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Research article
KOFFKA KHAN
The University of the West Indies at Saint Augustine
Corresponding Author
ORCiD: https://orcid.org/0000-0001-7712-1124
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia continues to affect the whole world including major cities such as China, USA, Italy, France and the United Kingdom. We present outcome ('recovered', 'isolated' or 'death') risk estimates of the 2019-nCoV over 'early' datasets. A major consideration is how likely are people to die from 2019-nCoV?
Method: Accounting for the impact of the variations in the reporting rate of 2019-nCoV, we modelled machine learning techniques (AdaBoost, Bagging, Extra-Trees, Decision-Trees and k-Nearest Neighbours Classifiers) on two 2019-nCoV datasets obtained from Kaggle in March 30th 2020. We used 'country', 'age' and 'gender' as features to predict outcome for both datasets. Including the patient's 'disease' history (only present in the second dataset) to predict outcome for the second dataset.
Results: The use of a patient's 'disease' history improves the prediction of 'death' by more than a 7-fold. Models ignoring a patent's 'disease' history performed poorly in test predictions.
Conclusion: Our findings indicate the potential of using a patient's 'disease' history as part of the feature set in machine learning techniques to improve 2019-nCoV predictions. This can have a positive effect on predictive patient treatment and result in ease for current overburdened healthcare systems worldwide, especially with an increasing prevalence of second and third wave re-infections in some countries.
Keywords
2019-nCoV, pneumonia, machine learning, AdaBoost, Bagging,classi ers, disease, death, prediction
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CURRENT STATUS: Published
View the published article at BMC Medical Informatics and Decision Making.
No community comments so far
Review #1 received
Received 08 Mar, 2021
Reviewer #1 agreed
On 14 Feb, 2021
Reviewers invited
Invitations sent on 07 Feb, 2021
Editor assigned
On 06 Jan, 2021
Submission checks complete
On 06 Jan, 2021
Editor invited
On 06 Jan, 2021
No comments provided
Editorial decision: Major revision
On 13 Dec, 2020
Review #1 received
Received 05 Dec, 2020
Reviewer #1 agreed
On 11 Nov, 2020
Reviewers invited
Invitations sent on 10 Nov, 2020
Editor assigned
On 01 Nov, 2020
Editor invited
On 01 Nov, 2020
Submission checks complete
On 28 Oct, 2020
Version 1
Posted 02 Jul, 2020
No comments provided
Editorial decision: Major revision
On 30 Sep, 2020
Review #2 received
Received 25 Sep, 2020
Review #1 received
Received 09 Sep, 2020
Reviewer #2 agreed
On 19 Aug, 2020
Reviewer #1 agreed
On 09 Aug, 2020
Reviewers invited
Invitations sent on 23 Jul, 2020
Editor assigned
On 08 Jun, 2020
First submitted
On 07 Jun, 2020
Submission checks complete
On 07 Jun, 2020
Editor invited
On 07 Jun, 2020
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Subject Areas
Medical Informatics
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A new preprint design is coming!
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See the published version of this preprint at BMC Medical Informatics and Decision Making.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
KOFFKA KHAN
The University of the West Indies at Saint Augustine
Corresponding Author
ORCiD: https://orcid.org/0000-0001-7712-1124
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Medical Informatics and Decision Making.
No community comments so far
Review #1 received
Received 08 Mar, 2021
Reviewer #1 agreed
On 14 Feb, 2021
Reviewers invited
Invitations sent on 07 Feb, 2021
Editor assigned
On 06 Jan, 2021
Submission checks complete
On 06 Jan, 2021
Editor invited
On 06 Jan, 2021
No comments provided
Editorial decision: Major revision
On 13 Dec, 2020
Review #1 received
Received 05 Dec, 2020
Reviewer #1 agreed
On 11 Nov, 2020
Reviewers invited
Invitations sent on 10 Nov, 2020
Editor assigned
On 01 Nov, 2020
Editor invited
On 01 Nov, 2020
Submission checks complete
On 28 Oct, 2020
Version 1
Posted 02 Jul, 2020
No comments provided
Editorial decision: Major revision
On 30 Sep, 2020
Review #2 received
Received 25 Sep, 2020
Review #1 received
Received 09 Sep, 2020
Reviewer #2 agreed
On 19 Aug, 2020
Reviewer #1 agreed
On 09 Aug, 2020
Reviewers invited
Invitations sent on 23 Jul, 2020
Editor assigned
On 08 Jun, 2020
First submitted
On 07 Jun, 2020
Submission checks complete
On 07 Jun, 2020
Editor invited
On 07 Jun, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Medical Informatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Medical Informatics and Decision Making.
Background: An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia continues to affect the whole world including major cities such as China, USA, Italy, France and the United Kingdom. We present outcome ('recovered', 'isolated' or 'death') risk estimates of the 2019-nCoV over 'early' datasets. A major consideration is how likely are people to die from 2019-nCoV?
Method: Accounting for the impact of the variations in the reporting rate of 2019-nCoV, we modelled machine learning techniques (AdaBoost, Bagging, Extra-Trees, Decision-Trees and k-Nearest Neighbours Classifiers) on two 2019-nCoV datasets obtained from Kaggle in March 30th 2020. We used 'country', 'age' and 'gender' as features to predict outcome for both datasets. Including the patient's 'disease' history (only present in the second dataset) to predict outcome for the second dataset.
Results: The use of a patient's 'disease' history improves the prediction of 'death' by more than a 7-fold. Models ignoring a patent's 'disease' history performed poorly in test predictions.
Conclusion: Our findings indicate the potential of using a patient's 'disease' history as part of the feature set in machine learning techniques to improve 2019-nCoV predictions. This can have a positive effect on predictive patient treatment and result in ease for current overburdened healthcare systems worldwide, especially with an increasing prevalence of second and third wave re-infections in some countries.
Figure 1
Figure 2
The full text of this article is available to read as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
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