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  1. Article ; Online: A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data.

    Chieregato, Matteo / Frangiamore, Fabio / Morassi, Mauro / Baresi, Claudia / Nici, Stefania / Bassetti, Chiara / Bnà, Claudio / Galelli, Marco

    Scientific reports

    2022  Volume 12, Issue 1, Page(s) 4329

    Abstract: COVID-19 clinical presentation and prognosis are highly variable, ranging from asymptomatic and paucisymptomatic cases to acute respiratory distress syndrome and multi-organ involvement. We developed a hybrid machine learning/deep learning model to ... ...

    Abstract COVID-19 clinical presentation and prognosis are highly variable, ranging from asymptomatic and paucisymptomatic cases to acute respiratory distress syndrome and multi-organ involvement. We developed a hybrid machine learning/deep learning model to classify patients in two outcome categories, non-ICU and ICU (intensive care admission or death), using 558 patients admitted in a northern Italy hospital in February/May of 2020. A fully 3D patient-level CNN classifier on baseline CT images is used as feature extractor. Features extracted, alongside with laboratory and clinical data, are fed for selection in a Boruta algorithm with SHAP game theoretical values. A classifier is built on the reduced feature space using CatBoost gradient boosting algorithm and reaching a probabilistic AUC of 0.949 on holdout test set. The model aims to provide clinical decision support to medical doctors, with the probability score of belonging to an outcome class and with case-based SHAP interpretation of features importance.
    MeSH term(s) Algorithms ; COVID-19/diagnostic imaging ; Deep Learning ; Humans ; Machine Learning ; Tomography, X-Ray Computed/methods
    Language English
    Publishing date 2022-03-14
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-07890-1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Preliminary Analysis of Relationships between COVID19 and Climate, Morphology, and Urbanization in the Lombardy Region (Northern Italy).

    Fazzini, Massimiliano / Baresi, Claudia / Bisci, Carlo / Bna, Claudio / Cecili, Alessandro / Giuliacci, Andrea / Illuminati, Sonia / Pregliasco, Fabrizio / Miccadei, Enrico

    International journal of environmental research and public health

    2020  Volume 17, Issue 19

    Abstract: The coronavirus disease 2019 (COVID-19) pandemic is the most severe global health and socioeconomic crisis of our time, and represents the greatest challenge faced by the world since the end of the Second World War. The academic literature indicates that ...

    Abstract The coronavirus disease 2019 (COVID-19) pandemic is the most severe global health and socioeconomic crisis of our time, and represents the greatest challenge faced by the world since the end of the Second World War. The academic literature indicates that climatic features, specifically temperature and absolute humidity, are very important factors affecting infectious pulmonary disease epidemics - such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS); however, the influence of climatic parameters on COVID-19 remains extremely controversial. The goal of this study is to individuate relationships between several climate parameters (temperature, relative humidity, accumulated precipitation, solar radiation, evaporation, and wind direction and intensity), local morphological parameters, and new daily positive swabs for COVID-19, which represents the only parameter that can be statistically used to quantify the pandemic. The daily deaths parameter was not considered, because it is not reliable, due to frequent administrative errors. Daily data on meteorological conditions and new cases of COVID-19 were collected for the Lombardy Region (Northern Italy) from 1 March, 2020 to 20 April, 2020. This region exhibited the largest rate of official deaths in the world, with a value of approximately 1700 per million on 30 June 2020. Moreover, the apparent lethality was approximately 17% in this area, mainly due to the considerable housing density and the extensive presence of industrial and craft areas. Both the Mann-Kendall test and multivariate statistical analysis showed that none of the considered climatic variables exhibited statistically significant relationships with the epidemiological evolution of COVID-19, at least during spring months in temperate subcontinental climate areas, with the exception of solar radiation, which was directly related and showed an otherwise low explained variability of approximately 20%. Furthermore, the average temperatures of two highly representative meteorological stations of Molise and Lucania (Southern Italy), the most weakly affected by the pandemic, were approximately 1.5 °C lower than those in Bergamo and Brescia (Lombardy), again confirming that a significant relationship between the increase in temperature and decrease in virulence from COVID-19 is not evident, at least in Italy.
    MeSH term(s) Betacoronavirus ; COVID-19 ; Climate ; Coronavirus Infections/epidemiology ; Humans ; Italy ; Pandemics ; Pneumonia, Viral/epidemiology ; SARS-CoV-2 ; Temperature ; Urbanization
    Keywords covid19
    Language English
    Publishing date 2020-09-23
    Publishing country Switzerland
    Document type Journal Article
    ISSN 1660-4601
    ISSN (online) 1660-4601
    DOI 10.3390/ijerph17196955
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data

    Chieregato, Matteo / Frangiamore, Fabio / Morassi, Mauro / Baresi, Claudia / Nici, Stefania / Bassetti, Chiara / Bnà, Claudio / Galelli, Marco

    2021  

    Abstract: COVID-19 clinical presentation and prognosis are highly variable, ranging from asymptomatic and paucisymptomatic cases to acute respiratory distress syndrome and multi-organ involvement. We developed a hybrid machine learning/deep learning model to ... ...

    Abstract COVID-19 clinical presentation and prognosis are highly variable, ranging from asymptomatic and paucisymptomatic cases to acute respiratory distress syndrome and multi-organ involvement. We developed a hybrid machine learning/deep learning model to classify patients in two outcome categories, non-ICU and ICU (intensive care admission or death), using 558 patients admitted in a northern Italy hospital in February/May of 2020. A fully 3D patient-level CNN classifier on baseline CT images is used as feature extractor. Features extracted, alongside with laboratory and clinical data, are fed for selection in a Boruta algorithm with SHAP game theoretical values. A classifier is built on the reduced feature space using CatBoost gradient boosting algorithm and reaching a probabilistic AUC of 0.949 on holdout test set. The model aims to provide clinical decision support to medical doctors, with the probability score of belonging to an outcome class and with case-based SHAP interpretation of features importance.

    Comment: 16 pages, 10 figures, 2 supplementary tables
    Keywords Quantitative Biology - Quantitative Methods ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Image and Video Processing ; Physics - Medical Physics
    Subject code 006
    Publishing date 2021-05-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  4. Article: Preliminary Analysis of Relationships between COVID19 and Climate, Morphology, and Urbanization in the Lombardy Region (Northern Italy)

    Fazzini, Massimiliano / Baresi, Claudia / Bisci, Carlo / Bna, Claudio / Cecili, Alessandro / Giuliacci, Andrea / Illuminati, Sonia / Pregliasco, Fabrizio / Miccadei, Enrico

    International Journal of Environmental Research and Public Health

    Abstract: The coronavirus disease 2019 (COVID-19) pandemic is the most severe global health and socioeconomic crisis of our time, and represents the greatest challenge faced by the world since the end of the Second World War The academic literature indicates that ... ...

    Abstract The coronavirus disease 2019 (COVID-19) pandemic is the most severe global health and socioeconomic crisis of our time, and represents the greatest challenge faced by the world since the end of the Second World War The academic literature indicates that climatic features, specifically temperature and absolute humidity, are very important factors affecting infectious pulmonary disease epidemics - such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS);however, the influence of climatic parameters on COVID-19 remains extremely controversial The goal of this study is to individuate relationships between several climate parameters (temperature, relative humidity, accumulated precipitation, solar radiation, evaporation, and wind direction and intensity), local morphological parameters, and new daily positive swabs for COVID-19, which represents the only parameter that can be statistically used to quantify the pandemic The daily deaths parameter was not considered, because it is not reliable, due to frequent administrative errors Daily data on meteorological conditions and new cases of COVID-19 were collected for the Lombardy Region (Northern Italy) from 1 March, 2020 to 20 April, 2020 This region exhibited the largest rate of official deaths in the world, with a value of approximately 1700 per million on 30 June 2020 Moreover, the apparent lethality was approximately 17% in this area, mainly due to the considerable housing density and the extensive presence of industrial and craft areas Both the Mann–Kendall test and multivariate statistical analysis showed that none of the considered climatic variables exhibited statistically significant relationships with the epidemiological evolution of COVID-19, at least during spring months in temperate subcontinental climate areas, with the exception of solar radiation, which was directly related and showed an otherwise low explained variability of approximately 20% Furthermore, the average temperatures of two highly representative meteorological stations of Molise and Lucania (Southern Italy), the most weakly affected by the pandemic, were approximately 1 5 °C lower than those in Bergamo and Brescia (Lombardy), again confirming that a significant relationship between the increase in temperature and decrease in virulence from COVID-19 is not evident, at least in Italy
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #783854
    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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