A machine learning-based model for survival prediction in patients with severe COVID-19 infection

Abstract

We screened the electronic records of 2,799 patients admitted in Tongji Hospital from January 10th to February 18th, 2020. There were 375 discharged patients including 201 survivors. We built a prognostic prediction model based on XGBoost machine learning algorithm and then tested 29 patients (included 3 patients from other hospital) who were cleared after February 19th. Results:The mean age of the 375 patients was 58.83 years old with 58.7% of males. Fever was the most common initial symptom (49.9%), followed by cough (13.9%), fatigue (3.7%), and dyspnea (2.1%). Our model identified three key clinical features, i.e., lactic dehydrogenase (LDH), lymphocyte and High-sensitivity C-reactive protein (hs-CRP), from a pool of more than 300 features. The clinical route is simple to check and can precisely and quickly assess the risk of death. Therefore, it is of great clinical significance. : medRxiv preprint Conclusion:The three indices-based prognostic prediction model we built is able to predict the mortality risk, and present a clinical route to the recognition of critical cases from severe cases. It can help doctors with early identification and intervention, thus potentially reducing mortality.

Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection

“…33 Prognostic models. To assist in the prognosis of mortality, a nomogram (a graphic aid to calculate mortality risk), 7 a decision tree, 21 and a CT-based scoring rule are available in the articles. 22 There is also a nomogram to predict progression to severe COVID- 19.…”

“…Bai, Fang, et al 9 low unclear unclear high 38 Caramelo, Ferreira, et al 18 high high high high Gong, Ou, et al 32 low unclear unclear high Lu, Hu, et al 19 low low low high Qi, Jiang, et al 20 unclear low low high Shi, Yu, et al 37 high high high high Xie, Hungerford, et al 7 low low low high Yan, Zhang, et al 21 low high low high Yuan, Yin, et al 22 low high low high *1 Risk of bias high due to not evaluating calibration. If this criterion is not taken into account, analysis risk of bias would have been unclear.…”

Data-driven discovery of a clinical route for severity detection of COVID-19 pediatric cases

“…A previous study [1] described the demographic, epidemiologic, and clinical features for infected infants. However, compared with adult cases, little attention has been paid to the infected pediatric cases [2][3][4][5][6].…”

“…"-1" was used to complement the incomplete clinical measures to avoid bias. We used standard F1-score [4] to evaluate the performance of the classifier.…”

Artificial intelligence and machine learning to fight COVID-19

“…The large-scale data of COVID-19 patients can be integrated and analyzed by advanced machine learning algorithms to better understand the pattern of viral spread, further improve diagnostic speed and accuracy, develop novel effective therapeutic approaches, and potentially identify the most susceptible people based on personalized genetic and physiological characteristics. Inspirationally, within a short period of time since COVID-19 outbreak, advanced machine learning techniques have been used in taxonomic classification of COVID-19 genomes (8), CRISPR-based COVID-19 detection assay (6), survival prediction of severe COVID-19 patients (11), and discovering potential drug candidates against COVID-19 (4).…”