Predictive Analytics of COVID-19 with Neural Networks
Bibliographic record
Abstract
Neural networks (NNs) have been applied in numerous real-life applications and services. These include the applications in disease and healthcare analytics, such as identification and predictive analytics of coronavirus disease 2019 (COVID-19). However, many existing NN-based solutions train the models based on data (e.g., computed tomography (CT) scan images, viral/molecular test results) that can be expensive to produce and/or not easily accessible. They also require large volumes of these data for training. However, partially due to privacy concerns and other factors, the volume of available COVID-19 data can be limited. Hence, in this paper, we present a solution for predictive analytics of COVID-19 with NNs. Our solution consists of three algorithms, which make good use of autoencoder and few-shot learning, to train the prediction model with only a few samples of more accessible and less expensive types of data (e.g., serology/antibody test results from blood samples). Evaluation results on a real-life Brazilian COVID-19 dataset demonstrate the effectiveness of our solution in predictive analytics of COVD-19 with NNs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".