Multi-Label Classification of Contributing Causal Factors in Self-Reported Safety Narratives

Robinson, S.D.

doi:10.3390/safety4030030

Cited by 12 publications

(8 citation statements)

References 18 publications

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“…e disadvantage of their method is that, even when things like "cabin" and "weather" are mentioned in an incident report, they are not necessarily the actual factors that caused the incident. Robinson [13] was one of the first authors to tackle multilabel classification using an ASRS data set. e author built a latent semantic analysis (LSA) model, trained it with 4,497 incidents, and tested the model on 2,987 other incidents.…”

Section: Automated Incident Analysis In Safety Managementmentioning

confidence: 99%

Identifying Incident Causal Factors to Improve Aviation Transportation Safety: Proposing a Deep Learning Approach

Dong

Yang

Ebadi

et al. 2021

Journal of Advanced Transportation

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Aviation is a complicated transportation system, and safety is of paramount importance because aircraft failure often involves casualties. Prevention is clearly the best strategy for aviation transportation safety. Learning from past incident data to prevent potential accidents from happening has proved to be a successful approach. To prevent potential safety hazards and make effective prevention plans, aviation safety experts identify primary and contributing factors from incident reports. However, safety experts’ review processes have become prohibitively expensive nowadays. The number of incident reports is increasing rapidly due to the acceleration of advances in information technologies and the growth of the commercial and private aviation transportation industries. Consequently, advanced text mining algorithms should be applied to help aviation safety experts facilitate the process of incident data extraction. This paper focuses on constructing deep-learning-based models to identify causal factors from incident reports. First, we prepare the data sets used for training, validation, and testing with approximately 200,000 qualified incident reports from the Aviation Safety Reporting System (ASRS). Then, we take an open-source natural language model, which is well trained with a large corpus of Wikipedia texts, as the baseline and fine-tune it with the texts in incident reports to make it more suited to our specific research task. Finally, we build and train an attention-based long short-term memory (LSTM) model to identify primary and contributing factors in each incident report. The solution we propose has multilabel capability and is automated and customizable, and it is more accurate and adaptable than traditional machine learning methods in extant research. This novel application of deep learning algorithms to the incident reporting system can efficiently improve aviation safety.

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Section: Automated Incident Analysis In Safety Managementmentioning

confidence: 99%

Identifying Incident Causal Factors to Improve Aviation Transportation Safety: Proposing a Deep Learning Approach

Dong

Yang

Ebadi

et al. 2021

Journal of Advanced Transportation

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“…This is possibly due to the different number of training data for both corpus; the MATA-D dataset had classified 39 CSB reports and 13 NTSB reports, among a total of 238 accident reports from different industry sectors. However, 85% is considered an equally good result for the classification of narrative reports into a taxonomy, especially if considered that the inter-rater reliabilities within experts are considered acceptable if the label accuracy is above 70% [9].…”

Section: Accuracy Of the Machine-learning Model Createdmentioning

confidence: 99%

“…In the paper, a machine learning tool based on text recognition and supporting vector machine is proposed to automatically extract relevant information from accident reports. Previous works have used machine-learning to classify textual narratives for aviation and railway into defined (taxonomy) or dynamic (ontology) categories [9]- [10]. The main differences is that they have used a tanomy/ontology not entirely relevant for the human error model, and they have used voluntarily submitted reports, where the model needed inputs from investigation reports.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning Tool for Human Factors Evaluation – Application to Lion Air Boeing 737-8 Max Accident

Morais

Yung

Patelli

2019

Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

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The capability of learning from accidents as quickly as possible allows preventing repeated mistakes to happen. This has been shown by the small time interval between two accidents with the same aircraft model: the Boeing 737-8 MAX. However, learning from major accidents and subsequently update the developed accident models has been proved to be a cumbersome process. This is because safety specialists use to take a long period of time to read and digest the information, as the accident reports are usually very detailed, long and sometimes with a difficult language and structure. A strategy to automatically extract relevant information from report accidents and update model parameters is investigated. A machine-learning tool has been developed and trained on previous expert opinion on several accident reports. The intention is that for each new accident report that is issued, the machine can quickly identify the more relevant features in secondsinstead of waiting for some days for the expert opinion. This way, the model can be more quickly and dynamically updated. An application to the preliminary accident report of the 2018 Lion Air accident is provided to show the feasibility of the machine-learning proposed approach.

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“…To the best of our knowledge, there is no work reported in the field of radiotherapy to identify the severity of the incidents reported using incident description. However there have been well reported research in other industries such as aviation, and nuclear [ 8 , 9 , 10 , 11 , 12 ] to classify the incidents reported in the respective fields. In healthcare there has been successful work done in classifying the verbal autopsies [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Incident Triage in Radiation Oncology Incident Learning System

et al. 2020

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The Radiotherapy Incident Reporting and Analysis System (RIRAS) receives incident reports from Radiation Oncology facilities across the US Veterans Health Affairs (VHA) enterprise and Virginia Commonwealth University (VCU). In this work, we propose a computational pipeline for analysis of radiation oncology incident reports. Our pipeline uses machine learning (ML) and natural language processing (NLP) based methods to predict the severity of the incidents reported in the RIRAS platform using the textual description of the reported incidents. These incidents in RIRAS are reviewed by a radiation oncology subject matter expert (SME), who initially triages some incidents based on the salient elements in the incident report. To automate the triage process, we used the data from the VHA treatment centers and the VCU radiation oncology department. We used NLP combined with traditional ML algorithms, including support vector machine (SVM) with linear kernel, and compared it against the transfer learning approach with the universal language model fine-tuning (ULMFiT) algorithm. In RIRAS, severities are divided into four categories; A, B, C, and D, with A being the most severe to D being the least. In this work, we built models to predict High (A & B) vs. Low (C & D) severity instead of all the four categories. Models were evaluated with macro-averaged precision, recall, and F1-Score. The Traditional ML machine learning (SVM-linear) approach did well on the VHA dataset with 0.78 F1-Score but performed poorly on the VCU dataset with 0.5 F1-Score. The transfer learning approach did well on both datasets with 0.81 F1-Score on VHA dataset and 0.68 F1-Score on the VCU dataset. Overall, our methods show promise in automating the triage and severity determination process from radiotherapy incident reports.

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Multi-Label Classification of Contributing Causal Factors in Self-Reported Safety Narratives

Cited by 12 publications

References 18 publications

Identifying Incident Causal Factors to Improve Aviation Transportation Safety: Proposing a Deep Learning Approach

Identifying Incident Causal Factors to Improve Aviation Transportation Safety: Proposing a Deep Learning Approach

Machine-Learning Tool for Human Factors Evaluation – Application to Lion Air Boeing 737-8 Max Accident

Automatic Incident Triage in Radiation Oncology Incident Learning System

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