LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 2 of total 2

Search options

  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

    More links

    Kategorien

  2. 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)

    More links

    Kategorien

To top