流程挖掘的学科在医疗保健领域成功应用程序具有可靠的记录。在这样的研究领域，我们进行了与德国Uniklinik Aachen医院重症监护病房（ICU）病房有关的案例研究。这项工作的目的是双重的：开发一个规范模型，该模型代表了COVID-19患者治疗的临床指南，并分析观察到的行为（记录在医院的信息系统中）对此类准则的依从性。我们表明，通过一致性检查技术，可以分析COVID-19患者的护理过程，并强调与临床准则的主要偏差。结果为医生提供了改善过程并确保服务质量和患者满意度的有用指示。我们将结果模型作为开源BPMN文件共享。

Kirilov et al (2019) develop a metric, called Panoptic Quality (PQ), to evaluate image segmentation methods. The metric is based on a confusion table, and compares a predicted to a ground truth segmentation. The only non straightforward part in this comparison is to align the segments in the two segmentations. A metric only works well if that alignment is a partial bijection. Kirilov et al (2019) list 3 desirable properties for a definition of alignment: it should be simple, interpretable and effectively computable. There are many definitions guaranteeing a partial bijection and these 3 properties. We present the weakest: one that is both sufficient and necessary to guarantee that the alignment is a partial bijection. This new condition is effectively computable and natural. It simply says that the number of correctly predicted elements (in image segmentation, the pixels) should be larger than the number of missed, and larger than the number of spurious elements. This is strictly weaker than the proposal in Kirilov et al (2019). In formulas, instead of |TP|> |FN\textbar| + |FP|, the weaker condition requires that |TP|> |FN| and |TP| > |FP|. We evaluate the new alignment condition theoretically and empirically.

Individual particle rotation and displacement were measured in triaxial tests on transparent sand stabilized with geogrid simulants. The Cellpose U-Net model, originally developed to segment biological cells, was trained to segment images of fused quartz particles. The Score-CAM metric from the field of Explainable AI was used to validate the application of Cellpose to segment particles of fused quartz. These segmented particles were characterized in terms of Fourier shape descriptors and tracked across images. The measured particle displacements in the monotonic triaxial tests correlated with displacement fields from Digital Image Correlation (DIC). In contrast to DIC, the new technique also allows for the measurement of individual particle rotation. The particle rotation measurements were found to be repeatable across different specimens. A state boundary line between probable and improbable particle motions could be identified for a given test based on the measured particle displacements and rotations. The size of the zone of probable motions was used to quantify the effectiveness of the stabilizing inclusions. The results of repeated load tests revealed that the honeycomb inclusions used stabilized the specimens by reducing both particle displacements and rotations.

Human Activity Recognition (HAR) using on-body devices identifies specific human actions in unconstrained environments. HAR is challenging due to the inter and intra-variance of human movements; moreover, annotated datasets from on-body devices are scarce. This problem is mainly due to the difficulty of data creation, i.e., recording, expensive annotation, and lack of standard definitions of human activities. Previous works demonstrated that transfer learning is a good strategy for addressing scenarios with scarce data. However, the scarcity of annotated on-body device datasets remains. This paper proposes using datasets intended for human-pose estimation as a source for transfer learning; specifically, it deploys sequences of annotated pixel coordinates of human joints from video datasets for HAR and human pose estimation. We pre-train a deep architecture on four benchmark video-based source datasets. Finally, an evaluation is carried out on three on-body device datasets improving HAR performance.

Mechanistic cardiac electrophysiology models allow for personalized simulations of the electrical activity in the heart and the ensuing electrocardiogram (ECG) on the body surface. As such, synthetic signals possess known ground truth labels of the underlying disease and can be employed for validation of machine learning ECG analysis tools in addition to clinical signals. Recently, synthetic ECGs were used to enrich sparse clinical data or even replace them completely during training leading to improved performance on real-world clinical test data. We thus generated a novel synthetic database comprising a total of 16,900 12 lead ECGs based on electrophysiological simulations equally distributed into healthy control and 7 pathology classes. The pathological case of myocardial infraction had 6 sub-classes. A comparison of extracted features between the virtual cohort and a publicly available clinical ECG database demonstrated that the synthetic signals represent clinical ECGs for healthy and pathological subpopulations with high fidelity. The ECG database is split into training, validation, and test folds for development and objective assessment of novel machine learning algorithms.

Dysgraphia, a handwriting learning disability, has a serious negative impact on children's academic results, daily life and overall wellbeing. Early detection of dysgraphia allows for an early start of a targeted intervention. Several studies have investigated dysgraphia detection by machine learning algorithms using a digital tablet. However, these studies deployed classical machine learning algorithms with manual feature extraction and selection as well as binary classification: either dysgraphia or no dysgraphia. In this work, we investigated fine grading of handwriting capabilities by predicting SEMS score (between 0 and 12) with deep learning. Our approach provide accuracy more than 99% and root mean square error lower than one, with automatic instead of manual feature extraction and selection. Furthermore, we used smart pen called SensoGrip, a pen equipped with sensors to capture handwriting dynamics, instead of a tablet, enabling writing evaluation in more realistic scenarios.

必须校准不确定性估计值（即准确）和清晰（即信息性），以便有用。这激发了各种重新校准的方法，这些方法使用固定数据将未校准的模型转化为校准模型。但是，由于原始模型也是概率模型，因此现有方法的适用性受到限制。我们在回归中引入了一种用于重新校准的算法类别，我们称为模块化保形校准（MCC）。该框架允许人们将任何回归模型转换为校准的概率模型。 MCC的模块化设计使我们能够对现有算法进行简单调整，以实现良好的分配预测。我们还为MCC算法提供有限样本的校准保证。我们的框架恢复了等渗的重新校准，保形校准和共形间隔预测，这意味着我们的理论结果也适用于这些方法。最后，我们对17个回归数据集进行了MCC的经验研究。我们的结果表明，在我们的框架中设计的新算法实现了接近完美的校准，并相对于现有方法提高了清晰度。

手写文本识别（HTR）模型的性能在很大程度上取决于标签和代表性培训样本的可用性。但是，在许多应用程序方案中，标有样品的稀缺或昂贵。在这项工作中，我们提出了一种自我训练的方法，以训练HTR模型仅在合成样品和未标记的数据上进行培训。拟议的培训方案使用对合成数据训练的初始模型，对未标记的目标数据集进行预测。从这个初始模型的性能相当差，我们表明，通过针对预测的伪标签进行训练，可以进行相当大的适应性。此外，调查的自我训练策略不需要任何手动注释的培训样本。我们在四个广泛使用的基准数据集上评估了所提出的方法，并显示了以完全监督方式训练的模型来缩小差距的有效性。

一个经过深入研究的问题是，将机器学习算法提供的置信度得分校准为地面真实概率。我们的起点是，校准似乎与班级的加权不相容，这是一种经常使用的技术，当时一个类别不那么普遍（阶级失衡）或希望实现一些外部目标（成本敏感的学习）。我们为这种不兼容提供了基于模型的解释，并使用我们的拟人化模型来生成一种从算法中恢复似然的简单方法，该算法因类权重而被误解。我们在Rajpurkar，Irvin，Zhu等的二元肺炎检测任务中验证了这种方法。（2017）。

We present an approach to quantifying both aleatoric and epistemic uncertainty for deep neural networks in image classification, based on generative adversarial networks (GANs). While most works in the literature that use GANs to generate out-of-distribution (OoD) examples only focus on the evaluation of OoD detection, we present a GAN based approach to learn a classifier that produces proper uncertainties for OoD examples as well as for false positives (FPs). Instead of shielding the entire in-distribution data with GAN generated OoD examples which is state-of-the-art, we shield each class separately with out-of-class examples generated by a conditional GAN and complement this with a one-vs-all image classifier. In our experiments, in particular on CIFAR10, CIFAR100 and Tiny ImageNet, we improve over the OoD detection and FP detection performance of state-of-the-art GAN-training based classifiers. Furthermore, we also find that the generated GAN examples do not significantly affect the calibration error of our classifier and result in a significant gain in model accuracy.