2022-12-06
With the increasing popularity of telehealth, it becomes critical to ensure that basic physiological signals can be monitored accurately at home, with minimal patient overhead. In this paper, we propose a contactless approach for monitoring patients' blood oxygen at home, simply by analyzing the radio signals in the room, without any wearable devices. We extract the patients' respiration from the radio signals that bounce off their bodies and devise a novel neural network that infers a patient's oxygen estimates from their breathing signal. Our model, called \emph{Gated BERT-UNet}, is designed to adapt to the patient's medical indices (e.g., gender, sleep stages). It has multiple predictive heads and selects the most suitable head via a gate controlled by the person's physiological indices. Extensive empirical results show that our model achieves high accuracy on both medical and radio datasets.
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2022-01-11

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2021-11-22

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2022-09-14

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2022-12-14
The promise of Mobile Health (mHealth) is the ability to use wearable sensors to monitor participant physiology at high frequencies during daily life to enable temporally-precise health interventions. However, a major challenge is frequent missing data. Despite a rich imputation literature, existing techniques are ineffective for the pulsative signals which comprise many mHealth applications, and a lack of available datasets has stymied progress. We address this gap with PulseImpute, the first large-scale pulsative signal imputation challenge which includes realistic mHealth missingness models, an extensive set of baselines, and clinically-relevant downstream tasks. Our baseline models include a novel transformer-based architecture designed to exploit the structure of pulsative signals. We hope that PulseImpute will enable the ML community to tackle this significant and challenging task.
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2022-04-13
Nowadays, due to the widespread use of smartphones in everyday life and the improvement of computational capabilities of these devices, many complex tasks can now be deployed on them. Concerning the need for continuous monitoring of vital signs, especially for the elderly or those with certain types of diseases, the development of algorithms that can estimate vital signs using smartphones has attracted researchers worldwide. Such algorithms estimate vital signs (heart rate and oxygen saturation level) by processing an input PPG signal. These methods often apply multiple pre-processing steps to the input signal before the prediction step. This can increase the computational complexity of these methods, meaning only a limited number of mobile devices can run them. Furthermore, multiple pre-processing steps also require the design of a couple of hand-crafted stages to obtain an optimal result. This research proposes a novel end-to-end solution to mobile-based vital sign estimation by deep learning. The proposed method does not require any pre-processing. Due to the use of fully convolutional architecture, the parameter count of our proposed model is, on average, a quarter of the ordinary architectures that use fully-connected layers as the prediction heads. As a result, the proposed model has less over-fitting chance and computational complexity. A public dataset for vital sign estimation, including 62 videos collected from 35 men and 27 women, is also provided. The experimental results demonstrate state-of-the-art estimation accuracy.
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2021-12-14

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2021-11-12

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2022-09-16
COVID-19大流行对全球医疗保健系统造成了沉重的负担，并造成了巨大的社会破坏和经济损失。已经提出了许多深度学习模型来执行临床预测任务，例如使用电子健康记录（EHR）数据在重症监护病房中为Covid-19患者的死亡率预测。尽管在某些临床应用中取得了最初的成功，但目前缺乏基准测试结果来获得公平的比较，因此我们可以选择最佳模型以供临床使用。此外，传统预测任务的制定与重症监护现实世界的临床实践之间存在差异。为了填补这些空白，我们提出了两项​​临床预测任务，特定于结局的预测和重症监护病房中的COVID-19患者的早期死亡率预测。这两个任务是根据幼稚的停车时间和死亡率预测任务的改编，以适应COVID-19患者的临床实践。我们提出了公平，详细的开源数据预处管道，并评估了两项任务的17个最先进的预测模型，包括5个机器学习模型，6种基本的深度学习模型和6种专门为EHR设计的深度学习预测模型数据。我们使用来自两个现实世界Covid-19 EHR数据集的数据提供基准测试结果。这两个数据集都可以公开可用，而无需任何查询，并且可以根据要求访问一个数据集。我们为两项任务提供公平，可重复的基准测试结果。我们在在线平台上部署所有实验结果和模型。我们还允许临床医生和研究人员将其数据上传到平台上，并使用训练有素的模型快速获得预测结果。我们希望我们的努力能够进一步促进Covid-19预测建模的深度学习和机器学习研究。
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2021-11-19

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2022-09-08
Covid-19在全球范围内影响了223多个国家。迫切需要非侵入性，低成本和高度可扩展的解决方案来检测COVID-19，尤其是在PCR测试无普遍可用的低资源国家。我们的目的是开发一个深度学习模型，使用普通人群（语音录音和简短问卷）通过其个人设备自发提供的语音数据记录来识别Covid-19。这项工作的新颖性在于开发一个深度学习模型，以鉴定来自语音记录的199名患者。方法：我们使用了由893个音频样本组成的剑桥大学数据集，该数据集由4352名参与者的人群来源，这些参与者使用了COVID-19 Sounds应用程序。使用MEL光谱分析提取语音功能。根据语音数据，我们开发了深度学习分类模型，以检测阳性的Covid-19情况。这些模型包括长期术语记忆（LSTM）和卷积神经网络（CNN）。我们将它们的预测能力与基线分类模型进行了比较，即逻辑回归和支持向量机。结果：基于MEL频率CEPSTRAL系数（MFCC）功能的LSTM具有最高的精度（89％），其灵敏度和特异性分别为89％和89％，其结果通过提议的模型获得了显着改善，这表明该结果显着改善与艺术状态获得的结果相比，COVID-19诊断的预测准确性。结论：深度学习可以检测到199例患者的声音中的细微变化，并有令人鼓舞的结果。作为当前测试技术的补充，该模型可以使用简单的语音分析帮助卫生专业人员快速诊断和追踪Covid-19案例
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2021-11-16

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2021-11-18
2019年12月，一个名为Covid-19的新型病毒导致了迄今为止的巨大因果关系。与新的冠状病毒的战斗在西班牙语流感后令人振奋和恐怖。虽然前线医生和医学研究人员在控制高度典型病毒的传播方面取得了重大进展，但技术也证明了在战斗中的重要性。此外，许多医疗应用中已采用人工智能，以诊断许多疾病，甚至陷入困境的经验丰富的医生。因此，本调查纸探讨了提议的方法，可以提前援助医生和研究人员，廉价的疾病诊断方法。大多数发展中国家难以使用传统方式进行测试，但机器和深度学习可以采用显着的方式。另一方面，对不同类型的医学图像的访问已经激励了研究人员。结果，提出了一种庞大的技术数量。本文首先详细调了人工智能域中传统方法的背景知识。在此之后，我们会收集常用的数据集及其用例日期。此外，我们还显示了采用深入学习的机器学习的研究人员的百分比。因此，我们对这种情况进行了彻底的分析。最后，在研究挑战中，我们详细阐述了Covid-19研究中面临的问题，我们解决了我们的理解，以建立一个明亮健康的环境。
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2022-07-13

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2022-08-18

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2021-11-18

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2022-11-09
Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, or heart fluttering, and is therefore of particular interest. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning. It providing a taxonomy based on the factors that divide anomaly detection models into different categories. Aside from describing the basic anomaly detection technique for each category, the advantages and limitations are also discussed. Furthermore, this study includes examples of deep anomaly detection in time series across various application domains in recent years. It finally summarises open issues in research and challenges faced while adopting deep anomaly detection models.
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2021-11-16

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2022-06-13

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2022-11-08
Health sensing for chronic disease management creates immense benefits for social welfare. Existing health sensing studies primarily focus on the prediction of physical chronic diseases. Depression, a widespread complication of chronic diseases, is however understudied. We draw on the medical literature to support depression prediction using motion sensor data. To connect human expertise in the decision-making, safeguard trust for this high-stake prediction, and ensure algorithm transparency, we develop an interpretable deep learning model: Temporal Prototype Network (TempPNet). TempPNet is built upon the emergent prototype learning models. To accommodate the temporal characteristic of sensor data and the progressive property of depression, TempPNet differs from existing prototype learning models in its capability of capturing the temporal progression of depression. Extensive empirical analyses using real-world motion sensor data show that TempPNet outperforms state-of-the-art benchmarks in depression prediction. Moreover, TempPNet interprets its predictions by visualizing the temporal progression of depression and its corresponding symptoms detected from sensor data. We further conduct a user study to demonstrate its superiority over the benchmarks in interpretability. This study offers an algorithmic solution for impactful social good - collaborative care of chronic diseases and depression in health sensing. Methodologically, it contributes to extant literature with a novel interpretable deep learning model for depression prediction from sensor data. Patients, doctors, and caregivers can deploy our model on mobile devices to monitor patients' depression risks in real-time. Our model's interpretability also allows human experts to participate in the decision-making by reviewing the interpretation of prediction outcomes and making informed interventions.
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