Predicting Patient COVID-19 Disease Severity by means of Statistical and Machine Learning Analysis of Blood Cell Transcriptome Data

Sakifa Aktar; Md. Martuza Ahamad; Md. Rashed-Al-Mahfuz; AKM Azad; Shahadat Uddin; A H M Kamal; Salem A. Alyami; Ping-I Lin; Sheikh Mohammed Shariful Islam; Julian M. W. Quinn; Valsamma Eapen; Mohammad Ali Moni

doi:10.2196/25884

arXiv · 2011.10657

Predicting Patient COVID-19 Disease Severity by means of Statistical and Machine Learning Analysis of Blood Cell Transcriptome Data

Sakifa Aktar·Md. Martuza Ahamad·Md. Rashed-Al-Mahfuz·AKM Azad·Shahadat Uddin·A H M Kamal·Salem A. Alyami·Ping-I Lin·Sheikh Mohammed Shariful Islam·Julian M. W. Quinn·Valsamma Eapen·Mohammad Ali Moni

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Abstract

Introduction: For COVID-19 patients accurate prediction of disease severity and mortality risk would greatly improve care delivery and resource allocation. There are many patient-related factors, such as pre-existing comorbidities that affect disease severity. 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, which discriminated between healthy people and COVID-19 positive patients and showed predictive value for later severity of COVID-19 symptoms. We thus developed a number of analytic methods that showed accuracy and precision for disease severity and mortality outcome predictions that were above 90%. Conclusions: In sum, we developed methodologies to analyse patient routine clinical data which enables more accurate prediction of COVID-19 patient outcomes. This type of approaches 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 their treatment to be optimised.

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Sakifa Aktar, Md. Martuza Ahamad, Md. Rashed-Al-Mahfuz, AKM Azad, Shahadat Uddin, A H M Kamal, Salem A. Alyami, Ping-I Lin, Sheikh Mohammed Shariful Islam, Julian M. W. Quinn, Valsamma Eapen, Mohammad Ali Moni. 2020-11-19. Predicting Patient COVID-19 Disease Severity by means of Statistical and Machine Learning Analysis of Blood Cell Transcriptome Data. https://doi.org/10.2196/25884

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