Dataset Information

A Method to Extract Causality for Safety Events in Chemical Accidents from Fault Trees and Accident Reports.

ABSTRACT: Chemical event evolutionary graph (CEEG) is an effective tool to perform safety analysis, early warning, and emergency disposal for chemical accidents. However, it is a complicated work to find causality among events in a CEEG. This paper presents a method to accurately extract event causality by using a neural network and structural analysis. First, we identify the events and their component elements from fault trees by natural language processing technology. Then, causality in accident events is divided into explicit causality and implicit causality. Explicit causality is obtained by analyzing the hierarchical structure relations of event nodes and the semantics of component logic gates in fault trees. By integrating internal structural features of events and semantic features of event sentences, we extract implicit causality by utilizing a bidirectional gated recurrent unit (BiGRU) neural network. An algorithm, named CEFTAR, is presented to extract causality for safety events in chemical accidents from fault trees and accident reports. Compared with the existing methods, experimental results show that our method has a higher accuracy and recall rate in extracting causality.

SUBMITTER: Du J

PROVIDER: S-EPMC7321517 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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A Method to Extract Causality for Safety Events in Chemical Accidents from Fault Trees and Accident Reports.

Du Junwei J Zhao Hanrui H Yu Yangyang Y Hu Qiang Q

Computational intelligence and neuroscience 20200619

Chemical event evolutionary graph (CEEG) is an effective tool to perform safety analysis, early warning, and emergency disposal for chemical accidents. However, it is a complicated work to find causality among events in a CEEG. This paper presents a method to accurately extract event causality by using a neural network and structural analysis. First, we identify the events and their component elements from fault trees by natural language processing technology. Then, causality in accident events ...[more]

PMID: 32788919

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Project description:OBJECTIVES:We sought to clarify how large a proportion of fatal medical accidents can be considered to be caused by poor non-technical skills, and to support development of a policy to reduce number of such accidents by making recommendations about possible training requirements. DESIGN:Summaries of reports of fatal medical accidents, published by the Japan Medical Safety Research Organization, were reviewed individually. Three experienced clinicians and one patient safety expert conducted the reviews to determine the cause of death. Views of the patient safety expert were given additional weight in the overall determination. SETTING:A total of 73 summary reports of fatal medical accidents were reviewed. These reports had been submitted by healthcare organisations across Japan to the Japan Medical Safety Research Organization between April 2010 and March 2013. PRIMARY AND SECONDARY OUTCOME MEASURES:The cause of death in fatal medical accidents, categorised into technical skills, non-technical skills and inevitable progress of disease were evaluated. Non-technical skills were further subdivided into situation awareness, decision making, communication, team working, leadership, managing stress and coping with fatigue. RESULTS:Overall, the cause of death was identified as non-technical skills in 34 cases (46.6%), disease progression in 33 cases (45.2%) and technical skills in two cases (5.5%). In two cases, no consensual determination could be achieved. Further categorisation of cases of non-technical skills were identified as 14 cases (41.2%) of problems with situation awareness, eight (23.5%) with team working and three (8.8%) with decision making. These three subcategories, or combinations of them, were identified as the cause of death in 33 cases (97.1%). CONCLUSIONS:Poor non-technical skills were considered to be a significant cause of adverse events in nearly half of the fatal medical accidents examined. Improving non-technical skills may be effective for reducing accidents, and training in particular subcategories of non-technical skills may be especially relevant.

Project description:Clinical narratives are a valuable source of information for both patient care and biomedical research. Given the unstructured nature of medical reports, specific automatic techniques are required to extract relevant entities from such texts. In the natural language processing (NLP) community, this task is often addressed by using supervised methods. To develop such methods, both reliably-annotated corpora and elaborately designed features are needed. Despite the recent advances on corpora collection and annotation, research on multiple domains and languages is still limited. In addition, to compute the features required for supervised classification, suitable language- and domain-specific tools are needed. In this work, we propose a novel application of recurrent neural networks (RNNs) for event extraction from medical reports written in Italian. To train and evaluate the proposed approach, we annotated a corpus of 75 cardiology reports for a total of 4365 mentions of relevant events and their attributes (e.g., the polarity). For the annotation task, we developed specific annotation guidelines, which are provided together with this paper. The RNN-based classifier was trained on a training set including 3335 events (60 documents). The resulting model was integrated into an NLP pipeline that uses a dictionary lookup approach to search for relevant concepts inside the text. A test set of 1030 events (15 documents) was used to evaluate and compare different pipeline configurations. As a main result, using the RNN-based classifier instead of the dictionary lookup approach allowed increasing recall from 52.4% to 88.9%, and precision from 81.1% to 88.2%. Further, using the two methods in combination, we obtained final recall, precision, and F1 score of 91.7%, 88.6%, and 90.1%, respectively. These experiments indicate that integrating a well-performing RNN-based classifier with a standard knowledge-based approach can be a good strategy to extract information from clinical text in non-English languages.

Project description:PurposeThis meta-epidemiological study aimed to systematically review case reports regarding sports nutrition supplements and adverse events (AEs), specifically addressing the issue of causality assessments.MethodsThrough a systematic literature search we identified all published case reports of AEs associated with sports nutrition supplements between 1 January 2008 and 1 March 2019. Data regarding AEs, suspected supplements, relevant causality assessment factors and the reporting of clinical reasoning and/or systematic causality assessment methods were extracted.ResultsIn all, 72 publications were included, reporting 134 supplements and 37 different AEs in 97 patients (85% males; median age: 30 years [range: 14-60]). Information regarding previous health and regular prescription drugs was not presented in 30% (29/97) and 46% (45/97) of cases, respectively. In 23% (22/97) of the cases, no alternative cause was mentioned. Clinical reasoning was identified in 63% (61/97), and in 13% (8/61) of these, a systematic causality assessment method was applied. In cases with clinical reasoning, a theoretic rationale (92% vs 78%, P = 0.05), a description of previous cases (90% vs 72%, P = 0.021) and body fluid analysis (18% vs 3%, P = 0.027) were reported to a greater extent. Among cases with clinical reasoning, the application of a systematic causality assessment method captured additional important aspects: use of medication (100% vs 55%, P = 0.015), alcohol use (88% vs 43%, P = 0.020) and illicit drug use (88% vs 40%, P = 0.011).ConclusionsIn published case reports where sports nutrition supplements were suspected to have caused AEs, essential factors for causality assessment were left out in a non-negligible proportion. Clinical reasoning was identified in most cases whereas a systematic causality assessment method was applied in a minority. Factors of importance for causality assessment were reported to a greater extent in cases including clinical reasoning, and the application of a systematic causality assessment method captured additional aspects of importance.

Project description:BackgroundSince 2004, Brazil has had a national policy for occupational health and safety. This policy means companies' tax burden is altered according to the numbers of work-related accidents and ill-health amongst their workers. In 2010, a multiplication factor was introduced to this policy, called the Accident Prevention Factor. The idea of this new multiplication factor is to encourage individual employers to take initiatives to prevent accidents and ill health in the workplace. This study was designed to investigate the incidence of work-related accidents and ill-health in Brazil according to their causes, their severity, and the economic activity in which they occur, and to compare the data before and after the introduction of the Accident Prevention Factor.MethodsAn ecological study was conducted by analyzing the time series of work-related accidents/ill-health between 2008 and 2014 from the Brazilian social security system (Previdência Social) statistical yearbooks. Incidences were calculated per cause, economic activity, and severity of the accident/ill-health. Data from before and after the introduction of the Accident Prevention Factor were compared using the Mann-Whitney test per cause and per economic activity. Statistical analyses were made using the SPSS software, with significance set at 5%.ResultsA reduction in the incidence of work-related accidents/ill-health was found across all the groups of causes analyzed, except for the groups "external causes of morbidity and mortality" and "factors influencing health status and contact with health services." Greater reductions were found for diseases of the musculoskeletal system and connective tissue and diseases of the nervous system. Reductions in work-related accidents/ill-health were found in the different economic activities and in the different severity groups. The highest reduction after the introduction of the Accident Prevention Factor was in manufacturing and production (p < 0.05).ConclusionsOverall, the incidence of accidents/ill-health was found to be on decline, except those with external causes of morbidity and mortality and those involving factors influencing health status and contact with health services. The biggest reduction was found in manufacturing and production. However, generally speaking progress still needs to be made in accident prevention and occupational health across a whole range of work environments.

Dataset Information

A Method to Extract Causality for Safety Events in Chemical Accidents from Fault Trees and Accident Reports.

Publications

A Method to Extract Causality for Safety Events in Chemical Accidents from Fault Trees and Accident Reports.

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