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Research Theme

Modernising Medical Microbiology and Big Infection Diagnostics

Home > Research Themes Overview > Modernising Medical Microbiology and Big Infection Diagnostics > Infections in Oxfordshire Research Database > IORD Projects > Using Machine Learning to Predict Mortality for COVID-19 Patients on Day Zero in the ICU

IORD Project

Using Machine Learning to Predict Mortality for COVID-19 Patients on Day Zero in the ICU

ONGOING
IORD category: COVID-19
Chief Investigator: Dr Nahal Mansouri
Sponsor: Lausanne University Hospital
Research location: Lausanne University Hospital
Approval date: 28 May 2021

Using a dataset of 270 critically ill patients with COVID-19 we have identified a set of variables that predict the mortality for patients on the day of admission to the ICU. The use of this practical prediction model should affect clinical decision-making and medical management of COVID-19 patients.

  1. We can predict the chance of survival based on values measured on the day of arrival in the ICU. This timely prediction should improve the treatment strategies and outcome of patients.
  2. There is a paucity of ICU severity predictor scales overall. No risk scales are yet available for the general prediction of mortality in severe COVID-19 patients.
  3. Using the local interpretable model-agnostic explanations (LIME) model we are able to indicate which specific clinical condition is responsible for the prediction. Thus, physicians can apply this machine learning model to individual patients and can tailor treatments toward medical conditions knowing the key variables underlying the prognosis.

In order to validate our model we will require a similar number of datapoints (about 300 patients) to externally confirm the prediction model. The preprint of our work can be found under:

https://www.medrxiv.org/content/10.1101/2021.02.04.21251131v1

Modernising Medical Microbiology and Big Infection Diagnostics

  • Modernising Medical Microbiology and Big Infection Diagnostics
  • Sub-theme 1: Novel rapid, high-throughput diagnostic workflows for infection
  • Sub-theme 2: Big data-led infection diagnosis and management strategies
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