-03-24 DOI: 10.1609/aaai.v38i21.30326

Bhanu Teja Gullapalli, Stephanie Carreiro, Brittany P Chapman, Eric L Garland, Tauhidur Rahman

{"title":"Pharmacokinetics-Informed Neural Network for Predicting Opioid Administration Moments with Wearable Sensors.","authors":"Bhanu Teja Gullapalli, Stephanie Carreiro, Brittany P Chapman, Eric L Garland, Tauhidur Rahman","doi":"10.1609/aaai.v38i21.30326","DOIUrl":"10.1609/aaai.v38i21.30326","url":null,"abstract":"Long-term and high-dose prescription opioid use places individuals at risk for opioid misuse, opioid use disorder (OUD), and overdose. Existing methods for monitoring opioid use and detecting misuse rely on self-reports, which are prone to reporting bias, and toxicology testing, which may be infeasible in outpatient settings. Although wearable technologies for monitoring day-to-day health metrics have gained significant traction in recent years due to their ease of use, flexibility, and advancements in sensor technology, their application within the opioid use space remains underexplored. In the current work, we demonstrate that oral opioid administrations can be detected using physiological signals collected from a wrist sensor. More importantly, we show that models informed by opioid pharmacokinetics increase reliability in predicting the timing of opioid administrations. Forty-two individuals who were prescribed opioids as a part of their medical treatment in-hospital and after discharge were enrolled. Participants wore a wrist sensor throughout the study, while opioid administrations were tracked using electronic medical records and self-reports. We collected 1,983 hours of sensor data containing 187 opioid administrations from the inpatient setting and 927 hours of sensor data containing 40 opioid administrations from the outpatient setting. We demonstrate that a self-supervised pre-trained model, capable of learning the canonical time series of plasma concentration of the drug derived from opioid pharmacokinetics, can reliably detect opioid administration in both settings. Our work suggests the potential of pharmacokinetic-informed, data-driven models to objectively detect opioid use in daily life.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"38 21","pages":"22892-22898"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11027727/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140861820","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

IGAMT: Privacy-Preserving Electronic Health Record Synthesization with Heterogeneity and Irregularity. IGAMT：具有异质性和不规则性的隐私保护电子病历综合。

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2024-01-01 Epub Date: 2024-03-24 DOI: 10.1609/aaai.v38i14.29491

Wenjie Wang, Pengfei Tang, Jian Lou, Yuanming Shao, Lance Waller, Yi-An Ko, Li Xiong

{"title":"IGAMT: Privacy-Preserving Electronic Health Record Synthesization with Heterogeneity and Irregularity.","authors":"Wenjie Wang, Pengfei Tang, Jian Lou, Yuanming Shao, Lance Waller, Yi-An Ko, Li Xiong","doi":"10.1609/aaai.v38i14.29491","DOIUrl":"https://doi.org/10.1609/aaai.v38i14.29491","url":null,"abstract":"Utilizing electronic health records (EHR) for machine learning-driven clinical research has great potential to enhance outcome predictions and treatment personalization. Nonetheless, due to privacy and security concerns, the secondary use of EHR data is regulated, constraining researchers' access to EHR data. Generating synthetic EHR data with deep learning methods is a viable and promising approach to mitigate privacy concerns, offering not only a supplementary resource for downstream applications but also sidestepping the privacy risks associated with real patient data. While prior efforts have concentrated on EHR data synthesis, significant challenges persist: addressing the heterogeneity of features including temporal and non-temporal features, structurally missing values, and irregularity of the temporal measures, and ensuring rigorous privacy of the real data used for model training. Existing works in this domain only focused on solving one or two aforementioned challenges. In this work, we propose IGAMT, an innovative framework to generate privacy-preserved synthetic EHR data that not only maintains high quality with heterogeneous features, missing values, and irregular measures but also achieves differential privacy with enhanced privacy-utility trade-off. Extensive experiments prove that IGAMT significantly outperforms baseline and state-of-the-art models in terms of resemblance to real data and performance of downstream applications. Ablation studies also prove the effectiveness of the techniques applied in IGAMT.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"38 14","pages":"15634-15643"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11606572/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142775537","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

Erratum to: 3D-TOGO: Towards Text-Guided Cross-Category 3D Object Generation 更正:3D- togo:迈向文本引导的跨类别3D对象生成

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-09-06 DOI: 10.1609/aaai.v37i13.27320

Zutao Jiang, Guansong Lu, Xiaodan Liang, Jihua Zhu, Wei Zhang, Xiaojun Chang, Hang Xu

引用次数: 0

Multimodal Deep Generative Models for Remote Medical Applications 远程医疗应用的多模态深度生成模型

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i13.26924

Catherine Ordun

引用次数: 0

McOmet: Multimodal Fusion Transformer for Physical Audiovisual Commonsense Reasoning 物理视听常识推理的多模态融合变压器

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i5.25813

Daoming Zong, Shiliang Sun

{"title":"McOmet: Multimodal Fusion Transformer for Physical Audiovisual Commonsense Reasoning","authors":"Daoming Zong, Shiliang Sun","doi":"10.1609/aaai.v37i5.25813","DOIUrl":"https://doi.org/10.1609/aaai.v37i5.25813","url":null,"abstract":"Physical commonsense reasoning is essential for building reliable and interpretable AI systems, which involves a general understanding of the physical properties and affordances of everyday objects, how these objects can be manipulated, and how they interact with others. It is fundamentally a multi-modal task, as physical properties are manifested through multiple modalities, including vision and acoustics. In this work, we present a unified framework, named Multimodal Commonsense Transformer (MCOMET), for physical audiovisual commonsense reasoning. MCOMET has two intriguing properties: i) it fully mines higher-ordered temporal relationships across modalities (e.g., pairs, triplets, and quadruplets); and ii) it restricts the cross-modal flow through the feature collection and propagation mechanism along with tight fusion bottlenecks, forcing the model to attend the most relevant parts in each modality and suppressing the dissemination of noisy information. We evaluate our model on a very recent public benchmark, PACS. Results show that MCOMET significantly outperforms a variety of strong baselines, revealing powerful multi-modal commonsense reasoning capabilities.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"42 1","pages":"6621-6629"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75194890","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: 1

Revisiting Unsupervised Local Descriptor Learning 回顾无监督局部描述符学习

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i3.25367

Wu‐ru Wang, Lei Zhang, Hua Huang

{"title":"Revisiting Unsupervised Local Descriptor Learning","authors":"Wu‐ru Wang, Lei Zhang, Hua Huang","doi":"10.1609/aaai.v37i3.25367","DOIUrl":"https://doi.org/10.1609/aaai.v37i3.25367","url":null,"abstract":"Constructing accurate training tuples is crucial for unsupervised local descriptor learning, yet challenging due to the absence of patch labels. The state-of-the-art approach constructs tuples with heuristic rules, which struggle to precisely depict real-world patch transformations, in spite of enabling fast model convergence. A possible solution to alleviate the problem is the clustering-based approach, which can capture realistic patch variations and learn more accurate class decision boundaries, but suffers from slow model convergence. This paper presents HybridDesc, an unsupervised approach that learns powerful local descriptor models with fast convergence speed by combining the rule-based and clustering-based approaches to construct training tuples. In addition, HybridDesc also contributes two concrete enhancing mechanisms: (1) a Differentiable Hyperparameter Search (DHS) strategy to find the optimal hyperparameter setting of the rule-based approach so as to provide accurate prior for the clustering-based approach, (2) an On-Demand Clustering (ODC) method to reduce the clustering overhead of the clustering-based approach without eroding its advantage. Extensive experimental results show that HybridDesc can efficiently learn local descriptors that surpass existing unsupervised local descriptors and even rival competitive supervised ones.","PeriodicalId":74506,"journal":{"name":"Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence","volume":"26 1","pages":"2680-2688"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75665780","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: 0

FC-TrackNet: Fast Convergence Net for 6D Pose Tracking in Synthetic Domains FC-TrackNet:用于合成域6D姿态跟踪的快速收敛网络

Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence Pub Date : 2023-06-26 DOI: 10.1609/aaai.v37i13.27077

Di Jia, Qianqian Wang, Jun Cao, Peng Cai, Zhiyang Jin

引用次数: 0