Summary of Knowledge-informed Machine Learning For Cancer Diagnosis and Prognosis: a Review, by Lingchao Mao et al.
Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review
by Lingchao Mao, Hairong Wang, Leland S. Hu, Nhan L Tran, Peter D Canoll, Kristin R Swanson, Jing Li
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 challenges in treating cancer using machine learning models, which are hindered by limited labeled sample sizes, high-dimensional data, and concerns about interpretability and consistency with biomedical knowledge. To overcome these limitations, integrating biomedical knowledge into data-driven models can improve accuracy, robustness, and interpretability. The study reviews state-of-the-art machine learning studies that adopt this approach for cancer diagnosis and prognosis, highlighting modeling considerations relevant to different data types (clinical, imaging, molecular, and treatment). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cancer is a tough disease to treat. Machine learning helps doctors analyze patient data and images to diagnose and predict cancer. But there are problems with these models. They don’t have enough labeled samples, the data is too complex, and it’s hard to understand how they make decisions. One way to fix this is by combining medical knowledge with machine learning. This can make the results more accurate, reliable, and easy to understand. The study looks at how scientists are using this approach for cancer diagnosis and prognosis. |
Keywords
* Artificial intelligence * Machine learning
