合成健康数据在共享数据以支持生物医学研究和创新医疗保健应用的发展时有可能减轻隐私问题。基于机器学习，尤其是生成对抗网络（GAN）方法的现代方法生成的现代方法继续发展并表现出巨大的潜力。然而，缺乏系统的评估框架来基准测试方法，并确定哪些方法最合适。在这项工作中，我们引入了一个可推广的基准测试框架，以评估综合健康数据的关键特征在实用性和隐私指标方面。我们将框架应用框架来评估来自两个大型学术医疗中心的电子健康记录（EHRS）数据的合成数据生成方法。结果表明，共享合成EHR数据存在公用事业私人关系权衡。结果进一步表明，在每个用例中，在所有标准上都没有明确的方法是最好的，这使得为什么需要在上下文中评估合成数据生成方法。

A new method for solving the wave equation is presented, called the learned Born series (LBS), which is derived from a convergent Born Series but its components are found through training. The LBS is shown to be significantly more accurate than the convergent Born series for the same number of iterations, in the presence of high contrast scatterers, while maintaining a comparable computational complexity. The LBS is able to generate a reasonable prediction of the global pressure field with a small number of iterations, and the errors decrease with the number of learned iterations.

Actively monitoring machine learning models during production operations helps ensure prediction quality and detection and remediation of unexpected or undesired conditions. Monitoring models already deployed in big data environments brings the additional challenges of adding monitoring in parallel to the existing modelling workflow and controlling resource requirements. In this paper, we describe (1) a framework for monitoring machine learning models; and, (2) its implementation for a big data supply chain application. We use our implementation to study drift in model features, predictions, and performance on three real data sets. We compare hypothesis test and information theoretic approaches to drift detection in features and predictions using the Kolmogorov-Smirnov distance and Bhattacharyya coefficient. Results showed that model performance was stable over the evaluation period. Features and predictions showed statistically significant drifts; however, these drifts were not linked to changes in model performance during the time of our study.

Although prediction models for delirium, a commonly occurring condition during general hospitalization or post-surgery, have not gained huge popularity, their algorithmic bias evaluation is crucial due to the existing association between social determinants of health and delirium risk. In this context, using MIMIC-III and another academic hospital dataset, we present some initial experimental evidence showing how sociodemographic features such as sex and race can impact the model performance across subgroups. With this work, our intent is to initiate a discussion about the intersectionality effects of old age, race and socioeconomic factors on the early-stage detection and prevention of delirium using ML.

Graph neural networks (GNNs) have received great attention due to their success in various graph-related learning tasks. Several GNN frameworks have then been developed for fast and easy implementation of GNN models. Despite their popularity, they are not well documented, and their implementations and system performance have not been well understood. In particular, unlike the traditional GNNs that are trained based on the entire graph in a full-batch manner, recent GNNs have been developed with different graph sampling techniques for mini-batch training of GNNs on large graphs. While they improve the scalability, their training times still depend on the implementations in the frameworks as sampling and its associated operations can introduce non-negligible overhead and computational cost. In addition, it is unknown how much the frameworks are 'eco-friendly' from a green computing perspective. In this paper, we provide an in-depth study of two mainstream GNN frameworks along with three state-of-the-art GNNs to analyze their performance in terms of runtime and power/energy consumption. We conduct extensive benchmark experiments at several different levels and present detailed analysis results and observations, which could be helpful for further improvement and optimization.

Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from structure remains a major challenge. Here, we introduce Holographic Convolutional Neural Network (H-CNN) for proteins, which is a physically motivated machine learning approach to model amino acid preferences in protein structures. H-CNN reflects physical interactions in a protein structure and recapitulates the functional information stored in evolutionary data. H-CNN accurately predicts the impact of mutations on protein function, including stability and binding of protein complexes. Our interpretable computational model for protein structure-function maps could guide design of novel proteins with desired function.

我们介绍了贴片采样时间表（PSS）的概念，该概念在训练过程中每批次使用的视觉变压器（VIT）贴片的数量变化。由于对于大多数视觉目标（例如，分类），所有补丁都不同样重要，因此我们认为，不太重要的补丁可以用于较少的训练迭代中，从而导致较短的训练时间，对性能的影响最小。此外，我们观察到，使用PSS的训练可以使VIT在推理过程中对更宽的贴片采样范围更强。这允许在推理过程中进行吞吐量和准确性之间的细粒度，动态的权衡。我们使用PSSS在VIT上评估Imagenet的VIT，均通过从头开始训练并使用重建损耗函数进行了预训练。对于预训练的模型，与使用所有斑块相比，我们的分类准确性降低了0.26％（从25小时到17小时）降低了0.26％。代码，模型检查点和日志可在https://github.com/bradmcdanel/pss上找到。

机器学习和临床研究社区利用现实世界数据（RWD）的方法，包括电子健康记录中捕获的数据（EHR）截然不同。虽然临床研究人员谨慎使用RWD进行临床研究，但用于医疗团队的ML会消费公共数据集，并以最少的审查来开发新算法。这项研究通过开发和验证ML-DQA来弥合这一差距，ML-DQA是基于RWD最佳实践的数据质量保证框架。 ML-DQA框架适用于两个地理位置的五个ML项目，分别是不同的医疗状况和不同的人群。在这五个项目中，共收集了247,536名患者的RWD，共有2,999项质量检查和24份质量报告。出现了五种可推广的实践：所有项目都使用类似的方法来分组冗余数据元素表示；所有项目都使用自动实用程序来构建诊断和药物数据元素；所有项目都使用了一个共同的基于规则的转换库；所有项目都使用统一的方法将数据质量检查分配给数据元素；所有项目都使用类似的临床裁决方法。包括临床医生，数据科学家和受训者在内的平均有5.8个人参与每个项目实施ML-DQA，每个项目平均进行了23.4个数据元素。这项研究证明了ML-DQA在医疗项目中的重要性作用，并为团队提供了开展这些基本活动的框架。

简介白质超强度（WMHS）的自动分割是磁共振成像（MRI）神经影像分析的重要步骤。流体减弱的反转恢复（FLAIR加权）是MRI对比度，对于可视化和量化WMHS，这是脑小血管疾病和阿尔茨海默氏病（AD）特别有用的。临床MRI方案迁移到三维（3D）FLAIR加权的采集，以在所有三个体素维度中实现高空间分辨率。当前的研究详细介绍了深度学习工具的部署，以使自动化的WMH分割和表征从获得的3D Flair加权图像作为国家广告成像计划的一部分获得。 DDI研究中的642名参与者（283名男性，平均年龄：（65.18 +/- 9.33）年）中的材料和方法，在五个国家收集地点进行了培训和验证两个内部网络。在642名参与者的内部数据和一个外部数据集中，对三个模型进行了测试，其中包含来自国际合作者的29个情况。这些测试集进行了独立评估。使用了五个已建立的WMH性能指标与地面真理人体分割进行比较。测试的三个网络的结果，3D NNU-NET具有最佳性能，平均骰子相似性系数得分为0.78 +/- 0.10，其性能优于内部开发的2.5D模型和SOTA DEEP DEEP BAYESIAN网络。结论MRI协议中3D Flair加权图像的使用越来越多，我们的结果表明，WMH分割模型可以在3D数据上进行训练，并产生与无需更高的或更好的无需先进的WMH分割性能用于包括T1加权图像系列。

深度学习在学习高维数据的低维表示方面取得了巨大的成功。如果在感兴趣的数据中没有隐藏的低维结构，那么这一成功将是不可能的。这种存在是由歧管假设提出的，该假设指出数据在于固有维度低的未知流形。在本文中，我们认为该假设无法正确捕获数据中通常存在的低维结构。假设数据在于单个流形意味着整个数据空间的内在维度相同，并且不允许该空间的子区域具有不同数量的变异因素。为了解决这一缺陷，我们提出了多种假设的结合，该假设适应了非恒定固有维度的存在。我们从经验上验证了在常用图像数据集上的这一假设，发现确实应该允许内在维度变化。我们还表明，具有较高内在维度的类更难分类，以及如何使用这种见解来提高分类精度。然后，我们将注意力转移到该假设的影响下，在深层生成模型（DGM）的背景下。当前的大多数DGM都难以建模具有几个连接组件和/或不同固有维度的数据集建模。为了解决这些缺点，我们提出了群集的DGM，首先将数据聚集，然后在每个群集上训练DGM。我们表明，聚类的DGM可以模拟具有不同固有维度的多个连接组件，并在没有增加计算要求的情况下经验优于其非簇的非群体。