Harikrishnan, N. B. Pranay, S. Y. Nagaraj, Nithin
Published in
Medical & Biological Engineering & Computing
Abstract The high spread rate of SARS-CoV-2 virus has put the researchers all over the world in a demanding situation. The need of the hour is to develop novel learning algorithms that can effectively learn a general pattern by training with fewer genome sequences of coronavirus. Learning from very few training samples is necessary and important du...
Adeoye, Elijah A. Rozenfeld, Yelena Beam, Jennifer Boudreau, Karen Cox, Emily J. Scanlan, James M.
Published in
Medical & Biological Engineering & Computing
Notable discrepancies in vulnerability to COVID-19 infection have been identified between specific population groups and regions in the USA. The purpose of this study was to estimate the likelihood of COVID-19 infection using a machine-learning algorithm that can be updated continuously based on health care data. Patient records were extracted for ...
Ali, Sarwan Zhou, Yijing Patterson, Murray
Published in
Medical & Biological Engineering & Computing
Because of the rapid spread of COVID-19 to almost every part of the globe, huge volumes of data and case studies have been made available, providing researchers with a unique opportunity to find trends and make discoveries like never before by leveraging such big data. This data is of many different varieties and can be of different levels of verac...
Lyra, Simon Rixen, Jöran Heimann, Konrad Karthik, Srinivasa Joseph, Jayaraj Jayaraman, Kumutha Orlikowsky, Thorsten Sivaprakasam, Mohanasankar Leonhardt, Steffen Hoog Antink, Christoph
...
Published in
Medical & Biological Engineering & Computing
Abstract The continuous monitoring of vital signs is a crucial aspect of medical care in neonatal intensive care units. Since cable-based sensors pose a potential risk for the immature skin of preterm infants, unobtrusive monitoring techniques using camera systems are increasingly investigated. The combination of deep learning–based algorithms and ...
Deng, Xinlei Li, Han Liao, Xin Qin, Zhiqiang Xu, Fan Friedman, Samantha Ma, Gang Ye, Kun Lin, Shao
Published in
Medical & Biological Engineering & Computing
Although some studies tried to identify risk factors for COVID-19, the evidence comparing COVID-19 and community-acquired pneumonia (CAP) is inconclusive, and CAP is the most common pneumonia with similar symptoms as COVID-19. We conducted a case–control study with 35 routine-collected clinical indicators and demographic factors to identify predict...
Piatkowska, Weronika Spolaor, Fabiola Guiotto, Annamaria Guarneri, Gabriella Avogaro, Angelo Sawacha, Zimi
Published in
Medical & Biological Engineering & Computing
The aim of this work was twofold: on one side to determine the most suitable parameters of surface electromyography (sEMG) to classify diabetic subjects with and without neuropathy and discriminate them from healthy controls and second to assess the role of the task acquired in the classification process. For this purpose 30 subjects were examined ...
Excoffier, Jean-Baptiste Salaün-Penquer, Noémie Ortala, Matthieu Raphaël-Rousseau, Mathilde Chouaid, Christos Jung, Camille
Published in
Medical & Biological Engineering & Computing
The COVID-19 pandemic rapidly puts a heavy pressure on hospital centers, especially on intensive care units. There was an urgent need for tools to understand typology of COVID-19 patients and identify those most at risk of aggravation during their hospital stay. Data included more than 400 patients hospitalized due to COVID-19 during the first wave...
Kamphuis, Marije E. de Vries, Gijs J. Kuipers, Henny Saaltink, Marloes Verschoor, Jacqueline Greuter, Marcel J. W. Slart, Riemer H. J. A. Slump, Cornelis H.
Published in
Medical & Biological Engineering & Computing
Kiziloluk, Soner Sert, Eser
Published in
Medical & biological engineering & computing
Coronavirus disease-2019 (COVID-19) is a new types of coronavirus which have turned into a pandemic within a short time. Reverse transcription-polymerase chain reaction (RT-PCR) test is used for the diagnosis of COVID-19 in national healthcare centers. Because the number of PCR test kits is often limited, it is sometimes difficult to diagnose the d...
Famiglini, Lorenzo Campagner, Andrea Carobene, Anna Cabitza, Federico
Published in
Medical & Biological Engineering & Computing
Abstract In this article, we discuss the development of prognostic machine learning (ML) models for COVID-19 progression, by focusing on the task of predicting ICU admission within (any of) the next 5 days. On the basis of 6,625 complete blood count (CBC) tests from 1,004 patients, of which 18% were admitted to intensive care unit (ICU), we created...
