Identifying low-quality patterns in accident reports from textual data

Macêdo, July Bias; Ramos, Plìnio M. S.; Maior, Caio Bezerra Souto; Moura, Márcio J. C.; Lins, Isis Didier; Vilela, Romulo Fernando

doi:10.1080/10803548.2022.2111847

Cited by 4 publications

(1 citation statement)

References 53 publications

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“…Researchers Domain Data purposes [2] Workplace safety Accident investigation reports Predict the occurrence of the accident leave for a given accident [3] Work accidents Documents collected from an actual hydroelectric power company Investigate low-quality accident reports.…”

Section: Table 1 Study On the Extraction Of Risk Factors And Associat...mentioning

confidence: 99%

Extraction and Association Analysis of Risk Factors in Gas Pipeline Network Emergencies Through Fusing Expert Knowledge and Pre-Trained Model

Zhao,

Hu,

Liu

et al. 2024

IEEE Access

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The gas pipeline network constitutes a critical component of urban infrastructure. Emergencies, such as leaks or explosions, can lead to severe casualties, extensive property damage, and long-lasting effects on societal and environmental safety. The genesis of such emergencies typically involves a complex interplay among various risk factors. Therefore, the meticulous identification of these risk factors and the effective modeling of their interrelations are crucial for assessing the risks associated with such emergencies and enhancing urban safety. In this research, we initially developed a specialized dataset tailored specifically to analyze emergencies within gas pipeline networks. Recognizing the significance of unstructured domainspecific knowledge, we introduced an innovative approach Fusing expert knowledge and pre-trained model. This approach facilitates the fusion of domain experts' knowledge into the process of risk factor identification and extraction in different steps, ensuring a thorough and precise determination and filtration of risk factors. Subsequently, a network depicting the associations between risk factors was established, drawing on their cooccurrence relationships, followed by a quantitative analysis of this network. Supported by real-world gas network management and emergency cause investigations, the methods proposed in this study provide strong support for gas network safety management and emergency decision-making.

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Section: Table 1 Study On the Extraction Of Risk Factors And Associat...mentioning

confidence: 99%

Extraction and Association Analysis of Risk Factors in Gas Pipeline Network Emergencies Through Fusing Expert Knowledge and Pre-Trained Model

Zhao,

Hu,

Liu

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

Selection and classification of COVID-19 CT images using artificial intelligence: A case study in a Brazilian university hospital

Lins,

Maior,

Raupp

et al. 2024

Biomedical Signal Processing and Control

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Analysis of coal mining accident risk factors based on text mining

He,

2024

Proceedings of the Institution of Mechanical Engineers, Part O:

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Frequently occurring coal mine safety accidents have caused great casualties and economic losses. Coal mine intelligence is the core technical support for the high-quality development of the coal industry. The deep integration of coal mining safety production management and Artificial Intelligence (AI) technology is practically significant to achieve accident prevention. To efficiently identify mining accident risk factors and explore mechanism of coupling between risk factors, this study mined 400 reported cases of mining accidents in Shanxi Province and identified 64 accident risk factors through custom, stopword, synonym dictionary construction, keyword extraction and keyword correlation analysis. Then, this study constructed association rules and a Bayesian causal network. The major risk factors are identified using a comprehensive high-frequency, sensitivity, strength and key path analysis of the Bayesian causal network. The following seven risk factors are found to play a major role in the occurrence of mine accidents: inadequate safety supervision, disordered safety management, illegal organization of production, inadequate staff safety education and training, operation against rules, command against rules and weak safety consciousness among the staff. Finally, a case study is conducted to validate the reliability of the results. This study solves the problem of incomplete extraction of key feature information in coal mine reports and the lack of analyses of coupling mechanisms between coal mine risk factors in traditional accident analysis methods, providing the methodological support for the effective use of unstructured coal mine safety production data for risk analysis.

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Identifying low-quality patterns in accident reports from textual data

Cited by 4 publications

References 53 publications

Extraction and Association Analysis of Risk Factors in Gas Pipeline Network Emergencies Through Fusing Expert Knowledge and Pre-Trained Model

Extraction and Association Analysis of Risk Factors in Gas Pipeline Network Emergencies Through Fusing Expert Knowledge and Pre-Trained Model

Selection and classification of COVID-19 CT images using artificial intelligence: A case study in a Brazilian university hospital

Analysis of coal mining accident risk factors based on text mining

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