Summary of Artificial Intelligence For Infectious Disease Prediction and Prevention: a Comprehensive Review, by Selestine Melchane et al.
Artificial Intelligence for Infectious Disease Prediction and Prevention: A Comprehensive Review
by Selestine Melchane, Youssef Elmir, Farid Kacimi, Larbi Boubchir
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Populations and Evolution (q-bio.PE)
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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 The abstract discusses the intersection of artificial intelligence (AI) and infectious diseases prediction, highlighting the advancements in machine learning (ML) and deep learning (DL) approaches. Despite their successes in predicting infectious diseases, challenges arise from data types and analysis methods used. This research categorizes contributions into three areas: using Public Health Data to prevent disease spread, Patients’ Medical Data for infection detection, and combining both datasets for population-level disease spread estimation. The paper also evaluates AI’s potential and limitations in infectious disease management. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how artificial intelligence (AI) can help predict and manage infectious diseases. It shows that machine learning (ML) and deep learning (DL) have made progress in this area, but there are still challenges to overcome. The research groups the achievements into three parts: using data about public health to stop disease from spreading, using medical data about individual patients to see if they’re infected, and combining both types of data to figure out how a disease is spreading through a population. |
Keywords
* Artificial intelligence * Deep learning * Machine learning
