Modelos de Previsão de Desenvolvimento da COVID-19 em Doentes de Risco para uma Medicina de Precisão / Predictive Models of COVID-19 Outcomes for Higher Risk Patients Towards a Precision Medicine

Grant number: DSAIPA/DS/0117/2020

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Key facts

  • Disease

    COVID-19
  • Start & end year

    N/A
  • Known Financial Commitments (USD)

    $0
  • Funder

    FCT Portugal
  • Principal Investigator

    Luís Filipe Nunes Bento
  • Research Location

    Portugal
  • Lead Research Institution

    Centro Hospitalar Universitário de Lisboa Central, EPE (CHULC)
  • 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

    Unspecified
  • Clinical Trial Details

    N/A
  • Broad Policy Alignment

    Pending
  • Age Group

    Unspecified
  • Vulnerable Population

    Unspecified
  • Occupations of Interest

    Unspecified

Abstract

20 Publications linked via Europe PMC

Metabolomic Signatures of Biotrauma Associated with Mortality in ICU Patients Requiring Invasive Mechanical Ventilation and ECMO.

Predicting delirium in critically Ill COVID-19 patients using EEG-derived data: a machine learning approach.

Analyzing Heart Rate Variability for COVID-19 ICU Mortality Prediction Using Continuous Signal Processing Techniques.

Multiplex Targeted Proteomic Analysis of Cytokine Ratios for ICU Mortality in Severe COVID-19.

Predicting ICU Delirium in Critically Ill COVID-19 Patients Using Demographic, Clinical, and Laboratory Admission Data: A Machine Learning Approach.

Cytokine-Based Insights into Bloodstream Infections and Bacterial Gram Typing in ICU COVID-19 Patients.

Early Mortality Prediction in Intensive Care Unit Patients Based on Serum Metabolomic Fingerprint.

Integration of FTIR Spectroscopy and Machine Learning for Kidney Allograft Rejection: A Complementary Diagnostic Tool.

Analysis of six consecutive waves of ICU-admitted COVID-19 patients: key findings and insights from a Portuguese population.