Proceedings of machine learning research最新文献

{"title":"Diffusion Models To Predict 3D Late Mechanical Activation From Sparse 2D Cardiac MRIs.","authors":"Nivetha Jayakumar, Jiarui Xing, Tonmoy Hossain, Fred Epstein, Kenneth Bilchick, Miaomiao Zhang","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Identifying regions of late mechanical activation (LMA) of the left ventricular (LV) myocardium is critical in determining the optimal pacing site for cardiac resynchronization therapy in patients with heart failure. Several deep learning-based approaches have been developed to predict 3D LMA maps of LV myocardium from a stack of sparse 2D cardiac magnetic resonance imaging (MRIs). However, these models often loosely consider the geometric shape structure of the myocardium. This makes the reconstructed activation maps suboptimal; hence leading to a reduced accuracy of predicting the late activating regions of hearts. In this paper, we propose to use shape-constrained diffusion models to better reconstruct a 3D LMA map, given a limited number of 2D cardiac MRI slices. In contrast to previous methods that primarily rely on spatial correlations of image intensities for 3D reconstruction, our model leverages object shape as priors learned from the training data to guide the reconstruction process. To achieve this, we develop a joint learning network that simultaneously learns a mean shape under deformation models. Each reconstructed image is then considered as a deformed variant of the mean shape. To validate the performance of our model, we train and test the proposed framework on a publicly available mesh dataset of 3D myocardium and compare it with state-of-the-art deep learning-based reconstruction models. Experimental results show that our model achieves superior performance in reconstructing the 3D LMA maps as compared to the state-of-the-art models.</p>","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"225 ","pages":"190-200"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10958778/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140208466","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}

{"title":"A Meta-Evaluation of Faithfulness Metrics for Long-Form Hospital-Course Summarization.","authors":"Griffin Adams, Jason Zucker, Noémie Elhadad","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Long-form clinical summarization of hospital admissions has real-world significance because of its potential to help both clinicians and patients. The factual consistency of summaries-their faithfulness-is critical to their safe usage in clinical settings. To better understand the limitations of state-of-the-art natural language processing (NLP) systems, as well as the suitability of existing evaluation metrics, we benchmark faithfulness metrics against fine-grained human annotations for model-generated summaries of a patient's Brief Hospital Course. We create a corpus of patient hospital admissions and summaries for a cohort of HIV patients, each with complex medical histories. Annotators are presented with summaries and source notes, and asked to categorize manually highlighted summary elements (clinical entities like conditions and medications as well as actions like \"following up\") into one of three categories: \"Incorrect,\" \"Missing,\" and \"Not in Notes.\" We meta-evaluate a broad set of faithfulness metrics-proposed for the general NLP domain-by measuring the correlation of metric scores to clinician ratings. Across metrics, we explore the importance of domain adaptation (e.g. the impact of in-domain pre-training and metric fine-tuning), the use of source-summary alignments, and the effects of distilling a single metric from an ensemble. We find that off-the-shelf metrics with no exposure to clinical text correlate well to clinician ratings yet overly rely on copy-and-pasted text. As a practical guide, we observe that most metrics correlate best to clinicians when provided with one summary sentence at a time and a minimal set of supporting sentences from the notes before discharge.</p>","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"219 ","pages":"2-30"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11441639/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142334002","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}

{"title":"Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning.","authors":"Zhe Huang, Benjamin S Wessler, Michael C Hughes","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Aortic stenosis (AS) is a degenerative valve condition that causes substantial morbidity and mortality. This condition is under-diagnosed and under-treated. In clinical practice, AS is diagnosed with expert review of transthoracic echocardiography, which produces dozens of ultrasound images of the heart. Only some of these views show the aortic valve. To automate screening for AS, deep networks must learn to mimic a human expert's ability to identify views of the aortic valve then aggregate across these relevant images to produce a study-level diagnosis. We find previous approaches to AS detection yield insufficient accuracy due to relying on inflexible averages across images. We further find that off-the-shelf attention-based multiple instance learning (MIL) performs poorly. We contribute a new end-to-end MIL approach with two key methodological innovations. First, a supervised attention technique guides the learned attention mechanism to favor relevant views. Second, a novel self-supervised pretraining strategy applies contrastive learning on the representation of the whole study instead of individual images as commonly done in prior literature. Experiments on an open-access dataset and a temporally-external heldout set show that our approach yields higher accuracy while reducing model size.</p>","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"219 ","pages":"285-307"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10923076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140095343","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}

{"title":"Fairness-Aware Class Imbalanced Learning on Multiple Subgroups.","authors":"Davoud Ataee Tarzanagh, Bojian Hou, Boning Tong, Qi Long, Li Shen","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>We present a novel Bayesian-based optimization framework that addresses the challenge of generalization in overparameterized models when dealing with imbalanced subgroups and limited samples per subgroup. Our proposed tri-level optimization framework utilizes <i>local</i> predictors, which are trained on a small amount of data, as well as a fair and class-balanced predictor at the middle and lower levels. To effectively overcome saddle points for minority classes, our lower-level formulation incorporates sharpness-aware minimization. Meanwhile, at the upper level, the framework dynamically adjusts the loss function based on validation loss, ensuring a close alignment between the <i>global</i> predictor and local predictors. Theoretical analysis demonstrates the framework's ability to enhance classification and fairness generalization, potentially resulting in improvements in the generalization bound. Empirical results validate the superior performance of our tri-level framework compared to existing state-of-the-art approaches. The source code can be found at https://github.com/PennShenLab/FACIMS.</p>","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"216 ","pages":"2123-2133"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11003754/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140857599","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}

{"title":"Maximum Likelihood Estimation of Flexible Survival Densities with Importance Sampling.","authors":"Mert Ketenci, Shreyas Bhave, Noémie Elhadad, Adler Perotte","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Survival analysis is a widely-used technique for analyzing time-to-event data in the presence of censoring. In recent years, numerous survival analysis methods have emerged which scale to large datasets and relax traditional assumptions such as proportional hazards. These models, while being performant, are very sensitive to model hyperparameters including: (1) number of bins and bin size for discrete models and (2) number of cluster assignments for mixture-based models. Each of these choices requires extensive tuning by practitioners to achieve optimal performance. In addition, we demonstrate in empirical studies that: (1) optimal bin size may drastically differ based on the metric of interest (e.g., concordance vs brier score), and (2) mixture models may suffer from mode collapse and numerical instability. We propose a survival analysis approach which eliminates the need to tune hyperparameters such as mixture assignments and bin sizes, reducing the burden on practitioners. We show that the proposed approach matches or outperforms baselines on several real-world datasets.</p>","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"219 ","pages":"360-380"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11441640/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142334003","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}

{"title":"Bringing At-home Pediatric Sleep Apnea Testing Closer to Reality: A Multi-modal Transformer Approach.","authors":"Hamed Fayyaz, Abigail Strang, Rahmatollah Beheshti","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Sleep apnea in children is a major health problem affecting one to five percent of children (in the US). If not treated in a timely manner, it can also lead to other physical and mental health issues. Pediatric sleep apnea has different clinical causes and characteristics than adults. Despite a large group of studies dedicated to studying adult apnea, pediatric sleep apnea has been studied in a much less limited fashion. Relatedly, at-home sleep apnea testing tools and algorithmic methods for automatic detection of sleep apnea are widely present for adults, but not children. In this study, we target this gap by presenting a machine learning-based model for detecting apnea events from commonly collected sleep signals. We show that our method outperforms state-of-the-art methods across two public datasets, as determined by the F1-score and AUROC measures. Additionally, we show that using two of the signals that are easier to collect at home (ECG and SpO₂) can also achieve very competitive results, potentially addressing the concerns about collecting various sleep signals from children outside the clinic. Therefore, our study can greatly inform ongoing progress toward increasing the accessibility of pediatric sleep apnea testing and improving the timeliness of the treatment interventions.</p>","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"219 ","pages":"167-185"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10854997/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139725300","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}

{"title":"A Computational Framework for EEG Causal Oscillatory Connectivity.","authors":"Eric Rawls, Casey Gilmore, Erich Kummerfeld, Kelvin Lim, Tasha Nienow","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Here we advance a new approach for measuring EEG causal oscillatory connectivity, capitalizing on recent advances in causal discovery analysis for skewed time series data and in spectral parameterization of time-frequency (TF) data. We first parameterize EEG TF data into separate oscillatory and aperiodic components. We then measure causal interactions between separated oscillatory data with the recently proposed causal connectivity method Greedy Adjacencies and Non-Gaussian Orientations (GANGO). We apply GANGO to contemporaneous time series, then we extend the GANGO method to lagged data that control for temporal autocorrelation. We apply this approach to EEG data acquired in the context of a clinical trial investigating noninvasive transcranial direct current stimulation to treat executive dysfunction following mild Traumatic Brain Injury (mTBI). First, we analyze whole-scalp oscillatory connectivity patterns using community detection. Then we demonstrate that tDCS increases the effect size of causal theta-band oscillatory connections between prefrontal sensors and the rest of the scalp, while simultaneously decreasing causal alpha-band oscillatory connections between prefrontal sensors and the rest of the scalp. Improved executive functioning following tDCS could result from increased prefrontal causal theta oscillatory influence, and decreased prefrontal alpha-band causal oscillatory influence.</p>","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"223 ","pages":"40-51"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11545965/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633724","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}

{"title":"Optimizing Dynamic Antibiotic Treatment Strategies against Invasive Methicillin-Resistant <i>Staphylococcus Aureus</i> Infections using Causal Survival Forests and G-Formula on Statewide Electronic Health Record Data.","authors":"Inyoung Jun, Scott A Cohen, Sarah E Ser, Simone Marini, Robert J Lucero, Jiang Bian, Mattia Prosperi","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Developing models for individualized, time-varying treatment optimization from observational data with large variable spaces, e.g., electronic health records (EHR), is problematic because of inherent, complex bias that can change over time. Traditional methods such as the g-formula are robust, but must identify critical subsets of variables due to combinatorial issues. Machine learning approaches such as causal survival forests have fewer constraints and can provide fine-tuned, individualized counterfactual predictions. In this study, we aimed to optimize time-varying antibiotic treatment -identifying treatment heterogeneity and conditional treatment effects- against invasive methicillin-resistant <i>Staphylococcus Aureus</i> (MRSA) infections, using statewide EHR data collected in Florida, USA. While many previous studies focused on measuring the effects of the first empiric treatment (i.e., usually vancomycin), our study focuses on dynamic sequential treatment changes, comparing possible vancomycin switches with other antibiotics at clinically relevant time points, e.g., after obtaining a bacterial culture and susceptibility testing. Our study population included adult individuals admitted to the hospital with invasive MRSA. We collected demographic, clinical, medication, and laboratory information from the EHR for these patients. Then, we followed three sequential antibiotic choices (i.e., their empiric treatment, subsequent directed treatment, and final sustaining treatment), evaluating 30-day mortality as the outcome. We applied both causal survival forests and g-formula using different clinical intervention policies. We found that switching from vancomycin to another antibiotic improved survival probability, yet there was a benefit from initiating vancomycin compared to not using it at any time point. These findings show consistency with the empiric choice of vancomycin before confirmation of MRSA and shed light on how to manage switches on course. In conclusion, this application of causal machine learning on EHR demonstrates utility in modeling dynamic, heterogeneous treatment effects that cannot be evaluated precisely using randomized clinical trials.</p>","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"218 ","pages":"98-115"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10584043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49686010","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}

{"title":"Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Search.","authors":"Joseph D Ramsey, Bryan Andrews","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>We give novel Python and R interfaces for the (Java) Tetrad project for causal modeling, search, and estimation. The Tetrad project is a mainstay in the literature, having been under consistent development for over 30 years. Some of its algorithms are now classics, like PC and FCI; others are recent developments. It is increasingly the case, however, that researchers need to access the underlying Java code from Python or R. Existing methods for doing this are inadequate. We provide new, up-to-date methods using the JPype Python-Java interface and the Reticulate Python-R interface, directly solving these issues. With the addition of some simple tools and the provision of working examples for both Python and R, using JPype and Reticulate to interface Python and R with Tetrad is straightforward and intuitive.</p>","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"223 ","pages":"40-51"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11316512/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141918282","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}

{"title":"Fair Survival Time Prediction via Mutual Information Minimization.","authors":"Hyungrok Do, Yuxin Chang, Yoon Sang Cho, Padhraic Smyth, Judy Zhong","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Survival analysis is a general framework for predicting the time until a specific event occurs, often in the presence of censoring. Although this framework is widely used in practice, few studies to date have considered fairness for time-to-event outcomes, despite recent significant advances in the algorithmic fairness literature more broadly. In this paper, we propose a framework to achieve demographic parity in survival analysis models by minimizing the mutual information between predicted time-to-event and sensitive attributes. We show that our approach effectively minimizes mutual information to encourage statistical independence of time-to-event predictions and sensitive attributes. Furthermore, we propose four types of disparity assessment metrics based on common survival analysis metrics. Through experiments on multiple benchmark datasets, we demonstrate that by minimizing the dependence between the prediction and the sensitive attributes, our method can systematically improve the fairness of survival predictions and is robust to censoring.</p>","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"219 ","pages":"128-149"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11067550/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140861818","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}