Using machine learning for personalized prediction of longitudinal coronavirus disease 2019 vaccine responses in transplant recipients
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
The coronavirus disease 2019 pandemic has underscored the importance of vaccines, especially for immunocompromised populations like solid organ transplant recipients, who often have weaker immune responses. The purpose of this study was to compare deep learning architectures for predicting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine responses 12 months postvaccination in this high-risk group. Using data from 303 solid organ transplant recipients from a Canadian multicenter cohort, models were developed to forecast anti-receptor-binding domain antibody levels. The study compared traditional machine learning models-logistic regression, epsilon-support vector regression, random forest regressor, and gradient boosting regressor-and deep learning architectures, including long short-term memory (LSTM), recurrent neural networks, and a novel model, routed LSTM. This new model combines capsule networks with LSTM to reduce the need for large data sets. Demographic, clinical, and transplant-specific data, along with longitudinal antibody measurements, were incorporated into the models. The routed LSTM performed best, achieving a mean square error of 0.02 ± 0.02 and a Pearson correlation coefficient of 0.79 ± 0.24, outperforming all other models. Key factors influencing vaccine response included age, immunosuppression, breakthrough infection, body mass index, sex, and transplant type. These findings suggest that artificial intelligence could be a valuable tool in tailoring vaccine strategies, improving health outcomes for vulnerable transplant recipients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".