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The research paper entitled, “COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm was recently published in the Journal of Anesthesia, Analgesia and Critical Care.

The study was coordinated by Paolo Navalesi, Director of the COVID-19 VENETO ICU Network, and Dario Gregori, Director of the Biostatistics Epidemiology and Public Health Unit of the Department of Cardiac, Thoracic, Vascular Sciences, and Public Health of the University of Padua. The research is a reflection of an investigation that predicts the mortality rate of patients in intensive care suffering from COVID-19.  The study offers insight into the importance of new techniques for analyzing clinical data through Machine Learning, one of the new frontiers of training for doctors and healthcare professionals.

Machine Learning (ML) is one of the main branches of artificial intelligence. As a fundamental form of technology for managing and understanding both healthcare and non-healthcare data. In the field of medicine, Machine Learning offers a faster and more precise identification of the mechanisms that cause disease or its degeneration and helps medical professionals to define therapies based on the personal characteristics of a patient.

The research highlights age as the leading variable for predicting ICU mortality in COVID-19 patients.

The data set gathered information from 1616 patients admitted to the intensive care units of the COVID-19 VENETO ICU Network and of the IRCCS Ca 'Granda Ospedale Maggiore Policlinico of Milan between February 28, 2020, and April 4, 2021. Since the beginning of the pandemic, the development of predictive models has aroused great interest due to the initial lack of knowledge on diagnosis, treatment and prognosis on the subject. It is worth noting, from the beginning of the pandemic, several tools were proposed for defining mortality predictions of COVID-19 patients; however, it is difficult to compare their performance because each model was developed on patients with different characteristics, using different sets of variables, and using different techniques for model development.

Using Machine Learning, the Biostatistics Unit has developed three different predictive models that include several sets of clinical variables. The three models demonstrated similar predictive performance, with age as the main predictor for all models. The tools offered several strengths, including that each was developed on a large multicenter cohort of patients admitted to intensive care units in two of the Italian regions most affected by the COVID-19 pandemic.

 

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The research paper entitled, “COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm was recently published in the Journal of Anesthesia, Analgesia and Critical Care.

The study was coordinated by Paolo Navalesi, Director of the COVID-19 VENETO ICU Network, and Dario Gregori, Director of the Biostatistics Epidemiology and Public Health Unit of the Department of Cardiac, Thoracic, Vascular Sciences, and Public Health of the University of Padua. The research is a reflection of an investigation that predicts the mortality rate of patients in intensive care suffering from COVID-19.  The study offers insight into the importance of new techniques for analyzing clinical data through Machine Learning, one of the new frontiers of training for doctors and healthcare professionals.

Machine Learning (ML) is one of the main branches of artificial intelligence. As a fundamental form of technology for managing and understanding both healthcare and non-healthcare data. In the field of medicine, Machine Learning offers a faster and more precise identification of the mechanisms that cause disease or its degeneration and helps medical professionals to define therapies based on the personal characteristics of a patient.

The research highlights age as the leading variable for predicting ICU mortality in COVID-19 patients.

The data set gathered information from 1616 patients admitted to the intensive care units of the COVID-19 VENETO ICU Network and of the IRCCS Ca 'Granda Ospedale Maggiore Policlinico of Milan between February 28, 2020, and April 4, 2021. Since the beginning of the pandemic, the development of predictive models has aroused great interest due to the initial lack of knowledge on diagnosis, treatment and prognosis on the subject. It is worth noting, from the beginning of the pandemic, several tools were proposed for defining mortality predictions of COVID-19 patients; however, it is difficult to compare their performance because each model was developed on patients with different characteristics, using different sets of variables, and using different techniques for model development.

Using Machine Learning, the Biostatistics Unit has developed three different predictive models that include several sets of clinical variables. The three models demonstrated similar predictive performance, with age as the main predictor for all models. The tools offered several strengths, including that each was developed on a large multicenter cohort of patients admitted to intensive care units in two of the Italian regions most affected by the COVID-19 pandemic.

 

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The research paper entitled, “COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm was recently published in the Journal of Anesthesia, Analgesia and Critical Care.

The study was coordinated by Paolo Navalesi, Director of the COVID-19 VENETO ICU Network, and Dario Gregori, Director of the Biostatistics Epidemiology and Public Health Unit of the Department of Cardiac, Thoracic, Vascular Sciences, and Public Health of the University of Padua. The research is a reflection of an investigation that predicts the mortality rate of patients in intensive care suffering from COVID-19.  The study offers insight into the importance of new techniques for analyzing clinical data through Machine Learning, one of the new frontiers of training for doctors and healthcare professionals.

Machine Learning (ML) is one of the main branches of artificial intelligence. As a fundamental form of technology for managing and understanding both healthcare and non-healthcare data. In the field of medicine, Machine Learning offers a faster and more precise identification of the mechanisms that cause disease or its degeneration and helps medical professionals to define therapies based on the personal characteristics of a patient.

The research highlights age as the leading variable for predicting ICU mortality in COVID-19 patients.

The data set gathered information from 1616 patients admitted to the intensive care units of the COVID-19 VENETO ICU Network and of the IRCCS Ca 'Granda Ospedale Maggiore Policlinico of Milan between February 28, 2020, and April 4, 2021. Since the beginning of the pandemic, the development of predictive models has aroused great interest due to the initial lack of knowledge on diagnosis, treatment and prognosis on the subject. It is worth noting, from the beginning of the pandemic, several tools were proposed for defining mortality predictions of COVID-19 patients; however, it is difficult to compare their performance because each model was developed on patients with different characteristics, using different sets of variables, and using different techniques for model development.

Using Machine Learning, the Biostatistics Unit has developed three different predictive models that include several sets of clinical variables. The three models demonstrated similar predictive performance, with age as the main predictor for all models. The tools offered several strengths, including that each was developed on a large multicenter cohort of patients admitted to intensive care units in two of the Italian regions most affected by the COVID-19 pandemic.

 

[summary] => [format] => 2 [safe_value] =>

The research paper entitled, “COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm was recently published in the Journal of Anesthesia, Analgesia and Critical Care.

The study was coordinated by Paolo Navalesi, Director of the COVID-19 VENETO ICU Network, and Dario Gregori, Director of the Biostatistics Epidemiology and Public Health Unit of the Department of Cardiac, Thoracic, Vascular Sciences, and Public Health of the University of Padua. The research is a reflection of an investigation that predicts the mortality rate of patients in intensive care suffering from COVID-19.  The study offers insight into the importance of new techniques for analyzing clinical data through Machine Learning, one of the new frontiers of training for doctors and healthcare professionals.

Machine Learning (ML) is one of the main branches of artificial intelligence. As a fundamental form of technology for managing and understanding both healthcare and non-healthcare data. In the field of medicine, Machine Learning offers a faster and more precise identification of the mechanisms that cause disease or its degeneration and helps medical professionals to define therapies based on the personal characteristics of a patient.

The research highlights age as the leading variable for predicting ICU mortality in COVID-19 patients.

The data set gathered information from 1616 patients admitted to the intensive care units of the COVID-19 VENETO ICU Network and of the IRCCS Ca 'Granda Ospedale Maggiore Policlinico of Milan between February 28, 2020, and April 4, 2021. Since the beginning of the pandemic, the development of predictive models has aroused great interest due to the initial lack of knowledge on diagnosis, treatment and prognosis on the subject. It is worth noting, from the beginning of the pandemic, several tools were proposed for defining mortality predictions of COVID-19 patients; however, it is difficult to compare their performance because each model was developed on patients with different characteristics, using different sets of variables, and using different techniques for model development.

Using Machine Learning, the Biostatistics Unit has developed three different predictive models that include several sets of clinical variables. The three models demonstrated similar predictive performance, with age as the main predictor for all models. The tools offered several strengths, including that each was developed on a large multicenter cohort of patients admitted to intensive care units in two of the Italian regions most affected by the COVID-19 pandemic.

 

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The research paper entitled, “COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm was recently published in the Journal of Anesthesia, Analgesia and Critical Care.

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The research paper entitled, “COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm was recently published in the Journal of Anesthesia, Analgesia and Critical Care.

The study was coordinated by Paolo Navalesi, Director of the COVID-19 VENETO ICU Network, and Dario Gregori, Director of the Biostatistics Epidemiology and Public Health Unit of the Department of Cardiac, Thoracic, Vascular Sciences, and Public Health of the University of Padua. The research is a reflection of an investigation that predicts the mortality rate of patients in intensive care suffering from COVID-19.  The study offers insight into the importance of new techniques for analyzing clinical data through Machine Learning, one of the new frontiers of training for doctors and healthcare professionals.

Machine Learning (ML) is one of the main branches of artificial intelligence. As a fundamental form of technology for managing and understanding both healthcare and non-healthcare data. In the field of medicine, Machine Learning offers a faster and more precise identification of the mechanisms that cause disease or its degeneration and helps medical professionals to define therapies based on the personal characteristics of a patient.

The research highlights age as the leading variable for predicting ICU mortality in COVID-19 patients.

The data set gathered information from 1616 patients admitted to the intensive care units of the COVID-19 VENETO ICU Network and of the IRCCS Ca 'Granda Ospedale Maggiore Policlinico of Milan between February 28, 2020, and April 4, 2021. Since the beginning of the pandemic, the development of predictive models has aroused great interest due to the initial lack of knowledge on diagnosis, treatment and prognosis on the subject. It is worth noting, from the beginning of the pandemic, several tools were proposed for defining mortality predictions of COVID-19 patients; however, it is difficult to compare their performance because each model was developed on patients with different characteristics, using different sets of variables, and using different techniques for model development.

Using Machine Learning, the Biostatistics Unit has developed three different predictive models that include several sets of clinical variables. The three models demonstrated similar predictive performance, with age as the main predictor for all models. The tools offered several strengths, including that each was developed on a large multicenter cohort of patients admitted to intensive care units in two of the Italian regions most affected by the COVID-19 pandemic.

 

[summary] => [format] => 2 [safe_value] =>

The research paper entitled, “COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm was recently published in the Journal of Anesthesia, Analgesia and Critical Care.

The study was coordinated by Paolo Navalesi, Director of the COVID-19 VENETO ICU Network, and Dario Gregori, Director of the Biostatistics Epidemiology and Public Health Unit of the Department of Cardiac, Thoracic, Vascular Sciences, and Public Health of the University of Padua. The research is a reflection of an investigation that predicts the mortality rate of patients in intensive care suffering from COVID-19.  The study offers insight into the importance of new techniques for analyzing clinical data through Machine Learning, one of the new frontiers of training for doctors and healthcare professionals.

Machine Learning (ML) is one of the main branches of artificial intelligence. As a fundamental form of technology for managing and understanding both healthcare and non-healthcare data. In the field of medicine, Machine Learning offers a faster and more precise identification of the mechanisms that cause disease or its degeneration and helps medical professionals to define therapies based on the personal characteristics of a patient.

The research highlights age as the leading variable for predicting ICU mortality in COVID-19 patients.

The data set gathered information from 1616 patients admitted to the intensive care units of the COVID-19 VENETO ICU Network and of the IRCCS Ca 'Granda Ospedale Maggiore Policlinico of Milan between February 28, 2020, and April 4, 2021. Since the beginning of the pandemic, the development of predictive models has aroused great interest due to the initial lack of knowledge on diagnosis, treatment and prognosis on the subject. It is worth noting, from the beginning of the pandemic, several tools were proposed for defining mortality predictions of COVID-19 patients; however, it is difficult to compare their performance because each model was developed on patients with different characteristics, using different sets of variables, and using different techniques for model development.

Using Machine Learning, the Biostatistics Unit has developed three different predictive models that include several sets of clinical variables. The three models demonstrated similar predictive performance, with age as the main predictor for all models. The tools offered several strengths, including that each was developed on a large multicenter cohort of patients admitted to intensive care units in two of the Italian regions most affected by the COVID-19 pandemic.

 

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The research paper entitled, “COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm was recently published in the Journal of Anesthesia, Analgesia and Critical Care.

The study was coordinated by Paolo Navalesi, Director of the COVID-19 VENETO ICU Network, and Dario Gregori, Director of the Biostatistics Epidemiology and Public Health Unit of the Department of Cardiac, Thoracic, Vascular Sciences, and Public Health of the University of Padua. The research is a reflection of an investigation that predicts the mortality rate of patients in intensive care suffering from COVID-19.  The study offers insight into the importance of new techniques for analyzing clinical data through Machine Learning, one of the new frontiers of training for doctors and healthcare professionals.

Machine Learning (ML) is one of the main branches of artificial intelligence. As a fundamental form of technology for managing and understanding both healthcare and non-healthcare data. In the field of medicine, Machine Learning offers a faster and more precise identification of the mechanisms that cause disease or its degeneration and helps medical professionals to define therapies based on the personal characteristics of a patient.

The research highlights age as the leading variable for predicting ICU mortality in COVID-19 patients.

The data set gathered information from 1616 patients admitted to the intensive care units of the COVID-19 VENETO ICU Network and of the IRCCS Ca 'Granda Ospedale Maggiore Policlinico of Milan between February 28, 2020, and April 4, 2021. Since the beginning of the pandemic, the development of predictive models has aroused great interest due to the initial lack of knowledge on diagnosis, treatment and prognosis on the subject. It is worth noting, from the beginning of the pandemic, several tools were proposed for defining mortality predictions of COVID-19 patients; however, it is difficult to compare their performance because each model was developed on patients with different characteristics, using different sets of variables, and using different techniques for model development.

Using Machine Learning, the Biostatistics Unit has developed three different predictive models that include several sets of clinical variables. The three models demonstrated similar predictive performance, with age as the main predictor for all models. The tools offered several strengths, including that each was developed on a large multicenter cohort of patients admitted to intensive care units in two of the Italian regions most affected by the COVID-19 pandemic.

 

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The research paper entitled, “COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm was recently published in the Journal of Anesthesia, Analgesia and Critical Care.

The study was coordinated by Paolo Navalesi, Director of the COVID-19 VENETO ICU Network, and Dario Gregori, Director of the Biostatistics Epidemiology and Public Health Unit of the Department of Cardiac, Thoracic, Vascular Sciences, and Public Health of the University of Padua. The research is a reflection of an investigation that predicts the mortality rate of patients in intensive care suffering from COVID-19.  The study offers insight into the importance of new techniques for analyzing clinical data through Machine Learning, one of the new frontiers of training for doctors and healthcare professionals.

Machine Learning (ML) is one of the main branches of artificial intelligence. As a fundamental form of technology for managing and understanding both healthcare and non-healthcare data. In the field of medicine, Machine Learning offers a faster and more precise identification of the mechanisms that cause disease or its degeneration and helps medical professionals to define therapies based on the personal characteristics of a patient.

The research highlights age as the leading variable for predicting ICU mortality in COVID-19 patients.

The data set gathered information from 1616 patients admitted to the intensive care units of the COVID-19 VENETO ICU Network and of the IRCCS Ca 'Granda Ospedale Maggiore Policlinico of Milan between February 28, 2020, and April 4, 2021. Since the beginning of the pandemic, the development of predictive models has aroused great interest due to the initial lack of knowledge on diagnosis, treatment and prognosis on the subject. It is worth noting, from the beginning of the pandemic, several tools were proposed for defining mortality predictions of COVID-19 patients; however, it is difficult to compare their performance because each model was developed on patients with different characteristics, using different sets of variables, and using different techniques for model development.

Using Machine Learning, the Biostatistics Unit has developed three different predictive models that include several sets of clinical variables. The three models demonstrated similar predictive performance, with age as the main predictor for all models. The tools offered several strengths, including that each was developed on a large multicenter cohort of patients admitted to intensive care units in two of the Italian regions most affected by the COVID-19 pandemic.

 

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The study was coordinated by Paolo Navalesi, Director of the COVID-19 VENETO ICU Network, and Dario Gregori, Director of the Biostatistics Epidemiology and Public Health Unit of the Department of Cardiac, Thoracic, Vascular Sciences, and Public Health of the University of Padua. [format] => [safe_value] => A machine learning approach using SuperLearner algorithms to identify the risk of death for patients hospitalized with COVID-19. 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The research paper entitled, “COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm was recently published in the Journal of Anesthesia, Analgesia and Critical Care.

The study was coordinated by Paolo Navalesi, Director of the COVID-19 VENETO ICU Network, and Dario Gregori, Director of the Biostatistics Epidemiology and Public Health Unit of the Department of Cardiac, Thoracic, Vascular Sciences, and Public Health of the University of Padua. The research is a reflection of an investigation that predicts the mortality rate of patients in intensive care suffering from COVID-19.  The study offers insight into the importance of new techniques for analyzing clinical data through Machine Learning, one of the new frontiers of training for doctors and healthcare professionals.

Machine Learning (ML) is one of the main branches of artificial intelligence. As a fundamental form of technology for managing and understanding both healthcare and non-healthcare data. In the field of medicine, Machine Learning offers a faster and more precise identification of the mechanisms that cause disease or its degeneration and helps medical professionals to define therapies based on the personal characteristics of a patient.

The research highlights age as the leading variable for predicting ICU mortality in COVID-19 patients.

The data set gathered information from 1616 patients admitted to the intensive care units of the COVID-19 VENETO ICU Network and of the IRCCS Ca 'Granda Ospedale Maggiore Policlinico of Milan between February 28, 2020, and April 4, 2021. Since the beginning of the pandemic, the development of predictive models has aroused great interest due to the initial lack of knowledge on diagnosis, treatment and prognosis on the subject. It is worth noting, from the beginning of the pandemic, several tools were proposed for defining mortality predictions of COVID-19 patients; however, it is difficult to compare their performance because each model was developed on patients with different characteristics, using different sets of variables, and using different techniques for model development.

Using Machine Learning, the Biostatistics Unit has developed three different predictive models that include several sets of clinical variables. The three models demonstrated similar predictive performance, with age as the main predictor for all models. The tools offered several strengths, including that each was developed on a large multicenter cohort of patients admitted to intensive care units in two of the Italian regions most affected by the COVID-19 pandemic.

 

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The research paper entitled, “COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm was recently published in the Journal of Anesthesia, Analgesia and Critical Care.

The study was coordinated by Paolo Navalesi, Director of the COVID-19 VENETO ICU Network, and Dario Gregori, Director of the Biostatistics Epidemiology and Public Health Unit of the Department of Cardiac, Thoracic, Vascular Sciences, and Public Health of the University of Padua. The research is a reflection of an investigation that predicts the mortality rate of patients in intensive care suffering from COVID-19.  The study offers insight into the importance of new techniques for analyzing clinical data through Machine Learning, one of the new frontiers of training for doctors and healthcare professionals.

Machine Learning (ML) is one of the main branches of artificial intelligence. As a fundamental form of technology for managing and understanding both healthcare and non-healthcare data. In the field of medicine, Machine Learning offers a faster and more precise identification of the mechanisms that cause disease or its degeneration and helps medical professionals to define therapies based on the personal characteristics of a patient.

The research highlights age as the leading variable for predicting ICU mortality in COVID-19 patients.

The data set gathered information from 1616 patients admitted to the intensive care units of the COVID-19 VENETO ICU Network and of the IRCCS Ca 'Granda Ospedale Maggiore Policlinico of Milan between February 28, 2020, and April 4, 2021. Since the beginning of the pandemic, the development of predictive models has aroused great interest due to the initial lack of knowledge on diagnosis, treatment and prognosis on the subject. It is worth noting, from the beginning of the pandemic, several tools were proposed for defining mortality predictions of COVID-19 patients; however, it is difficult to compare their performance because each model was developed on patients with different characteristics, using different sets of variables, and using different techniques for model development.

Using Machine Learning, the Biostatistics Unit has developed three different predictive models that include several sets of clinical variables. The three models demonstrated similar predictive performance, with age as the main predictor for all models. The tools offered several strengths, including that each was developed on a large multicenter cohort of patients admitted to intensive care units in two of the Italian regions most affected by the COVID-19 pandemic.

 

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