RESUMO
Cough-based diagnosis for respiratory diseases (RDs) using artificial intelligence (AI) has attracted considerable attention, yet many existing studies overlook confounding variables in their predictive models. These variables can distort the relationship between cough recordings (input data) and RD status (output variable), leading to biased associations and unrealistic model performance. To address this gap, we propose the Bias-Free Network (RBF-Net), an end-to-end solution that effectively mitigates the impact of confounders in the training data distribution. RBF-Net ensures accurate and unbiased RD diagnosis features, emphasizing its relevance by incorporating a COVID-19 dataset in this study. This approach aims to enhance the reliability of AI-based RD diagnosis models by navigating the challenges posed by confounding variables. A hybrid of a Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed for the feature encoder module of RBF-Net. An additional bias predictor is incorporated in the classification scheme to formulate a conditional Generative Adversarial Network (c-GAN) that helps in decorrelating the impact of confounding variables from RD prediction. The merit of RBF-Net is demonstrated by comparing classification performance with a State-of-The-Art (SoTA) Deep Learning (DL) model (CNN-LSTM) after training on different unbalanced COVID-19 data sets, created by using a large-scale proprietary cough data set. RBF-Net proved its robustness against extremely biased training scenarios by achieving test set accuracies of 84.1%, 84.6%, and 80.5% for the following confounding variables-gender, age, and smoking status, respectively. RBF-Net outperforms the CNN-LSTM model test set accuracies by 5.5%, 7.7%, and 8.2%, respectively.
RESUMO
Wireless connectivity delay, disruption, or failure can significantly affect the performance of wireless-enabled medical devices, which in turn causes potential risks to the patient. Notably, the challenges related to connectivity provisioning are exacerbated in the fifth-generation (5G)-enabled healthcare use cases where mobility is utilized. In this article, we describe relevant 5G-enabled healthcare use cases involving mobility and identify the connectivity challenges that they face. We then illustrate practical implementation considerations, tradeoffs, and future research directions for enabling reliable 5G healthcare transmissions. This is done through simulation of connected ambulances as an example use-case.
RESUMO
5G and Beyond 5G (B5G) communication networks, with their characteristics of increasing speed, connectivity, reliability, availability and capacity while reducing latency, have the potential to transform the healthcare sector by opening possibilities for novel healthcare use cases and applications. Service level agreements (SLAs) can help enable these new healthcare use cases by documenting the communication requirements, performance standards, and roles and responsibilities of the stakeholders involved in providing safe and effective 5G-enabled healthcare to patients. However, the peculiarities and nuances of 5G implementations give rise to gaps in this area that should be addressed to streamline the implementation of 5G technology in healthcare. This magazine article highlights the key challenges and describes open research questions related to SLAs for 5G-healthcare systems. Addressing the research challenges in this space will help in developing robust SLAs that can ensure that device manufacturers, network service providers, users, and regulatory authorities share a common framework to safely integrate 5G & B5G technology in healthcare.
RESUMO
Fifth generation (5G) mobile communication technology can enable novel healthcare applications and augment existing ones. However, 5G-enabled healthcare applications demand diverse technical requirements for radio communication. Knowledge of these requirements is important for developers, network providers, and regulatory authorities in the healthcare sector to facilitate safe and effective healthcare. In this paper, we review, identify, describe, and compare the requirements for communication key performance indicators in relevant healthcare use cases, including remote robotic-assisted surgery, connected ambulance, wearable and implantable devices, and service robotics for assisted living, with a focus on quantitative requirements. We also compare 5G-healthcare requirements with the current state of 5G capabilities. Finally, we identify gaps in the existing literature and highlight considerations for this space.
RESUMO
5G and beyond networks will transform the healthcare sector by opening possibilities for novel use cases and applications. Service level agreements (SLAs) can enable 5G-enabled medical device use cases by documenting how a medical device communication requirements are met by the unique characteristics of 5G networks and the roles and responsibilities of the stakeholders involved in offering safe and effective 5G-enabled healthcare to patients. However, there are gaps in this space that should be addressed to facilitate the efficient implementation of 5G technology in healthcare. Current literature is scarce regarding SLAs for 5G and is absent regarding SLAs for 5G-enabled medical devices. This paper aims to bridge these gaps by identifying key challenges, providing insight, and describing open research questions related to SLAs in 5G and specifically 5G-healthcare systems. This is helpful to network service providers, users, and regulatory authorities in developing, managing, monitoring, and evaluating SLAs in 5G-enabled medical systems.