Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis

Abstract

Background The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests. Methods In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aim to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone. Results We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients. Conclusions We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups.

Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review

“…Overcoming data limitations necessitates a careful balance between data privacy and public health, as well as rigorous human–AI interaction [ 12 ]. Researchers worldwide are encouraged to release de-identified patient data to aid in data mining and ML efforts against COVID-19 [ 46 ].…”

“…On the other hand, clinical data and routine blood tests can represent a faster and cheaper diagnostic alternative with comparable, although inferior, performance [ 19 ]. Although viral testing is still the only specific method of diagnosis [ 44 ], accurate and fast models can be incredibly valuable during a pandemic peak to mitigate shortages of reference tests and to slow down the outbreak by early isolation of potential COVID-19 patients [ 19 , 45 , 46 ]. These models can also be used to cross-check RT-PCR tests where false negative results are well documented [ 47 , 48 ].…”

“…Li et al [ 46 ] developed a classification model based on XGBoost to discriminate between influenza and COVID-19 patients. The model predicts the presence of COVID-19 based on patient symptoms and routine test results.…”

“… Study; outcome Highest-weighted features ML approaches Sample size (no. of positive cases) Performance Li et al [ 46 ] ; diagnosis of COVID-19 and discrimination between influenza and COVID-19 Age, CT scan result, temperature, lymphocyte, fever, coughing XGBoost 413 patients (−) Sensitivity of 92.5% and specificity of 97.9% Kukar et al [ 49 ] ; diagnosis of COVID-19 MCHC, eosinophils count, albumin, INR, prothrombin activity % RF, DNN, and XGBoost (selected) 5333 patients (160 positive) AUC of 97%, sensitivity of 81.9%, specificity of 97.9% Bayat et al [ 48 ] ; diagnosis of COVID-19 Ferritin, WBC, eosinophil, temperature, CRP, LDH, D-dimer, basophil count, monocyte %, AST (in descending order of importance) XGBoost 75,991 patients (7335 positive) Accuracy of 86.4%, specificity of 86.8%, sensitivity of 82.4% Schwab et al [ 95 ] ; diagnosis of COVID-19 MISSING arterial tactic acid, age, leukocyte count, platelets, creatinine LR, NN, RF, SVM, and (XGBoost selected) 5644 (556 positive) XGBoost model achieved AUC of 0.66, sensitivity of 75%, and specificity of 49% Wu et al [ 50 ] ; diagnosis of COVID-19 Total bilirubin, glucose, creatinine, LDH, CK-MB, potassium, total protein, calcium, magnesium, PDW, basophils RF 253 samples from 169 suspected patients (105 samples from 27 patients confirmed positive) AUC of 99.26%, a sensitivity of 100%, and a specificity of 94.44% with an independent test set Brinati et al [ 51 ] ; diagnosis of COVID-19 AST, lymphocytes, LDH, WBC, eosinophils, ALT, age DT, ET, KNN, LR, NB, SVM, TWRF, and (RF selected) 279 patients (177 positive) AUC of 84%, accuracy of 8...…”

Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives

“…The potential applications of ML for COVID-19 have been previously described [14,[16][17][18][19][20][21][22][23][24][25][26]. The details are summarized in Table 1.…”