Working together against Corona with Intensive Care Data

  • Funded by Netherlands Organisation for Health Research and Development (ZonMW)
  • Total publications:10 publications

Grant number: 10430012010003

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2020
    2023
  • Funder

    Netherlands Organisation for Health Research and Development (ZonMW)
  • Principal Investigator

    Dr. PWG Elbers
  • Research Location

    N/A
  • Lead Research Institution

    Amsterdam UMC Locatie VUmc
  • Research Priority Alignment

    N/A
  • Research Category

    Clinical characterisation and management
  • Research Subcategory

    Prognostic factors for disease severity
  • Special Interest Tags

    N/A
  • Study Type

    Clinical
  • Clinical Trial Details

    Not applicable
  • Broad Policy Alignment

    Pending
  • Age Group

    Unspecified
  • Vulnerable Population

    Unspecified
  • Occupations of Interest

    Unspecified

Abstract

Treatment in intensive care appears to be necessary for many COVID-19 patients. It is very important to gain more insight into the disease course of intensive care patients with COVID-19 and the factors that influence this. This can provide direction for the treatment of future patients, help with the choice of who should and should not be treated in intensive care and contribute to future capacity planning. Research The project investigates which combination of treatments and properties of which individual intensive care patients with COVID-19 leads to the best outcomes. This is done using machine learning, an important form of artificial intelligence, which is applied to large amounts of data from intensive care patients with COVID-19 from around 50 collaborating hospitals. The project is supported by the Dutch Association for Intensive Care and the National Intensive Care Evaluation Foundation.

10 Publications linked via Europe PMC

Reinforcement learning for intensive care medicine: actionable clinical insights from novel approaches to reward shaping and off-policy model evaluation.

Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning.

Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis

Ethnic Differences in Coronavirus Disease 2019 Hospitalization and Hospital Outcomes in a Multiethnic Population in the Netherlands.

Some Patients Are More Equal Than Others: Variation in Ventilator Settings for Coronavirus Disease 2019 Acute Respiratory Distress Syndrome.

Systematic Review and Comparison of Publicly Available ICU Data Sets-A Decision Guide for Clinicians and Data Scientists.

Predictors for extubation failure in COVID-19 patients using a machine learning approach.

Large-scale ICU data sharing for global collaboration: the first 1633 critically ill COVID-19 patients in the Dutch Data Warehouse.

The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients.