Proceedings of machine learning research最新文献

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 local 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 global 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.

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.

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.

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.

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 Staphylococcus Aureus (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.

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.

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.

Reliable extraction of temporal relations from clinical notes is a growing need in many clinical research domains. Our work introduces typed markers to the task of clinical temporal relation extraction. We demonstrate that the addition of medical entity information to clinical text as tags with context sentences then input to a transformer-based architecture can outperform more complex systems requiring feature engineering and temporal reasoning. We propose several strategies of typed marker creation that incorporate entity type information at different granularities, with extensive experiments to test their effectiveness. Our system establishes the best result on I2B2, a clinical benchmark dataset for temporal relation extraction, with a F1 at 83.5% that provides a substantial 3.3% improvement over the previous best system.

We introduce the Explainable Analytical Systems Lab (EASL) framework, an end-to-end solution designed to facilitate the development, implementation, and evaluation of clinical machine learning (ML) tools. EASL is highly versatile and applicable to a variety of contexts and includes resources for data management, ML model development, visualization and user interface development, service hosting, and usage analytics. To demonstrate its practical applications, we present the EASL framework in the context of a case study: designing and evaluating a deep learning classifier to predict diagnoses from medical imaging. The framework is composed of three modules, each with their own set of resources. The Workbench module stores data and develops initial ML models, the Canvas module contains a medical imaging viewer and web development framework, and the Studio module hosts the ML model and provides web analytics and support for conducting user studies. EASL encourages model developers to take a holistic view by integrating the model development, implementation, and evaluation into one framework, and thus ensures that models are both effective and reliable when used in a clinical setting. EASL contributes to our understanding of machine learning applied to healthcare by providing a comprehensive framework that makes it easier to develop and evaluate ML tools within a clinical setting.

Just-in-Time Adaptive Interventions (JITAIs) are a class of personalized health interventions developed within the behavioral science community. JITAIs aim to provide the right type and amount of support by iteratively selecting a sequence of intervention options from a pre-defined set of components in response to each individual's time varying state. In this work, we explore the application of reinforcement learning methods to the problem of learning intervention option selection policies. We study the effect of context inference error and partial observability on the ability to learn effective policies. Our results show that the propagation of uncertainty from context inferences is critical to improving intervention efficacy as context uncertainty increases, while policy gradient algorithms can provide remarkable robustness to partially observed behavioral state information.