Application of Machine Learning on Predicting the Risk of Death of Respiratory Infectious Diseases Patients — Using COVID-19 as Example
Bibliographic record
Abstract
In modern society, with the accelerating pace of urbanization and the increasing frequency of global travel, densely populated areas have become hotspots for the outbreak of infectious diseases. These areas, such as large-scale urban centers and crowded public transportation hubs, facilitate the rapid spread of pathogens. The patients affected by infectious diseases span a wide spectrum of different groups, including the elderly with weakened immune systems, children whose immune systems are still developing, and individuals with pre-existing chronic health conditions. The impact of infectious diseases on patients varies significantly. Depending on a patient's underlying health condition, the course of the disease can range from a mild, self-limiting illness to a severe, life-threatening condition. For example, in the case of influenza, some healthy individuals may experience only mild symptoms like a runny nose and mild fever, while the elderly or those with respiratory diseases may develop severe pneumonia, which can be fatal. In the context of the ongoing global health challenges, such as the COVID - 19 pandemic, there is an urgent need to explore innovative methods for predicting the risk of patients infected by infectious diseases. To demonstrate whether machine learning techniques can be effectively used for this purpose, this study employs neural networks to develop a model specifically designed to predict the risk of death among COVID-19 patients. Through rigorous testing and validation, the model has been proven to have a high degree of accuracy in predicting these risks. This indicates that the method proposed in this study can be applied to identify which patients have a more urgent need for medical resources, thus providing valuable reference for healthcare providers to optimize the allocation of healthcare resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".