Summary of Matchminer-ai: An Open-source Solution For Cancer Clinical Trial Matching, by Ethan Cerami et al.
MatchMiner-AI: An Open-Source Solution for Cancer Clinical Trial Matching
by Ethan Cerami, Pavel Trukhanov, Morgan A. Paul, Michael J. Hassett, Irbaz B. Riaz, James Lindsay, Emily Mallaber, Harry Klein, Gufran Gungor, Matthew Galvin, Stephen C. Van Nostrand, Joyce Yu, Tali Mazor, Kenneth L. Kehl
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 MatchMiner-AI, a pipeline for clinical trial searching and ranking using artificial intelligence. The goal is to accelerate the process of matching patients to appropriate clinical trials, which can improve cancer treatments and outcomes. The pipeline focuses on matching patients to potential trials based on core criteria describing clinical “spaces” or disease contexts targeted by a trial. It includes modules for extracting key information from electronic health records, rapid ranking of candidate trial-patient matches using vector space embeddings, and classifying whether a match represents a reasonable clinical consideration. The authors provide code, synthetic data, and model weights based on synthetic data to facilitate further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence to help patients with cancer find the right clinical trials for them. Right now, many people with cancer don’t participate in trials, and it can be hard to find the right trial. The authors created a new system that uses AI to search for trials based on what’s happening inside someone’s body. This could make it easier to match patients with the right trials, which could lead to better treatments and outcomes. |
Keywords
» Artificial intelligence » Synthetic data » Vector space
