Proceedings of machine learning research最新文献

Uncertainty Quantification for Conditional Treatment Effect Estimation under Dynamic Treatment Regimes. 动态处理条件下条件处理效果评估的不确定性量化。

Proceedings of machine learning research Pub Date : 2024-12-01

Leon Deng, Hong Xiong, Feng Wu, Sanyam Kapoor, Soumya Ghosh, Zach Shahn, Li-Wei H Lehman

{"title":"Uncertainty Quantification for Conditional Treatment Effect Estimation under Dynamic Treatment Regimes.","authors":"Leon Deng, Hong Xiong, Feng Wu, Sanyam Kapoor, Soumya Ghosh, Zach Shahn, Li-Wei H Lehman","doi":"","DOIUrl":"","url":null,"abstract":"In medical decision-making, clinicians must choose between different time-varying treatment strategies. Counterfactual prediction via g-computation enables comparison of alternative outcome distributions under such treatment strategies. While deep learning can better model high-dimensional data with complex temporal dependencies, incorporating model uncertainty into predicted conditional counterfactual distributions remains challenging. We propose a principled approach to model uncertainty in deep learning implementations of g-computations using approximate Bayesian posterior predictive distributions of counterfactual outcomes via variational dropout and deep ensembles. We evaluate these methods by comparing their counterfactual predictive calibration and performance in decision-making tasks, using two simulated datasets from mechanistic models and a real-world sepsis dataset. Our findings suggest that the proposed uncertainty quantification approach improves both calibration and decision-making performance, particularly in minimizing risks of worst-case adverse clinical outcomes under alternative dynamic treatment regimes. To our knowledge, this is the first work to propose and compare multiple uncertainty quantification methods in machine learning models of g-computation in estimating conditional treatment effects under dynamic treatment regimes.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"259 ","pages":"248-266"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12121963/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144182919","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

An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation. 儿童肥胖风险评估的可互操作机器学习管道。

Proceedings of machine learning research Pub Date : 2024-12-01

Hamed Fayyaz, Mehak Gupta, Alejandra Perez Ramirez, Claudine Jurkovitz, H Timothy Bunnell, Thao-Ly T Phan, Rahmatollah Beheshti

{"title":"An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation.","authors":"Hamed Fayyaz, Mehak Gupta, Alejandra Perez Ramirez, Claudine Jurkovitz, H Timothy Bunnell, Thao-Ly T Phan, Rahmatollah Beheshti","doi":"","DOIUrl":"","url":null,"abstract":"Reliable prediction of pediatric obesity can offer a valuable resource to providers, helping them engage in timely preventive interventions before the disease is established. Many efforts have been made to develop ML-based predictive models of obesity, and some studies have reported high predictive performances. However, no commonly used clinical decision support tool based on existing ML models currently exists. This study presents a novel end-to-end pipeline specifically designed for pediatric obesity prediction, which supports the entire process of data extraction, inference, and communication via an API or a user interface. While focusing only on routinely recorded data in pediatric electronic health records (EHRs), our pipeline uses a diverse expert-curated list of medical concepts to predict the 1-3 years risk of developing obesity. Furthermore, by using the Fast Healthcare Interoperability Resources (FHIR) standard in our design procedure, we specifically target facilitating low-effort integration of our pipeline with different EHR systems. In our experiments, we report the effectiveness of the predictive model as well as its alignment with the feedback from various stakeholders, including ML scientists, providers, health IT personnel, health administration representatives, and patient group representatives.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"259 ","pages":"308-324"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11884402/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143574461","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

Multimodal Sleep Apnea Detection with Missing or Noisy Modalities. 多模态睡眠呼吸暂停检测缺失或噪声模态。

Proceedings of machine learning research Pub Date : 2024-08-01

Hamed Fayyaz, Niharika S D'Souza, Rahmatollah Beheshti

{"title":"Multimodal Sleep Apnea Detection with Missing or Noisy Modalities.","authors":"Hamed Fayyaz, Niharika S D'Souza, Rahmatollah Beheshti","doi":"","DOIUrl":"","url":null,"abstract":"Polysomnography (PSG) is a type of sleep study that records multimodal physiological signals and is widely used for purposes such as sleep staging and respiratory event detection. Conventional machine learning methods assume that each sleep study is associated with a fixed set of observed modalities and that all modalities are available for each sample. However, noisy and missing modalities are a common issue in real-world clinical settings. In this study, we propose a comprehensive pipeline aiming to compensate for the missing or noisy modalities when performing sleep apnea detection. Unlike other existing studies, our proposed model works with any combination of available modalities. Our experiments show that the proposed model outperforms other state-of-the-art approaches in sleep apnea detection using various subsets of available data and different levels of noise, and maintains its high performance (AUROC>0.9) even in the presence of high levels of noise or missingness. This is especially relevant in settings where the level of noise and missingness is high (such as pediatric or outside-of-clinic scenarios). Our code is publicly available at https://github.com/healthylaife/apnea-missing-modality.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"252 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11893010/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598009","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

G-Transformer: Counterfactual Outcome Prediction under Dynamic and Time-varying Treatment Regimes. G-Transformer：动态和时变治疗机制下的反事实结果预测。

Proceedings of machine learning research Pub Date : 2024-08-01

Hong Xiong, Feng Wu, Leon Deng, Megan Su, Zach Shahn, Li-Wei H Lehman

{"title":"G-Transformer: Counterfactual Outcome Prediction under Dynamic and Time-varying Treatment Regimes.","authors":"Hong Xiong, Feng Wu, Leon Deng, Megan Su, Zach Shahn, Li-Wei H Lehman","doi":"","DOIUrl":"","url":null,"abstract":"In the context of medical decision making, counterfactual prediction enables clinicians to predict treatment outcomes of interest under alternative courses of therapeutic actions given observed patient history. In this work, we present G-Transformer for counterfactual outcome prediction under dynamic and time-varying treatment strategies. Our approach leverages a Transformer architecture to capture complex, long-range dependencies in time-varying covariates while enabling g-computation, a causal inference method for estimating the effects of dynamic treatment regimes. Specifically, we use a Transformer-based encoder architecture to estimate the conditional distribution of relevant covariates given covariate and treatment history at each time point, then produces Monte Carlo estimates of counterfactual outcomes by simulating forward patient trajectories under treatment strategies of interest. We evaluate G-Transformer extensively using two simulated longitudinal datasets from mechanistic models, and a real-world sepsis ICU dataset from MIMIC-IV. G-Transformer outperforms both classical and state-of-the-art counterfactual prediction models in these settings. To the best of our knowledge, this is the first Transformer-based architecture that supports g-computation for counterfactual outcome prediction under dynamic and time-varying treatment strategies.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"252 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12113242/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144164074","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

Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks. 用双层 ReLU 神经网络进行可证明的多任务表征学习

Proceedings of machine learning research Pub Date : 2024-07-01

Liam Collins, Hamed Hassani, Mahdi Soltanolkotabi, Aryan Mokhtari, Sanjay Shakkottai

{"title":"Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks.","authors":"Liam Collins, Hamed Hassani, Mahdi Soltanolkotabi, Aryan Mokhtari, Sanjay Shakkottai","doi":"","DOIUrl":"","url":null,"abstract":"An increasingly popular machine learning paradigm is to pretrain a neural network (NN) on many tasks offline, then adapt it to downstream tasks, often by re-training only the last linear layer of the network. This approach yields strong downstream performance in a variety of contexts, demonstrating that multitask pretraining leads to effective feature learning. Although several recent theoretical studies have shown that shallow NNs learn meaningful features when either (i) they are trained on a single task or (ii) they are linear, very little is known about the closer-to-practice case of nonlinear NNs trained on multiple tasks. In this work, we present the first results proving that feature learning occurs during training with a nonlinear model on multiple tasks. Our key insight is that multi-task pretraining induces a pseudo-contrastive loss that favors representations that align points that typically have the same label across tasks. Using this observation, we show that when the tasks are binary classification tasks with labels depending on the projection of the data onto an <math><mi>r</mi></math> -dimensional subspace within the <math><mi>d</mi> <mo>≫</mo> <mi>r</mi></math> -dimensional input space, a simple gradient-based multitask learning algorithm on a two-layer ReLU NN recovers this projection, allowing for generalization to downstream tasks with sample and neuron complexity independent of <math><mi>d</mi></math> . In contrast, we show that with high probability over the draw of a single task, training on this single task cannot guarantee to learn all <math><mi>r</mi></math> ground-truth features.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"235 ","pages":"9292-9345"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11486479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482676","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

Multi-Region Markovian Gaussian Process: An Efficient Method to Discover Directional Communications Across Multiple Brain Regions. 多区域马尔可夫高斯过程：发现跨多个脑区定向通信的高效方法

Proceedings of machine learning research Pub Date : 2024-07-01

Weihan Li, Chengrui Li, Yule Wang, Anqi Wu

{"title":"Multi-Region Markovian Gaussian Process: An Efficient Method to Discover Directional Communications Across Multiple Brain Regions.","authors":"Weihan Li, Chengrui Li, Yule Wang, Anqi Wu","doi":"","DOIUrl":"","url":null,"abstract":"Studying the complex interactions between different brain regions is crucial in neuroscience. Various statistical methods have explored the latent communication across multiple brain regions. Two main categories are the Gaussian Process (GP) and Linear Dynamical System (LDS), each with unique strengths. The GP-based approach effectively discovers latent variables with frequency bands and communication directions. Conversely, the LDS-based approach is computationally efficient but lacks powerful expressiveness in latent representation. In this study, we merge both methodologies by creating an LDS mirroring a multi-output GP, termed Multi-Region Markovian Gaussian Process (MRM-GP). Our work establishes a connection between an LDS and a multi-output GP that explicitly models frequencies and phase delays within the latent space of neural recordings. Consequently, the model achieves a linear inference cost over time points and provides an interpretable low-dimensional representation, revealing communication directions across brain regions and separating oscillatory communications into different frequency bands.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"235 ","pages":"28112-28131"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11526605/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142559682","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

Multi-Source Conformal Inference Under Distribution Shift. 分布偏移下的多源共形推理

Proceedings of machine learning research Pub Date : 2024-07-01

Yi Liu, Alexander W Levis, Sharon-Lise Normand, Larry Han

{"title":"Multi-Source Conformal Inference Under Distribution Shift.","authors":"Yi Liu, Alexander W Levis, Sharon-Lise Normand, Larry Han","doi":"","DOIUrl":"","url":null,"abstract":"Recent years have experienced increasing utilization of complex machine learning models across multiple sources of data to inform more generalizable decision-making. However, distribution shifts across data sources and privacy concerns related to sharing individual-level data, coupled with a lack of uncertainty quantification from machine learning predictions, make it challenging to achieve valid inferences in multi-source environments. In this paper, we consider the problem of obtaining distribution-free prediction intervals for a target population, leveraging multiple potentially biased data sources. We derive the efficient influence functions for the quantiles of unobserved outcomes in the target and source populations, and show that one can incorporate machine learning prediction algorithms in the estimation of nuisance functions while still achieving parametric rates of convergence to nominal coverage probabilities. Moreover, when conditional outcome invariance is violated, we propose a data-adaptive strategy to upweight informative data sources for efficiency gain and downweight non-informative data sources for bias reduction. We highlight the robustness and efficiency of our proposals for a variety of conformal scores and data-generating mechanisms via extensive synthetic experiments. Hospital length of stay prediction intervals for pediatric patients undergoing a high-risk cardiac surgical procedure between 2016-2022 in the U.S. illustrate the utility of our methodology.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"235 ","pages":"31344-31382"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11345809/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142082878","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

Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learning. 基于不确定性的非对称互惠学习桥接联邦学习模型异质性。

Proceedings of machine learning research Pub Date : 2024-07-01

Jiaqi Wang, Chenxu Zhao, Lingjuan Lyu, Quanzeng You, Mengdi Huai, Fenglong Ma

引用次数: 0

Adapt and Diffuse: Sample-Adaptive Reconstruction Via Latent Diffusion Models. 适应与扩散：通过潜在扩散模型进行样本适应性重建。

Proceedings of machine learning research Pub Date : 2024-07-01

Zalan Fabian, Berk Tinaz, Mahdi Soltanolkotabi

{"title":"Adapt and Diffuse: Sample-Adaptive Reconstruction Via Latent Diffusion Models.","authors":"Zalan Fabian, Berk Tinaz, Mahdi Soltanolkotabi","doi":"","DOIUrl":"","url":null,"abstract":"Inverse problems arise in a multitude of applications, where the goal is to recover a clean signal from noisy and possibly (non)linear observations. The difficulty of a reconstruction problem depends on multiple factors, such as the structure of the ground truth signal, the severity of the degradation and the complex interactions between the above. This results in natural sample-by-sample variation in the difficulty of a reconstruction task, which is often overlooked by contemporary techniques. Our key observation is that most existing inverse problem solvers lack the ability to adapt their compute power to the difficulty of the reconstruction task, resulting in subpar performance and wasteful resource allocation. We propose a novel method that we call severity encoding, to estimate the degradation severity of noisy, degraded signals in the latent space of an autoencoder. We show that the estimated severity has strong correlation with the true corruption level and can give useful hints at the difficulty of reconstruction problems on a sample-by-sample basis. Furthermore, we propose a reconstruction method based on latent diffusion models that leverages the predicted degradation severities to fine-tune the reverse diffusion sampling trajectory and thus achieve sample-adaptive inference times. Our framework acts as a wrapper that can be combined with any latent diffusion-based baseline solver, imbuing it with sample-adaptivity and acceleration. We perform numerical experiments on both linear and nonlinear inverse problems and demonstrate that our technique greatly improves the performance of the baseline solver and achieves up to 10× acceleration in mean sampling speed.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"235 ","pages":"12723-12753"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11421836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142334004","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

Contrastive Learning for Clinical Outcome Prediction with Partial Data Sources. 利用部分数据源进行临床结果预测的对比学习

Proceedings of machine learning research Pub Date : 2024-07-01

Meng Xia, Jonathan Wilson, Benjamin Goldstein, Ricardo Henao

{"title":"Contrastive Learning for Clinical Outcome Prediction with Partial Data Sources.","authors":"Meng Xia, Jonathan Wilson, Benjamin Goldstein, Ricardo Henao","doi":"","DOIUrl":"","url":null,"abstract":"The use of machine learning models to predict clinical outcomes from (longitudinal) electronic health record (EHR) data is becoming increasingly popular due to advances in deep architectures, representation learning, and the growing availability of large EHR datasets. Existing models generally assume access to the same data sources during both training and inference stages. However, this assumption is often challenged by the fact that real-world clinical datasets originate from various data sources (with distinct sets of covariates), which though can be available for training (in a research or retrospective setting), are more realistically only partially available (a subset of such sets) for inference when deployed. So motivated, we introduce Contrastive Learning for clinical Outcome Prediction with Partial data Sources (CLOPPS), that trains encoders to capture information across different data sources and then leverages them to build classifiers restricting access to a single data source. This approach can be used with existing cross-sectional or longitudinal outcome classification models. We present experiments on two real-world datasets demonstrating that CLOPPS consistently outperforms strong baselines in several practical scenarios.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"235 ","pages":"54156-54177"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11326519/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989752","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