Proceedings of machine learning research最新文献_第8页

Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression. 为患者风险进展建模的时间监督对比学习。

Proceedings of machine learning research Pub Date : 2023-01-01

Shahriar Noroozizadeh, Jeremy C Weiss, George H Chen

{"title":"Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression.","authors":"Shahriar Noroozizadeh, Jeremy C Weiss, George H Chen","doi":"","DOIUrl":"","url":null,"abstract":"We consider the problem of predicting how the likelihood of an outcome of interest for a patient changes over time as we observe more of the patient's data. To solve this problem, we propose a supervised contrastive learning framework that learns an embedding representation for each time step of a patient time series. Our framework learns the embedding space to have the following properties: (1) nearby points in the embedding space have similar predicted class probabilities, (2) adjacent time steps of the same time series map to nearby points in the embedding space, and (3) time steps with very different raw feature vectors map to far apart regions of the embedding space. To achieve property (3), we employ a nearest neighbor pairing mechanism in the raw feature space. This mechanism also serves as an alternative to \"data augmentation\", a key ingredient of contrastive learning, which lacks a standard procedure that is adequately realistic for clinical tabular data, to our knowledge. We demonstrate that our approach outperforms state-of-the-art baselines in predicting mortality of septic patients (MIMIC-III dataset) and tracking progression of cognitive impairment (ADNI dataset). Our method also consistently recovers the correct synthetic dataset embedding structure across experiments, a feat not achieved by baselines. Our ablation experiments show the pivotal role of our nearest neighbor pairing.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"225 ","pages":"403-427"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10976929/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140320101","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

Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data. 高维不完整数据的神经网络高斯过程多重插值。

Proceedings of machine learning research Pub Date : 2022-12-01

Zongyu Dai, Zhiqi Bu, Qi Long

{"title":"Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data.","authors":"Zongyu Dai, Zhiqi Bu, Qi Long","doi":"","DOIUrl":"","url":null,"abstract":"Missing data are ubiquitous in real world applications and, if not adequately handled, may lead to the loss of information and biased findings in downstream analysis. Particularly, high-dimensional incomplete data with a moderate sample size, such as analysis of multi-omics data, present daunting challenges. Imputation is arguably the most popular method for handling missing data, though existing imputation methods have a number of limitations. Single imputation methods such as matrix completion methods do not adequately account for imputation uncertainty and hence would yield improper statistical inference. In contrast, multiple imputation (MI) methods allow for proper inference but existing methods do not perform well in high-dimensional settings. Our work aims to address these significant methodological gaps, leveraging recent advances in neural network Gaussian process (NNGP) from a Bayesian viewpoint. We propose two NNGP-based MI methods, namely MI-NNGP, that can apply multiple imputations for missing values from a joint (posterior predictive) distribution. The MI-NNGP methods are shown to significantly outperform existing state-of-the-art methods on synthetic and real datasets, in terms of imputation error, statistical inference, robustness to missing rates, and computation costs, under three missing data mechanisms, MCAR, MAR, and MNAR. Code is available in the GitHub repository https://github.com/bestadcarry/MI-NNGP.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"189 ","pages":"265-279"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10348708/pdf/nihms-1861886.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9821494","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

Meta-analysis of individualized treatment rules via sign-coherency 基于符号一致性的个体化治疗规则元分析

Proceedings of machine learning research Pub Date : 2022-11-28 DOI: 10.48550/arXiv.2211.15476

Jay Jojo Cheng, J. Huling, Guanhua Chen

{"title":"Meta-analysis of individualized treatment rules via sign-coherency","authors":"Jay Jojo Cheng, J. Huling, Guanhua Chen","doi":"10.48550/arXiv.2211.15476","DOIUrl":"https://doi.org/10.48550/arXiv.2211.15476","url":null,"abstract":"Medical treatments tailored to a patient's baseline characteristics hold the potential of improving patient outcomes while reducing negative side effects. Learning individualized treatment rules (ITRs) often requires aggregation of multiple datasets(sites); however, current ITR methodology does not take between-site heterogeneity into account, which can hurt model generalizability when deploying back to each site. To address this problem, we develop a method for individual-level meta-analysis of ITRs, which jointly learns site-specific ITRs while borrowing information about feature sign-coherency via a scientifically-motivated directionality principle. We also develop an adaptive procedure for model tuning, using information criteria tailored to the ITR learning problem. We study the proposed methods through numerical experiments to understand their performance under different levels of between-site heterogeneity and apply the methodology to estimate ITRs in a large multi-center database of electronic health records. This work extends several popular methodologies for estimating ITRs (A-learning, weighted learning) to the multiple-sites setting.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"193 1","pages":"171-198"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47047493","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

Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data 高维不完全数据的神经网络高斯过程多重脉冲

Proceedings of machine learning research Pub Date : 2022-11-23 DOI: 10.48550/arXiv.2211.13297

Zongyu Dai, Zhiqi Bu, Q. Long

{"title":"Multiple Imputation with Neural Network Gaussian Process for High-dimensional Incomplete Data","authors":"Zongyu Dai, Zhiqi Bu, Q. Long","doi":"10.48550/arXiv.2211.13297","DOIUrl":"https://doi.org/10.48550/arXiv.2211.13297","url":null,"abstract":"Missing data are ubiquitous in real world applications and, if not adequately handled, may lead to the loss of information and biased findings in downstream analysis. Particularly, high-dimensional incomplete data with a moderate sample size, such as analysis of multi-omics data, present daunting challenges. Imputation is arguably the most popular method for handling missing data, though existing imputation methods have a number of limitations. Single imputation methods such as matrix completion methods do not adequately account for imputation uncertainty and hence would yield improper statistical inference. In contrast, multiple imputation (MI) methods allow for proper inference but existing methods do not perform well in high-dimensional settings. Our work aims to address these significant methodological gaps, leveraging recent advances in neural network Gaussian process (NNGP) from a Bayesian viewpoint. We propose two NNGP-based MI methods, namely MI-NNGP, that can apply multiple imputations for missing values from a joint (posterior predictive) distribution. The MI-NNGP methods are shown to significantly outperform existing state-of-the-art methods on synthetic and real datasets, in terms of imputation error, statistical inference, robustness to missing rates, and computation costs, under three missing data mechanisms, MCAR, MAR, and MNAR. Code is available in the GitHub repository https://github.com/bestadcarry/MI-NNGP.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"189 1","pages":"265-279"},"PeriodicalIF":0.0,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43147782","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: 2

Predicting Attrition Patterns from Pediatric Weight Management Programs. 预测儿科体重管理计划的减员模式

Proceedings of machine learning research Pub Date : 2022-11-01

Hamed Fayyaz, Thao-Ly T Phan, H Timothy Bunnell, Rahmatollah Beheshti

{"title":"Predicting Attrition Patterns from Pediatric Weight Management Programs.","authors":"Hamed Fayyaz, Thao-Ly T Phan, H Timothy Bunnell, Rahmatollah Beheshti","doi":"","DOIUrl":"","url":null,"abstract":"Obesity is a major public health concern. Multidisciplinary pediatric weight management programs are considered standard treatment for children with obesity who are not able to be successfully managed in the primary care setting. Despite their great potential, high dropout rates (referred to as attrition) are a major hurdle in delivering successful interventions. Predicting attrition patterns can help providers reduce the alarmingly high rates of attrition (up to 80%) by engaging in earlier and more personalized interventions. Previous work has mainly focused on finding static predictors of attrition on smaller datasets and has achieved limited success in effective prediction. In this study, we have collected a five-year comprehensive dataset of 4,550 children from diverse backgrounds receiving treatment at four pediatric weight management programs in the US. We then developed a machine learning pipeline to predict (a) the likelihood of attrition, and (b) the change in body-mass index (BMI) percentile of children, at different time points after joining the weight management program. Our pipeline is greatly customized for this problem using advanced machine learning techniques to process longitudinal data, smaller-size data, and interrelated prediction tasks. The proposed method showed strong prediction performance as measured by AUROC scores (average AUROC of 0.77 for predicting attrition, and 0.78 for predicting weight outcomes).","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"193 ","pages":"326-342"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854275/pdf/nihms-1865420.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10604379","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

Enzyme Activity Prediction of Sequence Variants on Novel Substrates using Improved Substrate Encodings and Convolutional Pooling. 基于改进底物编码和卷积池的新底物序列变异酶活性预测。

Proceedings of machine learning research Pub Date : 2022-11-01

Zhiqing Xu, Jinghao Wu, Yun S Song, Radhakrishnan Mahadevan

{"title":"Enzyme Activity Prediction of Sequence Variants on Novel Substrates using Improved Substrate Encodings and Convolutional Pooling.","authors":"Zhiqing Xu, Jinghao Wu, Yun S Song, Radhakrishnan Mahadevan","doi":"","DOIUrl":"","url":null,"abstract":"Protein engineering is currently being revolutionized by deep learning applications, especially through natural language processing (NLP) techniques. It has been shown that state-of-the-art self-supervised language models trained on entire protein databases capture hidden contextual and structural information in amino acid sequences and are capable of improving sequence-to-function predictions. Yet, recent studies have reported that current compound-protein modeling approaches perform poorly on learning interactions between enzymes and substrates of interest within one protein family. We attribute this to low-grade substrate encoding methods and over-compressed sequence representations received by downstream predictive models. In this study, we propose a new substrate-encoding based on Extended Connectivity Fingerprints (ECFPs) and a convolutional-pooling of the sequence embeddings. Through testing on an activity profiling dataset of haloalkanoate dehalogenase superfamily that measures activities of 218 phosphatases against 168 substrates, we show substantial improvements in predictive performances of compound-protein interaction modeling. In addition, we also test the workflow on three other datasets from the halogenase, kinase and aminotransferase families and show that our pipeline achieves good performance on these datasets as well. We further demonstrate the utility of this downstream model architecture by showing that it achieves good performance with six different protein embeddings, including ESM-1b (Rives et al., 2021), TAPE (Rao et al., 2019), ProtBert, ProtAlbert, ProtT5, and ProtXLNet (Elnaggar et al., 2021). This study provides a new workflow for activity prediction on novel substrates that can be used to engineer new enzymes for sustainability applications.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"165 ","pages":"78-87"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9759087/pdf/nihms-1842132.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10762656","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

Selecting deep neural networks that yield consistent attribution-based interpretations for genomics. 为基因组学选择能产生一致归因解释的深度神经网络。

Proceedings of machine learning research Pub Date : 2022-11-01

Antonio Majdandzic, Chandana Rajesh, Amber Tang, Shushan Toneyan, Ethan Labelson, Rohit Tripathy, Peter K Koo

{"title":"Selecting deep neural networks that yield consistent attribution-based interpretations for genomics.","authors":"Antonio Majdandzic, Chandana Rajesh, Amber Tang, Shushan Toneyan, Ethan Labelson, Rohit Tripathy, Peter K Koo","doi":"","DOIUrl":"","url":null,"abstract":"Deep neural networks (DNNs) have advanced our ability to take DNA primary sequence as input and predict a myriad of molecular activities measured via high-throughput functional genomic assays. Post hoc attribution analysis has been employed to provide insights into the importance of features learned by DNNs, often revealing patterns such as sequence motifs. However, attribution maps typically harbor spurious importance scores to an extent that varies from model to model, even for DNNs whose predictions generalize well. Thus, the standard approach for model selection, which relies on performance of a held-out validation set, does not guarantee that a high-performing DNN will provide reliable explanations. Here we introduce two approaches that quantify the consistency of important features across a population of attribution maps; consistency reflects a qualitative property of human interpretable attribution maps. We employ the consistency metrics as part of a multivariate model selection framework to identify models that yield high generalization performance and interpretable attribution analysis. We demonstrate the efficacy of this approach across various DNNs quantitatively with synthetic data and qualitatively with chromatin accessibility data.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"200 ","pages":"131-149"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10194041/pdf/nihms-1895253.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9544629","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

A Path Towards Clinical Adaptation of Accelerated MRI. 加速MRI的临床适应之路。

Proceedings of machine learning research Pub Date : 2022-11-01

Michael S Yao, Michael S Hansen

{"title":"A Path Towards Clinical Adaptation of Accelerated MRI.","authors":"Michael S Yao, Michael S Hansen","doi":"","DOIUrl":"","url":null,"abstract":"Accelerated MRI reconstructs images of clinical anatomies from sparsely sampled signal data to reduce patient scan times. While recent works have leveraged deep learning to accomplish this task, such approaches have often only been explored in simulated environments where there is no signal corruption or resource limitations. In this work, we explore augmentations to neural network MRI image reconstructors to enhance their clinical relevancy. Namely, we propose a ConvNet model for detecting sources of image artifacts that achieves a classifier F 2 score of 79.1%. We also demonstrate that training reconstructors on MR signal data with variable acceleration factors can improve their average performance during a clinical patient scan by up to 2%. We offer a loss function to overcome catastrophic forgetting when models learn to reconstruct MR images of multiple anatomies and orientations. Finally, we propose a method for using simulated phantom data to pre-train reconstructors in situations with limited clinically acquired datasets and compute capabilities. Our results provide a potential path forward for clinical adaptation of accelerated MRI.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"193 ","pages":"489-511"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10061571/pdf/nihms-1846161.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9336136","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

Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR. 在超图上进行反事实和事实推理，在电子病历上进行可解释的临床预测。

Proceedings of machine learning research Pub Date : 2022-11-01

Ran Xu, Yue Yu, Chao Zhang, Mohammed K Ali, Joyce C Ho, Carl Yang

引用次数: 0

Automated intracranial vessel labeling with learning boosted by vessel connectivity, radii and spatial context. 自动颅内血管标记与学习促进血管连接，半径和空间背景。

Proceedings of machine learning research Pub Date : 2022-11-01

Jannik Sobisch, Žiga Bizjak, Aichi Chien, Žiga Špiclin

{"title":"Automated intracranial vessel labeling with learning boosted by vessel connectivity, radii and spatial context.","authors":"Jannik Sobisch, Žiga Bizjak, Aichi Chien, Žiga Špiclin","doi":"","DOIUrl":"","url":null,"abstract":"Cerebrovascular diseases are among the world's top causes of death and their screening and diagnosis rely on angiographic imaging. We focused on automated anatomical labeling of cerebral arteries that enables their cross-sectional quantification and inter-subject comparisons and thereby identification of geometric risk factors correlated to the cerebrovascular diseases. We used 152 cerebral TOF-MRA angiograms from three publicly available datasets and manually created reference labeling using Slicer3D. We extracted centerlines from nnU-net based segmentations using VesselVio and labeled them according to the reference labeling. Vessel centerline coordinates, in combination with additional vessel connectivity, radius and spatial context features were used for training seven distinct PointNet++ models. Model trained solely on the vessel centerline coordinates resulted in ACC of 0.93 and across-labels average TPR was 0.88. Including vessel radius significantly improved ACC to 0.95, and average TPR to 0.91. Finally, focusing spatial context to the Circle of Willis are resulted in best ACC of 0.96 and best average TPR of 0.93. Hence, using vessel radius and spatial context greatly improved vessel labeling, with the attained perfomance opening the avenue for clinical applications of intracranial vessel labeling.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"194 ","pages":"34-44"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10112880/pdf/nihms-1889674.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9389427","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