Bi-Directional LSTM-Based COVID-19 Detection Using Clinical Reports
Bibliographic record
Abstract
COVID-19 has affected the entire globe with its rapid spreading, causing a high transmission rate. A huge amount of people come in contact with this deadly virus, and early diagnosis of such kind of viruses may save many lives. This paper proposes an improved approach for detecting COVID-19 based on Long Short Term Memory (LSTM) and taking advantage of early clinical reports. To train the LSTM-based classifier for COVID-19 detection, various preprocessing techniques and word embeddings are employed. These techniques ensure the data is in a suitable format for the LSTM model. The proposed LSTM model is then compared against state-of-the-art ensemble models like Bagging and Random Forest, demonstrating its superior performance. The evaluation results showcase a testing accuracy of 87.15%, with a precision of 91% and a recall of 88%. These metrics indicate the effectiveness of the proposed LSTM model in accurately detecting COVID-19-positive cases. By leveraging early clinical reports and utilizing advanced deep learning techniques, our approach achieves significant improvements in COVID-19 detection compared to existing ensemble models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".