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Issues And Trends In Healthcare Delivery System

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
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COVID-19 diagnosis from routine blood tests using artificial intelligence techniques.

Samin Babaei Rikan¹, Amir Sorayaie Azar¹, Ali Ghafari²

¹Department of Computer Engineering, Urmia University, Urmia, Iran.

Biomedical Signal Processing and Control

|November 8, 2021

Summary

A deep neural network model accurately diagnosed COVID-19 using routine blood tests. This artificial intelligence tool offers fast, reliable results to aid clinicians in timely diagnosis during the pandemic.

Keywords:

Blood tests COVID-19 Deep learning Diagnosis Machine learning

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Area of Science:

Medical Informatics
Computational Biology
Machine Learning in Healthcare

Background:

The COVID-19 pandemic necessitates rapid and accurate diagnostic tools.
Emerging SARS-CoV-2 variants increase transmission rates, highlighting the need for efficient detection.
Automated diagnostic systems can significantly support clinical decision-making.

Purpose of the Study:

To develop and evaluate machine learning and deep learning models for COVID-19 diagnosis.
To assess the efficacy of using routine laboratory blood tests for automated COVID-19 detection.
To identify the most effective model for accurate and rapid COVID-19 diagnosis.

Main Methods:

Utilized three routine laboratory blood test datasets.
Implemented seven machine learning and four deep learning models.
Employed Pearson, Spearman, and Kendall correlation coefficients and four-fold cross-validation.
Applied statistical t-tests and AI interpretation for feature analysis.

Main Results:

The deep neural network (DNN) model consistently outperformed other models across all datasets.
The DNN model achieved high performance metrics, including average accuracy (up to 93.16%), specificity (up to 93.02%), and AUC (up to 93.20%).
Identified key features contributing to the DNN model's diagnostic accuracy.

Conclusions:

The proposed DNN model demonstrates significant potential as a supplementary tool for COVID-19 diagnosis.
This AI-driven approach offers clinicians a fast and highly accurate method for identifying positive COVID-19 cases.
The study validates the use of routine blood tests and AI for efficient pandemic response.