Time Windows Voting Classifier for COVID-19 Mortality Prediction
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
The ability to predict COVID-19 patients’ level of severity (death or survival) enables clinicians to prioritise treatment. Recently, using three blood biomarkers, an interpretable machine learning model was developed to predict the mortality of COVID-19 patients. The method was reported to be suffering from performance stability because the identified biomarkers are not consistent predictors over an extended duration.
Methods
To sustain performance, the proposed method partitioned data into three different time windows. For each window, an end-classifier, a mid-classifier and a front-classifier were designed respectively using the XGboost single tree approach. These time window classifiers were integrated into a majority vote classifier and tested with an isolated test data set.
Results
The voting classifier strengthens the overall performance of 90% cumulative accuracy from a 14 days window to a 21 days prediction window.
Conclusions
An additional 7 days of prediction window can have a considerable impact on a patient’s chance of survival. This study validated the feasibility of the time window voting classifier and further support the selection of biomarkers features set for the early prognosis of patients with a higher risk of mortality.
Article activity feed
-
SciScore for 10.1101/2021.07.02.21259934: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources Statistical analyses were computed using SPSS (version 24). SPSSsuggested: (SPSS, RRID:SCR_002865)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study comes with certain limitations. First, the samples are retrospective and consist of 375 cases with 201 (53.6%) who survived and 174 …
SciScore for 10.1101/2021.07.02.21259934: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources Statistical analyses were computed using SPSS (version 24). SPSSsuggested: (SPSS, RRID:SCR_002865)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study comes with certain limitations. First, the samples are retrospective and consist of 375 cases with 201 (53.6%) who survived and 174 (46.4%) who died. While the small sample size limits generalisation, it does provide a direction for further analysis when larger data sets become available. Second, a more comprehensive analysis is needed to establish the stability of the algorithm. Our method may have reduced the instability, but we do need further evidence in support. Third, the data distribution with respect to mortality would affect the performance of the algorithm which requires further validation that takes hospital context into consideration (Wang et al., 2020).
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- No funding statement was detected.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
-