Development and utilization of an intelligent application for aiding COVID-19 diagnosis
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Background
COVID-19 has been spreading globally since emergence, but the diagnostic resources are relatively insufficient.
Results
In order to effectively relieve the resource deficiency of diagnosing COVID-19, we developed a machine learning-based diagnosis model on basis of laboratory examinations indicators from a total of 620 samples, and subsequently implemented it as a COVID-19 diagnosis aid APP to facilitate promotion.
Conclusions
External validation showed satisfiable model prediction performance (i.e., the positive predictive value and negative predictive value was 86.35% and 84.62%, respectively), which guarantees the promising use of this tool for extensive screening.
Article activity feed
-
SciScore for 10.1101/2020.03.18.20035816: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: Patients and information collection: We have complied with all relevant ethical regulations for work with human subjects and the studies were approved by the Clinical Trials and Biomedical Ethics Committee of West China Hospital, Sichuan University. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
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: An explicit section about the limitations of the …SciScore for 10.1101/2020.03.18.20035816: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: Patients and information collection: We have complied with all relevant ethical regulations for work with human subjects and the studies were approved by the Clinical Trials and Biomedical Ethics Committee of West China Hospital, Sichuan University. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
-
