Identification of high-risk COVID-19 patients using machine learning.
Mario A. Quiroz-Juárez,Armando Torres-Gomez,Irma Hoyo-Ulloa,Roberto de J. León-Montiel,Alfred B. U'Ren +4 more
TL;DR: In this paper, a machine-learning algorithm capable of identifying whether a given patient (actually infected or suspected to be infected) is more likely to survive than to die, or vice-versa, is presented.
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Abstract: The current COVID-19 public health crisis, caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has produced a devastating toll both in terms of human life loss and economic disruption. In this paper we present a machine-learning algorithm capable of identifying whether a given patient (actually infected or suspected to be infected) is more likely to survive than to die, or vice-versa. We train this algorithm with historical data, including medical history, demographic data, as well as COVID-19-related information. This is extracted from a database of confirmed and suspected COVID-19 infections in Mexico, constituting the official COVID-19 data compiled and made publicly available by the Mexican Federal Government. We demonstrate that the proposed method can detect high-risk patients with high accuracy, in each of four identified clinical stages, thus improving hospital capacity planning and timely treatment. Furthermore, we show that our method can be extended to provide optimal estimators for hypothesis-testing techniques commonly-used in biological and medical statistics. We believe that our work could be of use in the context of the current pandemic in assisting medical professionals with real-time assessments so as to determine health care priorities.
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Long-COVID diagnosis: From diagnostic to advanced AI-driven models
Riccardo Cau,Gavino Faa,Valentina Nardi,Antonella Balestrieri,Josep Puig,Jasjit S. Suri,Roberto Sanfilippo,Luca Saba +7 more
TL;DR: In this article , the authors reviewed different aspects of long COVID-19 syndrome from clinical presentation to diagnosis, highlighting the considerable impact that AI can have on the care and rehabilitation unit.
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Conventional and Novel Diagnostic Tools for the Diagnosis of Emerging SARS-CoV-2 Variants
Vivek P. Chavda,Disha Valu,Palak K. Parikh,Nikita Tiwari,Abu Sufiyan Chhipa,Somanshi Shukla,Snehal S. Patel,Pankti C. Balar,Ana Cláudia Paiva-Santos,Vandana B. Patravale +9 more
TL;DR: In this article , a review of available molecular diagnostic techniques and their pitfalls in detecting emerging VOCs of SARS-CoV-2, and lastly, we have discussed AI-ML- and nanotechnology-based smart diagnostic techniques for SARS CoV2 detection.
An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works
Ercan Gürsoy,Yasin Kaya +1 more
TL;DR: In this article , a review examines the applications of deep learning (DL) and machine learning (ML) in detecting COVID-19 using medical data such as CT scans, X-rays, cough sounds, MRIs, ultrasound, and clinical markers.
COVID-19 detection based on self-supervised transfer learning using chest X-ray images
TL;DR: Li et al. as discussed by the authors proposed a self-supervised transfer learning (SSL) method for detecting COVID-19 from chest X-ray (CXR) images, which achieved a harmonic mean (HM) score of 0.985, AUC of 0.,999, and four-class accuracy of 0,953.
Clinical Profiles at the Time of Diagnosis of SARS-CoV-2 Infection in Costa Rica During the Pre-vaccination Period Using a Machine Learning Approach
Jose Arturo Molina-Mora,Alejandra Salazar Gonzalez,Sergio Jimenez-Morgan,E Cordero-Laurent,Hebleen Brenes,Claudio Soto-Garita,Jorge Sequeira-Soto,F. Duarte-Martínez +7 more
TL;DR: In this paper , the authors implemented a symptom-based clustering using machine learning to identify clusters or clinical profiles at the population level among 18,974 records of positive SARS-CoV-2 cases.
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