Summary of Causal Relationship Network Of Risk Factors Impacting Workday Loss in Underground Coal Mines, by Shangsi Ren et al.
Causal Relationship Network of Risk Factors Impacting Workday Loss in Underground Coal Mines
by Shangsi Ren, Cameron A. Beeche, Zhiyi Shi, Maria Acevedo Garcia, Katherine Zychowski, Shuguang Leng, Pedram Roghanchi, Jiantao Pu
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Methodology (stat.ME)
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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 This research aims to uncover the underlying causes of workday losses in underground coal mines using a novel AI-based approach. By leveraging data from the National Institute for Occupational Safety and Health (NIOSH), the study analyzed 101,010 injury records from 3,982 unique mines spanning two decades. The researchers employed Grouped Greedy Equivalence Search (GGES) to visualize causal relationships and intervention do-calculus adjustment (IDA) scores to assess the impact of each variable on workday loss. The findings revealed that key direct causes of workday losses include total mining experience, mean office employees, county, and total mining experience after 2006. Total mining experience emerged as the most influential factor, while mean employees per mine exhibited the least influence. The models achieved optimal performance, with metrics such as adjacency precision (AP), adjacency recall (AR), arrowhead precision (AHP), and arrowhead recall (AHR) measuring 0.694, 0.653, 0.386, and 0.345, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses a special kind of artificial intelligence to figure out why people take days off from work in underground coal mines. They looked at lots of data from the National Institute for Occupational Safety and Health (NIOSH) to find patterns that explain when this happens. By using a technique called Grouped Greedy Equivalence Search, they found that things like how long someone has been working in mining, the number of office workers, the county where the mine is located, and how many years someone has worked in mining all play a role in why people take days off. The most important thing was how long someone has been working in mining. This study shows that using this special AI can help us understand what makes people take days off and how we might be able to prevent it. |
Keywords
* Artificial intelligence * Precision * Recall
