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Prognosis Prediction in COVID-19 Patients through Deep Feature Space Reasoning.

Jamil Ahmad1, Abdul Khader Jilani Saudagar2, Khalid Mahmood Malik3

  • 1Department of Computer Science, Islamia College Peshawar, Peshawar 25120, Pakistan.

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Summary

This study introduces a novel framework for predicting COVID-19 progression and prognosis using chest X-rays (CXR) and deep learning. The method enhances diagnostic accuracy with limited data, aiding clinical decision-making.

Keywords:
COVID-19case retrievaldeep feature space reasoningpatient demographicsprognosis prediction

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Infectious Disease Modeling

Background:

  • The COVID-19 pandemic created challenges for physicians due to limited diagnostic and prognostic data.
  • There is a critical need for innovative methods to support informed decisions with minimal patient information.

Purpose of the Study:

  • To develop a comprehensive framework for predicting COVID-19 progression and prognosis using chest X-rays (CXR).
  • To enable accurate predictions even with limited available patient data.

Main Methods:

  • Utilized a pre-trained deep learning model fine-tuned on COVID-19 CXRs to identify infection-sensitive features.
  • Employed a neuronal attention mechanism to create a COVID-specific deep feature space.
  • Integrated clinical attributes (age, comorbidities) and employed Dempster-Shafer theory for evidence-based reasoning.

Main Results:

  • The framework accurately retrieves relevant cases from electronic health records (EHRs) based on visual and clinical similarity.
  • Achieved 88% precision, 79% recall, and 83.7% F-score in predicting COVID-19 severity, progression, and prognosis on test datasets.

Conclusions:

  • The proposed deep learning framework effectively predicts COVID-19 outcomes using chest X-rays and clinical data.
  • This approach offers a valuable tool for aiding clinical decisions in resource-limited or uncertain pandemic scenarios.