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Related Experiment Video

Updated: Sep 28, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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Prognosis patients with COVID-19 using deep learning.

José Luis Guadiana-Alvarez1, Fida Hussain2, Ruben Morales-Menendez1

  • 1Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Tecnológico, 64849, Monterrey, N.L., Mexico.

BMC Medical Informatics and Decision Making
|March 29, 2022
PubMed
Summary
This summary is machine-generated.

This study developed a deep learning model to predict COVID-19 patient mortality risk, offering a cost-effective tool for hospitals. The model achieved high accuracy, aiding in better patient management and resource allocation.

Keywords:
COVID-19CoronavirusDeep learningMortality risk predictionPrognosisRandom forest

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Epidemiology

Background:

  • COVID-19 pandemic highlights the need for effective mortality risk assessment.
  • Current methods may be costly or inaccessible for some hospitals.
  • Accurate prediction of mortality risk in COVID-19 patients is crucial for hospital management.

Purpose of the Study:

  • To develop a COVID-19 mortality risk calculator using a deep learning model.
  • To address challenges of data imbalance and missing biomarker data.
  • To provide an accessible tool for assessing mortality risk in critically ill patients.

Main Methods:

  • A deep learning (DL) model was developed using patient data from HM Hospitals Madrid.
  • Pre-processing strategies included handling unbalanced classes and feature selection.
  • Synthetic Minority TEchnique (SMOTE) and K-nearest neighbour imputation were used for data evaluation.

Main Results:

  • The model achieved an Area Under the Curve (AUC) of 0.93 and an accuracy of 0.95.
  • High recall (1.00) and precision (0.91) were reported.
  • The deep learning model demonstrated superior performance, even with over-sampling techniques.

Conclusions:

  • The proposed deep learning model is effective for COVID-19 mortality risk prediction.
  • The tool can assist hospitals in managing critically ill patients and allocating resources.
  • The method offers a valuable approach for assessing prognosis in COVID-19 patients.