Development of targeted safety hazard management plans utilizing multidimensional association rule mining.

Xingbang Qiang, Guoqing Li, Yuksel Asli Sari, Chunchao Fan, Jie Hou

Abstract read

In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Application of text mining and causal analysis for risk identification in air traffic control safety operations.Scientific reports · 2025
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Xingbang QiangSchool of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Guoqing LiSchool of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Yuksel Asli SariThe Robert M. Buchan Department of Mining, Queen's University, Kingston K7L 3N6, Canada.

Chunchao FanSchool of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Jie HouSchool of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Investigating hidden hazards and implementing closed-loop management are essential strategies for accident prevention in the mining industry. This study tackles a key challenge in applying association rule mining to the development of hazard management plans for underground mines. The current approach mainly focuses on hazard description data, often underutilizing critical information such as hazard time and location. To address this, we integrate topic mining with association rule mining to uncover intrinsic association patterns among various attributes of mine safety hazards. Through a systematic analysis of standardized mining hazard attributes, five key analytical dimensions were identified: Hazard Type, Level, Time, Location, and Responsible Units. A topic mining model, utilizing the Biterm Topic Model, was constructed to reduce dimensionality and aggregate hazard description data. Evaluation indicators such as

Indexed as

Association rule miningMine safetySafety hazardTopic mining

Identifiers

PMID39719998

DOI10.1016/j.heliyon.2024.e40676

PMCPMC11666930

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.