AI4CoV: Matching COVID-19 Patients to Treatment Options Using Artificial Intelligence
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
We developed AI4CoV, a novel AI system to match thousands of COVID-19 clinical trials to patients based on each patient’s eligibility to clinical trials in order to help physicians select treatment options for patients. AI4CoV leveraged Natural Language Processing (NLP) and Machine Learning to parse through eligibility criteria of trials and patients’ clinical manifestations in their clinical notes, both presented in English text, to accomplish 92.76% AUROC on a cross-validation test with 3,156 patient-trial pairs labeled with ground truth of suitability. Our retrospective multiple-site review shows that according to AI4CoV, severe patients of COVID-19 generally have less treatment options suitable for them than mild and moderate patients and that suitable and unsuitable treatment options are different for each patient. Our results show that the general approach of AI4CoV is useful during the early stage of a pandemic when the best treatments are still unknown.
Article activity feed
-
-
SciScore for 10.1101/2020.11.29.20240614: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources For every clinical trial under consideration, AI4CoV applies a preprocessing step to split its eligibility criteria into a text file with each line containing three columns: a criteria sentence, “in” or “ex” depending on whether the criteria is an inclusion or exclusion, and the NCT number of the trial assigned by ClinicalTrials. ClinicalTrialssuggested: (ClinicalTrials.gov, RRID:SCR_002309)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 …SciScore for 10.1101/2020.11.29.20240614: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources For every clinical trial under consideration, AI4CoV applies a preprocessing step to split its eligibility criteria into a text file with each line containing three columns: a criteria sentence, “in” or “ex” depending on whether the criteria is an inclusion or exclusion, and the NCT number of the trial assigned by ClinicalTrials. ClinicalTrialssuggested: (ClinicalTrials.gov, RRID:SCR_002309)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: We found the following clinical trial numbers in your paper:
Identifier Status Title NCT04252664 Suspended A Trial of Remdesivir in Adults With Mild and Moderate COVID… NCT04343729 Active, not recruiting Methylprednisolone in the Treatment of Patients With Signs o… 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.
-