Summary of Review Of Interpretable Machine Learning Models For Disease Prognosis, by Jinzhi Shen and Ke Ma
Review of Interpretable Machine Learning Models for Disease Prognosis
by Jinzhi Shen, Ke Ma
First submitted to arxiv on: 19 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper reviews the application of interpretable machine learning techniques in predicting the prognosis of respiratory diseases, particularly COVID-19. The authors focus on machine learning models that can incorporate existing clinical knowledge and learn from new data. These models aid in managing the current pandemic and hold promise for addressing future disease outbreaks. By harnessing interpretable machine learning, healthcare systems can enhance preparedness and response capabilities, improving patient outcomes and mitigating respiratory disease impact. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how machine learning techniques can help doctors make better decisions during health crises like the COVID-19 pandemic. It looks at ways to understand and explain how these models work, so doctors can trust them and make informed choices. By using these transparent models, healthcare systems can prepare for future outbreaks and improve patient care. |
Keywords
» Artificial intelligence » Machine learning
