Deep Learning Based Multi-Label Prediction of Hospitalization for COVID-19 Cases
Bibliographic record
Abstract
Health informatics is an interdisciplinary area where computer science and related disciplines meet to address problems and support healthcare and medicine. In particular, computer has played an important role in medicine. Many existing computer-based systems (e.g., machine learning models) for healthcare applications produce binary prediction (e.g., whether a patient catches a disease or not). However, there are situations in which a non-binary prediction (e.g., what is hospitalization status of a patient) is needed. As a concrete example, over the past two years, people around the world have been affected by the coronavirus disease 2019 (COVID-19) pandemic. There have been works on binary prediction to determine whether a patient is COVID-19 positive or not. With availability of alternative methods (e.g., rapid test), such a binary prediction has become less important. Moreover, with the evolution of the disease (e.g., recent development of COVID-19 Omicron variant), multi-label prediction of the hospitalization status has become more important when compared with binary prediction on the confirmation of cases. Hence, in this paper, we present a multi-label prediction system for computer-based medical applications. Our system makes use of autoencoders (consisting of encoders and decoders) and few-shot learning to predict the hospitalization status (e.g., ICU, semi-ICU, regular wards, or no hospitalization). The prediction is important for allocation of medical resources (e.g., hospital facilities and medical staff), which in turn affect patient lives. Experimental results on real-life open datasets show that, when training with only a few data, our multilabel prediction system gave a high F1-score when predicting hospitalization status of COVID-19 cases.
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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 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".