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

• URL: http://arxiv.org/abs/2011.10657v1
• Date: Thu, 19 Nov 2020 10:32:46 GMT
• Title: Predicting Patient COVID-19 Disease Severity by means of Statistical and Machine Learning Analysis of Blood Cell Transcriptome Data
• Authors: 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, and Mohammad Ali Moni
• Abstract summary: We investigated how such data from the peripheral blood of COVID-19 patients might be used to predict clinical outcomes. Our work revealed several clinical parameters measurable in blood samples, which discriminated between healthy people and COVID-19 positive patients. We thus developed a number of analytic methods that showed accuracy and precision for disease severity and mortality outcome predictions that were above 90%.
• Score: 3.5699804146136676
• License: http://creativecommons.org/licenses/by/4.0/
• 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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