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Smart War on COVID-19 and Global Pandemics
Published in Chhabi Rani Panigrahi, Bibudhendu Pati, Mamata Rath, Rajkumar Buyya, Computational Modeling and Data Analysis in COVID-19 Research, 2021
Anil D. Pathak, Debasis Saran, Sibani Mishra, Madapathi Hitesh, Sivaiah Bathula, Kisor K. Sahu
According to recent medical research, the patients infected with COVID-19 have more rapid respiration and have a different respiratory pattern than patients with the flu or common cold. It has been reported that the Tachypnea type of respiratory patterns has been observed in COVID-19 patients (Williamson 2020). Based on breathing characteristics, Yunlu Wang et al. (2020) deployed a deep learning model for prognosis, diagnosis, and screening of patients infected with COVID-19. The author utilized Gated Recurrent Unit (GRU) neural network with bidirectional and attentional method to classify six types of respiratory patterns such as Eupnea, Tachypnea, Bradypnea, Biots, Cheyne-Stokes, and Central-Apnea. M. Iqbal et al. (2020) proposed a robust method of active surveillance for COVID-19 patient using AI-based speech-recognition techniques through a mobile application to analyze cough sounds of suspected people and classify them in three levels as: mild, moderate, and severe. Li Yan et al. (2020) developed XGBoost machine learning-based COVID-19 prognostic prediction model using three clinical features, i.e., lactic dehydrogenase (LDH), lymphocyte, and high-sensitivity C-reactive protein (hs-CRP). The model used a dataset of 2,799 patients with more than 90% accuracy, enabling early detection of COVID-19. The remote monitoring and diagnosis of COVID-19 patients are essential to reduce the risk of transmission and also reduce hospital resource requirements during a pandemic (HIMSS Research 2020).
The epidemiology and prehospital care of motorcycle crashes in a sub-Saharan African urban center
Published in Traffic Injury Prevention, 2020
A. Rosenberg, F. Z. Uwinshuti, M. Dworkin, V. Nsengimana, E. Kankindi, M. Niyonsaba, J. M. Uwitonze, I. Kabagema, T. Dushime, E. Krebs, S. Jayaraman
Demographics, vital signs, injury mechanism, type of injury, and treatment were analyzed for this study. Vital signs included oxygen saturation, systolic blood pressure, heart rate, and respiratory rate. Hypoxia was defined as oxygen saturation less than 90%. Tachypnea was defined as a respiratory rate greater than 20 breaths per minute. Hypotension was defined as systolic blood pressure less than 90 mmHg. Tachycardia was defined as heart rate greater than 100 beats per minute. RTCs were defined as any incident involving a road vehicle, including automobiles, motorcycles, and bicycles. Motorcycle-related RTCs were identified as any crash involving a motorized 2-wheeled vehicle. The SAMU team determined and recorded anatomic location of injury based on their clinical assessment and care during transportation. Additionally, SAMU utilizes a triage system that categorizes each case as “absolute,” “relative,” or “no urgency” based on mechanism, clinical presentation, and vital signs. Transport destinations included referral hospitals (highest level of care), district hospitals (mid-level care), and health centers (primary care). Primary transportation refers to movement of patients to the initial health facility and secondary transportation refers to transfer of patients between health facilities.