KDD : proceedings. International Conference on Knowledge Discovery & Data Mining最新文献

A Deep Subgrouping Framework for Precision Drug Repurposing via Emulating Clinical Trials on Real-world Patient Data. 通过模拟真实世界患者数据的临床试验实现精确药物再利用的深度亚分组框架。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2025-08-01 Epub Date: 2025-07-20 DOI: 10.1145/3690624.3709418

Seungyeon Lee, Ruoqi Liu, Feixiong Cheng, Ping Zhang

{"title":"A Deep Subgrouping Framework for Precision Drug Repurposing via Emulating Clinical Trials on Real-world Patient Data.","authors":"Seungyeon Lee, Ruoqi Liu, Feixiong Cheng, Ping Zhang","doi":"10.1145/3690624.3709418","DOIUrl":"10.1145/3690624.3709418","url":null,"abstract":"Drug repurposing identifies new therapeutic uses for existing drugs, reducing the time and costs compared to traditional de novo drug discovery. Most existing drug repurposing studies using real-world patient data often treat the entire population as homogeneous, ignoring the heterogeneity of treatment responses across patient subgroups. This approach may overlook promising drugs that benefit specific subgroups but lack notable treatment effects across the entire population, potentially limiting the number of repurposable candidates identified. To address this, we introduce STEDR, a novel drug repurposing framework that integrates subgroup analysis with treatment effect estimation. Our approach first identifies repurposing candidates by emulating multiple clinical trials on real-world patient data and then characterizes patient subgroups by learning subgroup-specific treatment effects. We deploy STEDR to Alzheimer's Disease (AD), a condition with few approved drugs and known heterogeneity in treatment responses. We emulate trials for over one thousand medications on a large-scale real-world database covering over 8 million patients, identifying 14 drug candidates with beneficial effects to AD in characterized subgroups. Experiments demonstrate STEDR's superior capability in identifying repurposing candidates compared to existing approaches. Additionally, our method can characterize clinically relevant patient subgroups associated with important AD-related risk factors, paving the way for precision drug repurposing.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2025 v1","pages":"2347-2358"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12001032/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060313","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

SepsisCalc: Integrating Clinical Calculators into Early Sepsis Prediction via Dynamic Temporal Graph Construction. SepsisCalc：通过动态时间图构建将临床计算器集成到早期脓毒症预测中。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2025-08-01 Epub Date: 2025-07-20 DOI: 10.1145/3690624.3709402

Changchang Yin, Shihan Fu, Bingsheng Yao, Thai-Hoang Pham, Weidan Cao, Dakuo Wang, Jeffrey Caterino, Ping Zhang

{"title":"SepsisCalc: Integrating Clinical Calculators into Early Sepsis Prediction via Dynamic Temporal Graph Construction.","authors":"Changchang Yin, Shihan Fu, Bingsheng Yao, Thai-Hoang Pham, Weidan Cao, Dakuo Wang, Jeffrey Caterino, Ping Zhang","doi":"10.1145/3690624.3709402","DOIUrl":"10.1145/3690624.3709402","url":null,"abstract":"Sepsis is an organ dysfunction caused by a deregulated immune response to an infection. Early sepsis prediction and identification allow for timely intervention, leading to improved clinical outcomes. Clinical calculators (e.g., the six-organ dysfunction assessment of SOFA in Figure 1) play a vital role in sepsis identification within clinicians' workflow, providing evidence-based risk assessments essential for sepsis diagnosis. However, artificial intelligence (AI) sepsis prediction models typically generate a single sepsis risk score without incorporating clinical calculators for assessing organ dysfunctions, making the models less convincing and transparent to clinicians. To bridge the gap, we propose to mimic clinicians' workflow with a novel framework SepsisCalc to integrate clinical calculators into the predictive model, yielding a clinically transparent and precise model for utilization in clinical settings. Practically, clinical calculators usually combine information from multiple component variables in Electronic Health Records (EHR), and might not be applicable when the variables are (partially) missing. We mitigate this issue by representing EHRs as temporal graphs and integrating a learning module to dynamically add the accurately estimated calculator to the graphs. Experimental results on real-world datasets show that the proposed model outperforms state-of-the-art methods on sepsis prediction tasks. Moreover, we developed a system to identify organ dysfunctions and potential sepsis risks, providing a human-AI interaction tool for deployment, which can help clinicians understand the prediction outputs and prepare timely interventions for the corresponding dysfunctions, paving the way for actionable clinical decision-making support for early intervention.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2025 v1","pages":"2779-2790"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11998859/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058805","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

SatHealth: A Multimodal Public Health Dataset with Satellite-based Environmental Factors. 健康：基于卫星环境因子的多模式公共卫生数据集。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2025-08-01 Epub Date: 2025-08-03 DOI: 10.1145/3711896.3737440

Yuanlong Wang, Pengqi Wang, Changchang Yin, Ping Zhang

{"title":"SatHealth: A Multimodal Public Health Dataset with Satellite-based Environmental Factors.","authors":"Yuanlong Wang, Pengqi Wang, Changchang Yin, Ping Zhang","doi":"10.1145/3711896.3737440","DOIUrl":"10.1145/3711896.3737440","url":null,"abstract":"Living environments play a vital role in the prevalence and progression of diseases, and understanding their impact on patient's health status becomes increasingly crucial for developing AI models. However, due to the lack of long-term and fine-grained spatial and temporal data in public and population health studies, most existing studies fail to incorporate environmental data, limiting the models' performance and real-world application. To address this shortage, we developed SatHealth, a novel dataset combining multimodal spatiotemporal data, including environmental data, satellite images, all-disease prevalences estimated from medical claims, and social determinants of health (SDoH) indicators. We conducted experiments under two use cases with SatHealth: regional public health modeling and personal disease risk prediction. Experimental results show that living environmental information can significantly improve AI models' performance and temporal-spatial generalizability on various tasks. Finally, we deploy a web-based application to provide an exploration tool for SatHealth and one-click access to both our data and regional environmental embedding to facilitate plug-and-play utilization. SatHealth is now published with data in Ohio, and we will keep updating SatHealth to cover the other parts of the US. With the web application and published code pipeline, our work provides valuable angles and resources to include environmental data in healthcare research and establishes a foundational framework for future research in environmental health informatics.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2025 ","pages":"5819-5830"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12340727/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144839290","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

Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks. 图ode及其以后：用图神经网络积分微分方程的综合研究。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2025-08-01 Epub Date: 2025-08-03 DOI: 10.1145/3711896.3736559

Zewen Liu, Xiaoda Wang, Bohan Wang, Zijie Huang, Carl Yang, Wei Jin

{"title":"Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks.","authors":"Zewen Liu, Xiaoda Wang, Bohan Wang, Zijie Huang, Carl Yang, Wei Jin","doi":"10.1145/3711896.3736559","DOIUrl":"https://doi.org/10.1145/3711896.3736559","url":null,"abstract":"Graph Neural Networks (GNNs) and differential equations (DEs) are two rapidly advancing areas of research that have shown remarkable synergy in recent years. GNNs have emerged as powerful tools for learning on graph-structured data, while differential equations provide a principled framework for modeling continuous dynamics across time and space. The intersection of these fields has led to innovative approaches that leverage the strengths of both, enabling applications in physics-informed learning, spatiotemporal modeling, and scientific computing. This survey aims to provide a comprehensive overview of the burgeoning research at the intersection of GNNs and DEs. We will categorize existing methods, discuss their underlying principles, and highlight their applications across domains such as molecular modeling, traffic prediction, and epidemic spreading. Furthermore, we identify open challenges and outline future research directions to advance this interdisciplinary field. A comprehensive paper list is provided at https://github.com/Emory-Melody/Awesome-Graph-NDEs.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2025 ","pages":"6118-6128"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12363673/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981309","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

Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models. 通过预测扩散模型综合多模式电子健康记录。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2024-08-01 Epub Date: 2024-08-24 DOI: 10.1145/3637528.3671836

Yuan Zhong, Xiaochen Wang, Jiaqi Wang, Xiaokun Zhang, Yaqing Wang, Mengdi Huai, Cao Xiao, Fenglong Ma

{"title":"Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models.","authors":"Yuan Zhong, Xiaochen Wang, Jiaqi Wang, Xiaokun Zhang, Yaqing Wang, Mengdi Huai, Cao Xiao, Fenglong Ma","doi":"10.1145/3637528.3671836","DOIUrl":"https://doi.org/10.1145/3637528.3671836","url":null,"abstract":"Synthesizing electronic health records (EHR) data has become a preferred strategy to address data scarcity, improve data quality, and model fairness in healthcare. However, existing approaches for EHR data generation predominantly rely on state-of-the-art generative techniques like generative adversarial networks, variational autoencoders, and language models. These methods typically replicate input visits, resulting in inadequate modeling of temporal dependencies between visits and overlooking the generation of time information, a crucial element in EHR data. Moreover, their ability to learn visit representations is limited due to simple linear mapping functions, thus compromising generation quality. To address these limitations, we propose a novel EHR data generation model called EHRPD. It is a diffusion-based model designed to predict the next visit based on the current one while also incorporating time interval estimation. To enhance generation quality and diversity, we introduce a novel time-aware visit embedding module and a pioneering predictive denoising diffusion probabilistic model (P-DDPM). Additionally, we devise a predictive U-Net (PU-Net) to optimize P-DDPM. We conduct experiments on two public datasets and evaluate EHRPD from fidelity, privacy, and utility perspectives. The experimental results demonstrate the efficacy and utility of the proposed EHRPD in addressing the aforementioned limitations and advancing EHR data generation.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2024 ","pages":"4607-4618"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12009115/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144030156","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

SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing. 败血症实验室：利用不确定性量化和主动传感技术进行早期败血症预测。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2024-08-01 Epub Date: 2024-08-24 DOI: 10.1145/3637528.3671586

Changchang Yin, Pin-Yu Chen, Bingsheng Yao, Dakuo Wang, Jeffrey Caterino, Ping Zhang

{"title":"SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing.","authors":"Changchang Yin, Pin-Yu Chen, Bingsheng Yao, Dakuo Wang, Jeffrey Caterino, Ping Zhang","doi":"10.1145/3637528.3671586","DOIUrl":"https://doi.org/10.1145/3637528.3671586","url":null,"abstract":"Sepsis is the leading cause of in-hospital mortality in the USA. Early sepsis onset prediction and diagnosis could significantly improve the survival of sepsis patients. Existing predictive models are usually trained on high-quality data with few missing information, while missing values widely exist in real-world clinical scenarios (especially in the first hours of admissions to the hospital), which causes a significant decrease in accuracy and an increase in uncertainty for the predictive models. The common method to handle missing values is imputation, which replaces the unavailable variables with estimates from the observed data. The uncertainty of imputation results can be propagated to the sepsis prediction outputs, which have not been studied in existing works on either sepsis prediction or uncertainty quantification. In this study, we first define such propagated uncertainty as the variance of prediction output and then introduce uncertainty propagation methods to quantify the propagated uncertainty. Moreover, for the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm to increase confidence by actively recommending clinicians to observe the most informative variables. We validate the proposed models in both publicly available data (i.e., MIMIC-III and AmsterdamUMCdb) and proprietary data in The Ohio State University Wexner Medical Center (OSUWMC). The experimental results show that the propagated uncertainty is dominant at the beginning of admissions to hospitals and the proposed algorithm outperforms state-of-the-art active sensing methods. Finally, we implement a SepsisLab system for early sepsis prediction and active sensing based on our pre-trained models. Clinicians and potential sepsis patients can benefit from the system in early prediction and diagnosis of sepsis.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2024 ","pages":"6158-6168"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11470769/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482497","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

TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data. TACCO：基于EHR数据的疾病亚型临床概念和患者就诊的任务导向共聚类。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2024-08-01 Epub Date: 2024-08-24 DOI: 10.1145/3637528.3671594

Ziyang Zhang, Hejie Cui, Ran Xu, Yuzhang Xie, Joyce C Ho, Carl Yang

{"title":"TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data.","authors":"Ziyang Zhang, Hejie Cui, Ran Xu, Yuzhang Xie, Joyce C Ho, Carl Yang","doi":"10.1145/3637528.3671594","DOIUrl":"10.1145/3637528.3671594","url":null,"abstract":"The growing availability of well-organized Electronic Health Records (EHR) data has enabled the development of various machine learning models towards disease risk prediction. However, existing risk prediction methods overlook the heterogeneity of complex diseases, failing to model the potential disease subtypes regarding their corresponding patient visits and clinical concept subgroups. In this work, we introduce TACCO, a novel framework that jointly discovers clusters of clinical concepts and patient visits based on a hypergraph modeling of EHR data. Specifically, we develop a novel self-supervised co-clustering framework that can be guided by the risk prediction task of specific diseases. Furthermore, we enhance the hypergraph model of EHR data with textual embeddings and enforce the alignment between the clusters of clinical concepts and patient visits through a contrastive objective. Comprehensive experiments conducted on the public MIMIC-III dataset and Emory internal CRADLE dataset over the downstream clinical tasks of phenotype classification and cardiovascular risk prediction demonstrate an average 31.25% performance improvement compared to traditional ML baselines and a 5.26% improvement on top of the vanilla hypergraph model without our co-clustering mechanism. In-depth model analysis, clustering results analysis, and clinical case studies further validate the improved utilities and insightful interpretations delivered by TACCO. Code is available at https://github.com/PericlesHat/TACCO.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2024 ","pages":"6324-6334"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11868038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544972","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

Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization. 分布式协调：联邦集群批量效应调整和泛化。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2024-01-01 Epub Date: 2024-08-24 DOI: 10.1145/3637528.3671590

Bao Hoang, Yijiang Pang, Siqi Liang, Liang Zhan, Paul M Thompson, Jiayu Zhou

{"title":"Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization.","authors":"Bao Hoang, Yijiang Pang, Siqi Liang, Liang Zhan, Paul M Thompson, Jiayu Zhou","doi":"10.1145/3637528.3671590","DOIUrl":"10.1145/3637528.3671590","url":null,"abstract":"Independent and identically distributed (i.i.d.) data is essential to many data analysis and modeling techniques. In the medical domain, collecting data from multiple sites or institutions is a common strategy that guarantees sufficient clinical diversity, determined by the decentralized nature of medical data. However, data from various sites are easily biased by the local environment or facilities, thereby violating the i.i.d. rule. A common strategy is to harmonize the site bias while retaining important biological information. The COMBAT is among the most popular harmonization approaches and has recently been extended to handle distributed sites. However, when faced with situations involving newly joined sites in training or evaluating data from unknown/unseen sites, COMBAT lacks compatibility and requires retraining with data from all the sites. The retraining leads to significant computational and logistic overhead that is usually prohibitive. In this work, we develop a novel Cluster ComBat harmonization algorithm, which leverages cluster patterns of the data in different sites and greatly advances the usability of COMBAT harmonization. We use extensive simulation and real medical imaging data from ADNI to demonstrate the superiority of the proposed approach. Our codes are provided in https://github.com/illidanlab/distributed-cluster-harmonization.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2024 ","pages":"5105-5115"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11529347/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142570543","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

MolSearch: Search-based Multi-objective Molecular Generation and Property Optimization. MolSearch:基于搜索的多目标分子生成和性能优化。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2022-08-01 DOI: 10.1145/3534678.3542676

Mengying Sun, Huijun Wang, Jing Xing, Bin Chen, Han Meng, Jiayu Zhou

{"title":"MolSearch: Search-based Multi-objective Molecular Generation and Property Optimization.","authors":"Mengying Sun, Huijun Wang, Jing Xing, Bin Chen, Han Meng, Jiayu Zhou","doi":"10.1145/3534678.3542676","DOIUrl":"https://doi.org/10.1145/3534678.3542676","url":null,"abstract":"Leveraging computational methods to generate small molecules with desired properties has been an active research area in the drug discovery field. Towards real-world applications, however, efficient generation of molecules that satisfy multiple property requirements simultaneously remains a key challenge. In this paper, we tackle this challenge using a search-based approach and propose a simple yet effective framework called MolSearch for multi-objective molecular generation (optimization). We show that given proper design and sufficient information, search-based methods can achieve performance comparable or even better than deep learning methods while being computationally efficient. Such efficiency enables massive exploration of chemical space given constrained computational resources. In particular, MolSearch starts with existing molecules and uses a two-stage search strategy to gradually modify them into new ones, based on transformation rules derived systematically and exhaustively from large compound libraries. We evaluate MolSearch in multiple benchmark generation settings and demonstrate its effectiveness and efficiency.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":"2022 ","pages":"4724-4732"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10097503/pdf/nihms-1888099.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9580527","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: 6

Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes. 基于动态治疗机制的政策适应解构行为者-批评者网络。

KDD : proceedings. International Conference on Knowledge Discovery & Data Mining Pub Date : 2022-08-01 Epub Date: 2022-08-13 DOI: 10.1145/3534678.3539413

Changchang Yin, Ruoqi Liu, Jeffrey Caterino, Ping Zhang

{"title":"Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes.","authors":"Changchang Yin, Ruoqi Liu, Jeffrey Caterino, Ping Zhang","doi":"10.1145/3534678.3539413","DOIUrl":"https://doi.org/10.1145/3534678.3539413","url":null,"abstract":"Despite intense efforts in basic and clinical research, an individualized ventilation strategy for critically ill patients remains a major challenge. Recently, dynamic treatment regime (DTR) with reinforcement learning (RL) on electronic health records (EHR) has attracted interest from both the healthcare industry and machine learning research community. However, most learned DTR policies might be biased due to the existence of confounders. Although some treatment actions non-survivors received may be helpful, if confounders cause the mortality, the training of RL models guided by long-term outcomes (e.g., 90-day mortality) would punish those treatment actions causing the learned DTR policies to be suboptimal. In this study, we develop a new deconfounding actor-critic network (DAC) to learn optimal DTR policies for patients. To alleviate confounding issues, we incorporate a patient resampling module and a confounding balance module into our actor-critic framework. To avoid punishing the effective treatment actions non-survivors received, we design a short-term reward to capture patients' immediate health state changes. Combining short-term with long-term rewards could further improve the model performance. Moreover, we introduce a policy adaptation method to successfully transfer the learned model to new-source small-scale datasets. The experimental results on one semi-synthetic and two different real-world datasets show the proposed model outperforms the state-of-the-art models. The proposed model provides individualized treatment decisions for mechanical ventilation that could improve patient outcomes.","PeriodicalId":74037,"journal":{"name":"KDD : proceedings. International Conference on Knowledge Discovery & Data Mining","volume":" ","pages":"2316-2326"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9466407/pdf/nihms-1830314.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40354004","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: 1