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Eliminating Indefiniteness of Clinical Spectrum for Better Screening COVID-19
Insights
A novel Indefiniteness Elimination Network (IE-Net) effectively screens for coronavirus disease 2019 (COVID-19) using common clinical data. This method overcomes challenges with varied diagnostic information, achieving high accuracy in identifying COVID-19 cases.
Area of Science:
- Medical Informatics
- Machine Learning in Healthcare
- Infectious Disease Diagnostics
Background:
- Coronavirus disease 2019 (COVID-19) diagnosis is often limited by access to advanced testing like RT-PCR or CT scans, particularly in developing nations.
- Suspected COVID-19 cases frequently rely on common clinical diagnoses, which present challenges due to highly variable and indefinite data dimensions.
- Classical classification algorithms struggle with processing such heterogeneous clinical data for accurate COVID-19 screening.
Purpose of the Study:
- To develop a rapid and effective screening method for COVID-19 utilizing only common clinical diagnosis results.
- To address the challenge of processing indefinite dimension clinical data for COVID-19 classification.
- To propose and evaluate an Indefiniteness Elimination Network (IE-Net) for improved COVID-19 case prediction.
Main Methods:
- Development of an Indefiniteness Elimination Network (IE-Net) based on an encoder-decoder framework.
- Introduction of an indefiniteness elimination operation to convert variable-dimension features into fixed-dimension features.
- Comprehensive experiments conducted on the public COVID-19 Clinical Spectrum dataset, comparing IE-Net with random forest, gradient boosting, and MLP.
Main Results:
- The IE-Net achieved high performance in distinguishing COVID-19 from non-COVID-19 cases, with 94.80% accuracy, 92.79% recall, 92.97% precision, and 94.93% AUC.
- The proposed indefiniteness elimination operation significantly enhanced classification performance compared to classical algorithms.
- Analysis explored the specificity of individual clinical test items and their relationship with COVID-19.
Conclusions:
- The IE-Net provides a robust and accurate method for COVID-19 screening using readily available clinical data, overcoming limitations of traditional diagnostic approaches.
- The indefiniteness elimination operation is crucial for handling variable clinical data dimensions, improving diagnostic model efficacy.
- This approach offers a valuable tool for rapid COVID-19 screening, especially in resource-limited settings.
Abstract:
The coronavirus disease 2019 (COVID-19) has swept all over the world. Due to the limited detection facilities, especially in developing countries, a large number of suspected cases can only receive common clinical diagnosis rather than more effective detections like Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests or CT scans. This motivates us to develop a quick screening method via common clinical diagnosis results. However, the diagnostic items of different patients may vary greatly, and there is a huge variation in the dimension of the diagnosis data among different suspected patients, it is hard to process these indefinite dimension data via classical classification algorithms. To resolve this problem, we propose an Indefiniteness Elimination Network (IE-Net) to eliminate the influence of the varied dimensions and make predictions about the COVID-19 cases. The IE-Net is in an encoder-decoder framework fashion, and an indefiniteness elimination operation is proposed to transfer the indefinite dimension feature into a fixed dimension feature. Comprehensive experiments were conducted on the public available COVID-19 Clinical Spectrum dataset. Experimental results show that the proposed indefiniteness elimination operation greatly improves the classification performance, the IE-Net achieves 94.80% accuracy, 92.79% recall, 92.97% precision and 94.93% AUC for distinguishing COVID-19 cases from non-COVID-19 cases with only common clinical diagnose data. We further compared our methods with 3 classical classification algorithms: random forest, gradient boosting and multi-layer perceptron (MLP). To explore each clinical test item's specificity, we further analyzed the possible relationship between each clinical test item and COVID-19.
