Summary of Deep Learning-based Identification Of Patients at Increased Risk Of Cancer Using Routine Laboratory Markers, by Vivek Singh et al.
Deep learning-based identification of patients at increased risk of cancer using routine laboratory markers
by Vivek Singh, Shikha Chaganti, Matthias Siebert, Sowmya Rajesh, Andrei Puiu, Raj Gopalan, Jamie Gramz, Dorin Comaniciu, Ali Kamen
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper presents a blood marker-based risk stratification approach for identifying patients with elevated cancer risk, which can be used to encourage diagnostic testing or participation in screening programs. The approach combines widely available blood tests, such as complete blood count and complete metabolic panel, to identify individuals at risk for colorectal, liver, and lung cancers. The proposed method achieves areas under the ROC curve of 0.76, 0.85, and 0.78, respectively. This approach has potential applications in population health management, enabling better cancer risk assessment in specific sub-populations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cancer screening is important for improving survival rates and reducing costly treatments. To make sure that the right people get screened, we need to figure out who’s most at risk. In this paper, scientists are working on a new way to do this using blood tests. They found that by combining simple tests like complete blood count and complete metabolic panel, they can identify people with a higher chance of getting certain types of cancer, such as colorectal, liver, or lung cancer. This could help us screen more effectively and manage health at the population level. |
Keywords
» Artificial intelligence » Roc curve
