Peer Review #5 of "An artificial neural network classification method employing longitudinally monitored immune biomarkers to predict the clinical outcome of critically ill COVID-19 patients (v0.2)"
Bibliographic record
Abstract
The severe form of COVID-19 can cause a dysregulated host immune syndrome that might lead patients to death.To understand the underlying immune mechanisms that contribute to COVID-19 disease we have examined 28 different biomarkers in two cohorts of COVID-19 patients, aiming to systematically capture, quantify, and algorithmize how immune signals might be associated to the clinical outcome of COVID-19 patients. MethodsThe longitudinal concentration of 28 biomarkers of 95 COVID-19 patients was measured.We performed a dimensionality reduction analysis to determine meaningful biomarkers for explaining the data variability.The biomarkers were used as input of Artificial Neural Network, Random Forest, Classification and Regression Trees, K-Nearest Neighbors and Support Vector Machines.Two different clinical cohorts were used to grant validity to the findings. ResultsWe benchmarked the classification capacity of two COVID-19 clinicals studies with different models and found that Artificial Neural Networks was the best classifier.From it, we could employ different sets of biomarkers to predict the clinical outcome of COVID-19 patients.First, all the biomarkers available yielded a satisfactory classification.Next, we assessed the prediction capacity of each protein separated.With a reduced set of biomarkers, our model presented 94% accuracy, 96.6% precision, 91.6% recall, and 95% of specificity upon the testing data.We used the same model to predict 83% and 87% (recovered and deceased) of unseen data, granting validity to the results obtained. ConclusionsIn this work, using state-of-the-art computational techniques, we systematically identified an optimal set of biomarkers that are related to a prediction capacity of COVID-19 patients.The screening of such biomarkers might assist in understanding the underlying immune response towards inflammatory diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.203 | 0.096 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".