Drones-aided Asset Maintenance in Hospitals

The rapid outbreak of COVID-19 pandemic invoked scientists and researchers to prepare the world for future disasters. During the pandemic, global authorities on healthcare urged the importance of disinfection of objects and surfaces. To implement efficient and safe disinfection services during the pandemic, robots have been utilized for indoor assets. In this paper, we envision the use of drones for disinfection of outdoor assets in hospitals and other facilities. Such heterogeneous assets may have different service demands (e.g., service time, quantity of the disinfectant material etc.), whereas drones have typically limited capacity (i.e., travel time, disinfectant carrying capacity). To serve all the facility assets in an efficient manner, the drone to assets allocation and drone travel routes must be optimized. In this paper, we formulate the capacitated vehicle routing problem (CVRP) to find optimal route for each drone such that the total service time is minimized, while simultaneously the drones meet the demands of each asset allocated to it. The problem is solved using mixed integer programming (MIP). As CVRP is an NP-hard problem, we propose a lightweight heuristic to achieve sub-optimal performance while reducing the time complexity in solving the problem involving a large number of assets. © 2022 IEEE.

A Unified Framework for Differentiated Services in Intelligent Healthcare Systems

The Coronavirus disease 2019 (COVID-19) outbreak continues to significantly expose the vulnerabilities of healthcare systems around the world. These unprecedented circumstances create an opportunity for improving healthcare services which is desperately needed. This paper proposes a novel framework that distributes the patients across heterogeneous medical facilities (MFs) so that a weighted sum of the expected service time (EST) and service time tail probability (STTP) for all patients is minimized. We propose a model-based and model-free algorithms to schedule patients requests across the MFs. Our algorithms prioritize the patients with severe/critical conditions over others who can tolerate more delay in service. Based on the model-based approach, we formulate an optimization problem as a convex combination of both EST and STTP metrics, and apply an efficient iterative algorithm to solve it. Then, a more practical model-free scheme is proposed by adopting a deep reinforcement learning approach. Our model-free approach does not rely on pre-defined models or assumptions about the environment. Rather, it learns to choose scheduling decisions solely through observations of the resulting performance of past decisions. Our extensive results demonstrate a significant performance improvement of our proposed scheduling schemes when compared with other algorithms and competitive baselines.

Machine learning screening of COVID-19 patients based on X-ray images for unbalanced classes

Background: COVID-19 is a pandemic that had already infected more than forty-six million people and caused more than a million deaths by 1st of November 2020. The virus pandemic appears to have had a catastrophic effect on the global population's safety. Therefore, efficient detection of infected patients is a key phase in the battle against COVID-19. One of the main screening methods is radiological testing. The goal of this study is using chest X-ray images to detect COVID-19 pneumonia patients while optimizing detection efficiency. Methods: As shown in Figure 1, we combined three methods to detect COVID-19 namely: convolutional neural network, transfer learning, and the focal loss1 function which are used for unbalanced classes, to build three binary classifiers which are COVID-19 versus normal, COVID-19 versus pneumonia, and COVID-19 versus normal pneumonia (normal and pneumonia). The database used2 includes a mixture of 400 COVID-19, 1,340 viral pneumonia, 2,560 bacterial pneumonia, and 1,340 normal chest X-ray images for training, validation, and testing of four pre-trained deep convolutional neural networks. Then, the pre-trained model that gives the best results was chosen to improve its performances by two enhancement techniques which are image augmentation, allowing us to reach approximately 2,500 images per class, and the adjustment of focal loss hyperparameters. Results: A comparative study was conducted of our proposed classifiers with well-known classifiers and obtained much better results in terms of accuracy, specificity, sensitivity and precision, as illustrated in Table 1. Conclusion: The high performance of this computer-aided diagnostic technique may greatly increase the screening speed and reliability of COVID-19 diagnostic cases. Particularly, at the crowded emergency services, it will be particularly helpful in this pandemic when the risk of infection and the necessity for prevention initiatives run contrary to the available resources.

Machine Learning Screening of COVID-19 Patients Based on X-ray Images for Imbalanced Classes

COVID-19 is a virus that has infected more than one hundred and fifty million people and caused more than three million deaths by 13th of Mai 2021 and is having a catastrophic effect on the world population's safety. Therefore, early detection of infected people is essential to fight this pandemic and one of the main screening methods is radiological testing. The goal of this study is the usage of chest x-ray images (CXRs) to effectively identify patients with COVID-19 pneumonia. To achieve an efficient model, we combined three methods named: Convolution Neural Network (CNN), transfer learning, and the focal loss function which is used for imbalanced classes to build 3 binary classifiers, namely COVID-19 vs Normal, COVID-19 vs pneumonia and COVID-19 vs Normal Pneumonia (Normal and Pneumonia). A comparative study has been made between our proposed classifiers with well-known classifiers and provided enhanced results in terms of accuracy, specificity, sensitivity and precision. The high performance of this computer-Aided diagnostic technique may greatly increase the screening speed and reliability of COVID-19 detection. © 2021 IEEE.