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Deep-Risk: Deep Learning-Based Mortality Risk Predictive Models for COVID-19.

Nada M Elshennawy1, Dina M Ibrahim1,2, Amany M Sarhan1

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Summary
This summary is machine-generated.

This study introduces three deep learning models for predicting COVID-19 patient mortality and severity. The IMG-CNN model, utilizing image-converted clinical data, achieved the highest accuracy at 94.14%, outperforming other methods.

Keywords:
COVID-19 detectiondeep learningmachine learningmortality and severity risk

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Computational Biology

Background:

  • The global spread of SARS-CoV-2 necessitates early diagnosis and risk assessment for COVID-19 patients.
  • Identifying mortality risk factors is crucial for managing severe cases and reducing fatalities.
  • Existing machine learning (ML) and deep learning (DL) approaches have shown limitations in predicting COVID-19 severity and mortality accurately.

Purpose of the Study:

  • To develop and evaluate novel deep learning models for predicting COVID-19 patient mortality risk and disease severity.
  • To compare the performance of different deep learning architectures, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks.
  • To investigate the efficacy of using image-converted clinical data for predictive modeling.

Main Methods:

  • Three supervised deep learning models were developed: CV-CNN (CNN), CV-LSTM + CNN (LSTM + CNN), and IMG-CNN (CNN with image-converted data).
  • Models were trained and validated using a clinical dataset comprising 12,020 COVID-19 patients, employing a 10-fold cross-validation approach.
  • The IMG-CNN model processed clinical data converted into an image format, with each image representing a patient record.

Main Results:

  • The IMG-CNN model demonstrated superior performance compared to CV-CNN and CV-LSTM + CNN.
  • IMG-CNN achieved an average accuracy of 94.14%, with 100% precision and specificity, 91.0% recall, and an F1-score of 95.3%.
  • The Area Under the Curve (AUC) for IMG-CNN was 93.6%, with a loss of 0.22, indicating robust predictive capability.

Conclusions:

  • Deep learning models, particularly CNNs applied to image-converted clinical data, show significant promise for predicting COVID-19 mortality and severity.
  • The IMG-CNN approach offers a highly accurate and effective method for analyzing clinical data to identify high-risk COVID-19 patients.
  • Further research into DL applications can enhance early diagnosis and patient management strategies for infectious diseases like COVID-19.