Automatic Classification Between COVID-19 and Non-COVID-19 Pneumonia Using Symptoms, Comorbidities, and Laboratory Findings: The Khorshid COVID Cohort Study
Notice bibliographique
Résumé
Coronavirus disease-2019, also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was a disaster in 2020. Accurate and early diagnosis of coronavirus disease-2019 (COVID-19) is still essential for health policymaking. Reverse transcriptase-polymerase chain reaction (RT-PCR) has been performed as the operational gold standard for COVID-19 diagnosis. We aimed to design and implement a reliable COVID-19 diagnosis method to provide the risk of infection using demographics, symptoms and signs, blood markers, and family history of diseases to have excellent agreement with the results obtained by the RT-PCR and CT-scan. Our study primarily used sample data from a 1-year hospital-based prospective COVID-19 open-cohort, the Khorshid COVID Cohort (KCC) study. A sample of 634 patients with COVID-19 and 118 patients with pneumonia with similar characteristics whose RT-PCR and chest CT scan were negative (as the control group) (dataset 1) was used to design the system and for internal validation. Two other online datasets, namely, some symptoms (dataset 2) and blood tests (dataset 3), were also analyzed. A combination of one-hot encoding, stability feature selection, over-sampling, and an ensemble classifier was used. Ten-fold stratified cross-validation was performed. In addition to gender and symptom duration, signs and symptoms, blood biomarkers, and comorbidities were selected. Performance indices of the cross-validated confusion matrix for dataset 1 were as follows: sensitivity of 96% [confidence interval, CI, 95%: 94-98], specificity of 95% [90-99], positive predictive value (PPV) of 99% [98-100], negative predictive value (NPV) of 82% [76-89], diagnostic odds ratio (DOR) of 496 [198-1,245], area under the ROC (AUC) of 0.96 [0.94-0.97], Matthews Correlation Coefficient (MCC) of 0.87 [0.85-0.88], accuracy of 96% [94-98], and Cohen's Kappa of 0.86 [0.81-0.91]. The proposed algorithm showed excellent diagnosis accuracy and class-labeling agreement, and fair discriminant power. The AUC on the datasets 2 and 3 was 0.97 [0.96-0.98] and 0.92 [0.91-0.94], respectively. The most important feature was white blood cell count, shortness of breath, and C-reactive protein for datasets 1, 2, and 3, respectively. The proposed algorithm is, thus, a promising COVID-19 diagnosis method, which could be an amendment to simple blood tests and screening of symptoms. However, the RT-PCR and chest CT-scan, performed as the gold standard, are not 100% accurate.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».