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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".