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  1. Article ; Online: Covid-19 rapid test by combining a Random Forest-based web system and blood tests.

    Barbosa, Valter Augusto de Freitas / Gomes, Juliana Carneiro / de Santana, Maíra Araújo / de Lima, Clarisse Lins / Calado, Raquel Bezerra / Bertoldo Júnior, Cláudio Roberto / Albuquerque, Jeniffer Emidio de Almeida / de Souza, Rodrigo Gomes / de Araújo, Ricardo Juarez Escorel / Mattos Júnior, Luiz Alberto Reis / de Souza, Ricardo Emmanuel / Dos Santos, Wellington Pinheiro

    Journal of biomolecular structure & dynamics

    2021  Volume 40, Issue 22, Page(s) 11948–11967

    Abstract: The disease caused by the new type of coronavirus, Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-CoV-2 has already ... ...

    Abstract The disease caused by the new type of coronavirus, Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-CoV-2 has already caused over 2 million deaths to date. In this work, we propose a web solution, called Heg.IA, to optimize the diagnosis of Covid-19 through the use of artificial intelligence. Our system aims to support decision-making regarding to diagnosis of Covid-19 and to the indication of hospitalization on regular ward, semi-ICU or ICU based on decision a Random Forest architecture with 90 trees. The main idea is that healthcare professionals can insert 41 hematological parameters from common blood tests and arterial gasometry into the system. Then, Heg.IA will provide a diagnostic report. The system reached good results for both Covid-19 diagnosis and to recommend hospitalization. For the first scenario we found average results of accuracy of 92.891%±0.851, kappa index of 0.858 ± 0.017, sensitivity of 0.936 ± 0.011, precision of 0.923 ± 0.011, specificity of 0.921 ± 0.012 and area under ROC of 0.984 ± 0.003. As for the indication of hospitalization, we achieved excellent performance of accuracies above 99% and more than 0.99 for the other metrics in all situations. By using a computationally simple method, based on the classical decision trees, we were able to achieve high diagnosis performance. Heg.IA system may be a way to overcome the testing unavailability in the context of Covid-19.Communicated by Ramaswamy H. Sarma.
    MeSH term(s) Humans ; COVID-19/diagnosis ; SARS-CoV-2 ; COVID-19 Testing ; Random Forest ; Artificial Intelligence ; Hematologic Tests
    Language English
    Publishing date 2021-08-31
    Publishing country England
    Document type Journal Article
    ZDB-ID 49157-3
    ISSN 1538-0254 ; 0739-1102
    ISSN (online) 1538-0254
    ISSN 0739-1102
    DOI 10.1080/07391102.2021.1966509
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Covid-19 rapid test by combining a random forest based web system and blood tests

    Barbosa, Valter Augusto de Freitas / Gomes, Juliana Carneiro / de Santana, Maira Araujo / de Lima, Clarisse Lins / Calado, Raquel Bezerra / Bertoldo Junior, Claudio Roberto / Albuquerque, Jeniffer Emidio de Almeida / de Souza, Rodrigo Gomes / de Araujo, Ricardo Juarez Escorel / de Souza, Ricardo Emmanuel / dos Santos, Wellington Pinheiro

    medRxiv

    Abstract: The disease caused by the new type of coronavirus, the Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-Cov2 has already ... ...

    Abstract The disease caused by the new type of coronavirus, the Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-Cov2 has already caused over 400 thousand deaths to date. The diagnosis of the disease has an important role in combating Covid-19. Proposed method In this work, we propose a web system, Heg.IA, which seeks to optimize the diagnosis of Covid-19 through the use of artificial intelligence. The main ideia is that healthcare professionals can insert 41 hematological parameters from common blood tests and arterial gasometry into the system. Then, Heg.IA will provide a diagnostic report. It will indicate if the patient is infected with SARS-Cov2 virus, and also predict the type of hospitalization (regular ward, semi-ICU, or ICU). We developed a web system called Heg.IA to support decision-making regarding to diagnosis of Covid-19 and to the indication of hospitalization on regular ward, semi-ICU or ICU. This application is based on decision trees in a Random Forest architecture with 90 trees. The system showed to be highly efficient, with great results for both Covid-19 diagnosis and to recommend hospitalization. For the first scenario we found average results of accuracy of 92.891% ± 0.851, kappa index of 0.858 ± 0.017, sensitivity of 0.936 ± 0.011, precision of 0.923 ± 0.011, specificity of 0.921 ± 0.012 and area under ROC of 0.984 ± 0.003. As for the indication of hospitalization, we achieved excellent performance of accuracies above 99% and more than 0.99 for the other metrics in all situations. By using a computationally simple method, based on the classical decision trees, we were able to achieve high diagnosis performance. Heg.IA system may be a way to overcome the testing unavailability in the context of Covid-19. We also expect the system will provide wide access to Covid-19 effective diagnosis and thereby reach and help saving lives.
    Keywords covid19
    Language English
    Publishing date 2020-06-16
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2020.06.12.20129866
    Database COVID19

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  3. Article ; Online: Rapid protocols to support Covid-19 clinical diagnosis based on hematological parameters

    Gomes, Juliana Carneiro / Barbosa, Valter Augusto de Freitas / de Santana, Maira Araujo / de Lima, Clarisse Lins / Calado, Raquel Bezerra / Bertoldo Junior, Claudio Roberto / Albuquerque, Jeniffer Emidio de Almeida / de Souza, Rodrigo Gomes / de Araujo, Ricardo Juarez Escorel / Moreno, Giselle Machado Magalhaes / Soares, Luiz Alberto Lira / Mattos Junior, Luiz Alberto Reis / de Souza, Ricardo Emmanuel / dos Santos, Wellington Pinheiro

    medRxiv

    Abstract: Purpose In December 2019, the Covid-19 pandemic began in the world. To reduce mortality, in addiction to mass vaccination, it is necessary to massify and accelerate clinical diagnosis, as well as creating new ways of monitoring patients that can help in ... ...

    Abstract Purpose In December 2019, the Covid-19 pandemic began in the world. To reduce mortality, in addiction to mass vaccination, it is necessary to massify and accelerate clinical diagnosis, as well as creating new ways of monitoring patients that can help in the construction of specific treatments for the disease. Objective In this work, we propose rapid protocols for clinical diagnosis of Covid-19 through the automatic analysis of hematological parameters using Evolutionary Computing and Machine Learning. These hematological parameters are obtained from blood tests common in clinical practice. Method We investigated the best classifier architectures. Then, we applied the particle swarm optimization algorithm (PSO) to select the most relevant attributes: serum glucose, troponin, partial thromboplastin time, ferritin, D-dimer, lactic dehydrogenase, and indirect bilirubin. Finally, we used decision trees to build four rapid protocols for Covid-19 clinical diagnosis. Results We developed a web system for Covid-19 diagnosis support. Using a 100-tree Random Forest, we obtained results for accuracy, sensitivity and specificity superior to 99%. Conclusion By using a reduced set of hematological parameters common in clinical practice, it was possible to achieve results of accuracy, sensitivity and specificity comparable to those obtained with RT-PCR. It was also possible to automatically generate clinical decision protocols, allowing relatively accurate clinical diagnosis even without the aid of the web decision support system.
    Keywords covid19
    Language English
    Publishing date 2021-06-28
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2021.06.21.21259252
    Database COVID19

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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