Exploring Machine Learning in Healthcare and its Impact on the SARS-CoV-2 Outbreak
Bibliographic record
Abstract
Machine learning can be defined as a comprehensive range of tools utilized for recognizing patterns in data. Owing to its reliance on artificial intelligence in lieu of age-old, traditional methods, machine learning has established itself as an exceedingly quicker way of discerning patterns and trends from bulk data. The advanced system can even update itself on the availability of new data. This paper intends to elucidate different techniques involved in machine learning that have facilitated the prediction, detection, and restriction of infectious diseases in the past few decades. Moreover, in light of the unprecedented COVID-19 pandemic, such tools and techniques have been utilized extensively by smart cities to curb the proliferation of the SARS-CoV-2 virus. However, the strengths and weaknesses of this approach remain abstruse and therefore, this review also aims to evaluate the role of machine learning in the recent coronavirus outbreak.
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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 | 0.001 | 0.000 |
| 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".