Prediction Effects of Personal, Psychosocial, and Occupational Risk Factors on Low Back Pain Severity Using Artificial Neural Networks Approach in Industrial Workers

Darvishi, Ebrahim; Khotanlou, Hassan; Khoubi, Jamshid; Giahi, Omid; Mahdavi, Neda

doi:10.1016/j.jmpt.2017.03.012

Cited by 16 publications

(27 citation statements)

References 20 publications

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“…Overall, the sample sizes ranged from 10 to 34,589 people. The populations consisted of 16 studies that looked at chronic LBP 19,20,24,28,29,31,36,37,39,42,[54][55][56][57]62,64 , two acute LBP 27,30 , one recurrent 22 , one lumbar spinal stenosis 21 , two surgical 46,61 , nine other (mixed samples) 35,38,40,41,48,51,53,65,66 and 17 were unclear (LBP type not defined) 23,25,26,[32][33][34][43][44][45]47,49,50,52,[58][59][60]63 . Ten studies did not report training and testing of the data sets 26,29,...…”

Section: Machine Learningmentioning

confidence: 99%

“…Classification of LBP was assessed in 25 studies, all of which attempted binary classification to detect the presence of LBP or not 19,20,[23][24][25]28,29,[31][32][33]37,[40][41][42]44,47,49,50,[53][54][55]57,[62][63][64] . One study classified golfers with and without LBP based on electromyography and golf kinematic data using a support vector machine (multilayer perceptron with one layer, where input data are placed into vector spaces) 12 with 100% accuracy 47 .…”

Section: Machine Learningmentioning

confidence: 99%

“…The accuracy of these studies for classifying LBP ranged from 83 to 92%. One study classified LBP in 160 industrial workers on personal, psychosocial and occupational factors using an artificial neural network (ANN; programs that operate with multiple processing elements or neurons to determine the strength of connections between nodes) with 92% accuracy 25 . The next largest study was one in 34,589 people and showed an ANN on lifestyle and psychosocial characteristics classified LBP with an area under the curve of 0.75.…”

Section: Machine Learningmentioning

confidence: 99%

“…An overview of risk of bias from the NOS is shown in Table 2. Overall, 29 studies 20,[23][24][25]28,29,32,34,38,[40][41][42]44,45,[47][48][49][50][53][54][55]57,58,[61][62][63][64][65][66] were case−control while eight 21,22,27,30,31,37,46,52 were cohort studies. Eleven studies did not fit the criteria for case−control or cohort studies and did not undergo the risk of bias assessment 19,26,33,35,36,39,43,51,56,59,60 .…”

Section: Machine Learningmentioning

confidence: 99%

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Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews

et al. 2020

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Artificial intelligence and machine learning (AI/ML) could enhance the ability to detect patterns of clinical characteristics in lowback pain (LBP) and guide treatment. We conducted three systematic reviews to address the following aims: (a) review the status of AI/ML research in LBP, (b) compare its status to that of two established LBP classification systems (STarT Back, McKenzie). AI/ML in LBP is in its infancy: 45 of 48 studies assessed sample sizes <1000 people, 19 of 48 studies used ≤5 parameters in models, 13 of 48 studies applied multiple models and attained high accuracy, 25 of 48 studies assessed the binary classification of LBP versus no-LBP only. Beyond the 48 studies using AI/ML for LBP classification, no studies examined use of AI/ML in prognosis prediction of specific subgroups , and AI/ML techniques are yet to be implemented in guiding LBP treatment. In contrast, the STarT Back tool has been assessed for internal consistency, test−retest reliability, validity, pain and disability prognosis, and influence on pain and disability treatment outcomes. McKenzie has been assessed for inter-and intra-tester reliability, prognosis, and impact on pain and disability outcomes relative to other treatments. For AI/ML methods to contribute to the refinement of LBP (sub-)classification and guide treatment allocation, large data sets containing known and exploratory clinical features should be examined. There is also a need to establish reliability, validity, and prognostic capacity of AI/ML techniques in LBP as well as its ability to inform treatment allocation for improved patient outcomes and/or reduced healthcare costs.

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Section: Machine Learningmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

See 2 more Smart Citations

Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews

et al. 2020

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“…The work of Asensio-Cuesta et al [42] and Zurada et al [43] analyzed the effect of specific industrial lifting jobs, and workplace design on lower back pain. The work of Darvishi et al [44] estimated the probability of lower back pain from occupational incidents in industrial units with a prediction accuracy rate of 92% in train and test data, based on sixteen risk factors of lower back pain in such injuries. The work of Aliabadi et al [45] predicted the hearing loss threshold in high-noise industrial workplaces in a steel factory, and analyzed the effects of occupational exposure risk factors on determining the hearing loss threshold.…”

Section: Artificial Neural Network In Occupational Safetymentioning

confidence: 99%

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

2019

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The grain handling industry plays a significant role in U.S. agriculture by storing, distributing, and processing a variety of agricultural commodities. Commercial grain elevators are hazardous agro-manufacturing work environments where workers are prone to severe injuries, due to the nature of the activities and workplace. Safety incidents in agro-manufacturing operations generally arise from a combination of factors, rather than a single cause, therefore, research on occupational incidents must look deeper into identifying the underlying causes, through the application of advanced analyses methods. In occupational safety, it is possible to estimate and predict probability of safety risks through developing artificial neural network predictive models. Due to the significance of safety risk assessment in the design and prioritization of effective prevention measures, this study aimed at classifying and predicting causes of occupational incidents in grain elevator agro-manufacturing operations in the Midwest region of the United States. Workers’ compensation claims data, from 2008 to 2016, were utilized for training multilayer perceptron (MLP) and radial basis function (RBF) neural networks. Both MLP and RBF models could predict the probability of safety risks with a high overall accuracy of 60%, 61%. Based on values of AUC (area under the curve) from the ROC (receiving operating charts), both models predicted the probability of individual safety risks with a high accuracy rate of between 71.5% and 99.2%. In addition, sensitivity analysis showed that nature of injury is the most significant determinant of safety risks probability, along with type of injury. The novelty of this study is the use of the artificial neural network methodology to analyze multi-level causes of occupational incidents as the sources of safety risks in bulk storage facilities. The results confirm that artificial neural networks are useful in safety risk estimation, and identifying the incidents’ risk factors. The implementation of safety measures in grain elevators can help in preventing occupational injuries, saving lives, and reducing the occurrence and severity of such incidents in industrial work environments.

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Predicting Absenteeism and Temporary Disability Using Machine Learning: a Systematic Review and Analysis

et al. 2020

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Prediction Effects of Personal, Psychosocial, and Occupational Risk Factors on Low Back Pain Severity Using Artificial Neural Networks Approach in Industrial Workers

Cited by 16 publications

References 20 publications

Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews

Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

Predicting Absenteeism and Temporary Disability Using Machine Learning: a Systematic Review and Analysis

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