Summary of Aces: Automatic Cohort Extraction System For Event-stream Datasets, by Justin Xu et al.
ACES: Automatic Cohort Extraction System for Event-Stream Datasets
by Justin Xu, Jack Gallifant, Alistair E. W. Johnson, Matthew B. A. McDermott
First submitted to arxiv on: 28 Jun 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 addresses the reproducibility challenge in machine learning for healthcare by introducing the Automatic Cohort Extraction System (ACES) for event-stream data. ACES simplifies developing tasks and cohorts, enabling reproduction at exact and conceptual levels across datasets. It provides a domain-specific configuration language for defining dataset-specific concepts and criteria, as well as a pipeline to extract patient records meeting defined criteria from real-world data. ACES can be applied to MEDS or ESGPT format datasets, or any event-stream form dataset. This system has the potential to lower the barrier to entry for ML tasks in representation learning, redefine researcher interactions with EHR datasets, and improve reproducibility for ML studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making it easier for researchers to work with electronic health records (EHRs) and get similar results when they test new ideas. This is important because EHRs are private and sharing them can be hard. The Automatic Cohort Extraction System (ACES) helps solve this problem by letting researchers define what they want to look at in the data and then automatically pulling out those patients from the records. ACES works with different types of data formats and has the potential to make it easier for people to do representation learning on EHRs, which could lead to better healthcare. |
Keywords
» Artificial intelligence » Machine learning » Representation learning
