In JMIR medical informatics ; h5-index 23.0
BACKGROUND : An accurate prediction of COVID-19 patient disease severity would greatly improve care delivery and resource allocation, and thereby reduce mortality risks, especially in less developed countries. There are many patient-related factors, such as pre-existing comorbidities that affect disease severity that could be used to aid prediction.
OBJECTIVE : Since rapid automated profiling of peripheral blood samples is widely available, we investigated how such data from the peripheral blood of COVID-19 patients might be used to predict clinical outcomes.
METHODS : We thus investigated such clinical datasets from COVID-19 patients with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, K-nearest neighbour and deep learning methods.
RESULTS : Our work revealed several clinical parameters measurable in blood samples as factors that can discriminate between healthy people and COVID-19 positive patients, and showed their value in predicting later severity of COVID-19 symptoms. We thus developed a number of analytical methods that showed accuracy and precision scores for disease severity predictions as above 90%.
CONCLUSIONS : We developed methodologies to analyse patient routine clinical data which enables more accurate prediction of COVID-19 patient outcomes. This type of approach could, by employing standard hospital laboratory analyses of patient blood, be utilised to identify COVID-19 patients at high risk of mortality and so enable optimised hospital facility for COVID-19 treatment.
Aktar Sakifa, Ahamad Md Martuza, Rashed-Al-Mahfuz Md, Azad Akm, Uddin Shahadat, Kamal A H M, Alyami Salem A, Lin Ping-I, Islam Sheikh Mohammed Shariful, Quinn Julian M W, Eapen Valsamma, Moni Mohammad Ali