scholarly journals Machine Learning Approaches to Predict Chronic Lower Back Pain in People Aged over 50 Years

Medicina ◽  
2021 ◽  
Vol 57 (11) ◽  
pp. 1230
Author(s):  
Jae-Geum Shim ◽  
Kyoung-Ho Ryu ◽  
Eun-Ah Cho ◽  
Jin Hee Ahn ◽  
Hong Kyoon Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Back Pain ◽  
Lower Back Pain ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Gradient Boosting ◽  
Support Vector ◽  
Chronic Lower Back Pain ◽  
Machine Learning Classification ◽  
Lower Back

Background and Objectives: Chronic lower back pain (LBP) is a common clinical disorder. The early identification of patients who will develop chronic LBP would help develop preventive measures and treatment. We aimed to develop machine learning models that can accurately predict the risk of chronic LBP. Materials and Methods: Data from the Sixth Korea National Health and Nutrition Examination Survey conducted in 2014 and 2015 (KNHANES VI-2, 3) were screened for selecting patients with chronic LBP. LBP lasting >30 days in the past 3 months was defined as chronic LBP in the survey. The following classification models with machine learning algorithms were developed and validated to predict chronic LBP: logistic regression (LR), k-nearest neighbors (KNN), naïve Bayes (NB), decision tree (DT), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), and artificial neural network (ANN). The performance of these models was compared with respect to the area under the receiver operating characteristic curve (AUROC). Results: A total of 6119 patients were analyzed in this study, of which 1394 had LBP. The feature selected data consisted of 13 variables. The LR, KNN, NB, DT, RF, GBM, SVM, and ANN models showed performances (in terms of AUROCs) of 0.656, 0.656, 0.712, 0.671, 0.699, 0.660, 0.707, and 0.716, respectively, with ten-fold cross-validation. Conclusions: In this study, the ANN model was identified as the best machine learning classification model for predicting the occurrence of chronic LBP. Therefore, machine learning could be effectively applied in the identification of populations at high risk of chronic LBP.

scholarly journals Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4068
Author(s):  
Xu Huang ◽  
Mirna Wasouf ◽  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Machine Learning ◽  
Search Algorithm ◽  
Weather Conditions ◽  
Prediction Performance ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Support Vector ◽  
Self Healing ◽  
Artificial Neural Network Ann

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

scholarly journals Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements

2021 ◽  
Vol 10 (1) ◽  
pp. 42
Author(s):  
Kieu Anh Nguyen ◽  
Walter Chen ◽  
Bor-Shiun Lin ◽  
Uma Seeboonruang
Keyword(s):  
Machine Learning ◽  
Soil Erosion ◽  
Ensemble Methods ◽  
Machine Learning Algorithms ◽  
Multivariate Adaptive Regression Splines ◽  
Gradient Boosting ◽  
Support Vector ◽  
Ensemble Machine Learning ◽  
Boosting Method ◽  
Bagging Method

Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study.

scholarly journals Effects of Prolonged Sitting with Slumped Posture on Trunk Muscular Fatigue in Adolescents with and without Chronic Lower Back Pain

Medicina ◽  
2020 ◽  
Vol 57 (1) ◽  
pp. 3
Author(s):  
Kyoung-Sim Jung ◽  
Jin-Hwa Jung ◽  
Tae-Sung In ◽  
Hwi-Young Cho
Keyword(s):  
Back Pain ◽  
Lower Back Pain ◽  
Transversus Abdominis ◽  
Muscular Fatigue ◽  
Trunk Muscles ◽  
Control Group ◽  
Chronic Lower Back Pain ◽  
Median Frequency ◽  
Prolonged Sitting ◽  
Lower Back

Background and Objectives: This study investigated the effects of prolonged sitting on trunk muscular fatigue and discomfort in participants with and without chronic lower back pain (LBP). Material and Methods: This study included 15 patients with LBP and 15 healthy controls. All participants were instructed to sit on a height-adjustable chair with their knee and hip joints bent at 90° for 30 min, in slumped sitting postures. Surface electromyography was used to assess the median frequency of the internal obliques (IO)/transversus abdominis (TrA) and multifidus (MF) muscles. Perceived discomfort was measured using a Borg category ratio-scale. Median frequency of the trunk muscles and perceived discomfort after 30 min of sitting were compared with baseline. Result: There were no significant differences within the group and between both groups in the median frequency of bilateral IO and MF muscles. The LBP group showed significantly greater perceived discomfort after prolonged sitting, as compared to the control group. Conclusions: Prolonged sitting with slumped posture could increase the risk of experiencing lower back discomfort.

scholarly journals Therapeutic Effectiveness of AxioBionics Wearable Therapy Pain Management System in Patients with Chronic Lower Back Pain

2021 ◽  
Vol 14 ◽  
pp. 117954412199377
Author(s):  
Philip Muccio ◽  
Josh Schueller ◽  
Miriam van Emde Boas ◽  
Norm Howe ◽  
Edward Dabrowski ◽  
...  
Keyword(s):  
Back Pain ◽  
Pain Management ◽  
Lower Back Pain ◽  
Chronic Lower Back Pain ◽  
Analog Scale ◽  
System A ◽  
System Reduction ◽  
Lower Back ◽  
Muscular Function

Chronic lower back pain is one of the most common medical conditions leading to a significant decrease in quality of life. This study retrospectively analyzed whether the AxioBionics Wearable Therapy Pain Management (WTPM) System, a customized and wearable electrical stimulation device, alleviated chronic lower back pain, and improved muscular function. This study assessed self-reported pain levels using the visual analog scale before and during the use of the AxioBionics WTPM System when performing normal activities such as sitting, standing, and walking (n = 69). Results showed that both at-rest and activity-related pain were significantly reduced during treatment with the AxioBionics WTPM System (% reduction in pain: 64% and 60%, respectively; P < .05). Thus, this study suggests that the AxioBionics WTPM System is efficacious in treating chronic lower back pain even when other therapies have failed to sufficiently decrease reported pain levels.

Cognitive profile and mild cognitive impairment in people with chronic lower back pain

2021 ◽  
Vol 151 ◽  
pp. 105737
Author(s):  
Emily J. Corti ◽  
Natalie Gasson ◽  
Andrea M. Loftus
Keyword(s):  
Cognitive Impairment ◽  
Back Pain ◽  
Mild Cognitive Impairment ◽  
Lower Back Pain ◽  
Cognitive Profile ◽  
Chronic Lower Back Pain ◽  
Lower Back

Listening to the body and talking to myself – the impact of chronic lower back pain: A qualitative study

2010 ◽  
Vol 47 (5) ◽  
pp. 586-592 ◽  
Author(s):  
Marie Crowe ◽  
Lisa Whitehead ◽  
Mary Jo Gagan ◽  
G. David Baxter ◽  
Avin Pankhurst ◽  
...  
Keyword(s):  
Back Pain ◽  
Qualitative Study ◽  
Lower Back Pain ◽  
The Body ◽  
Chronic Lower Back Pain ◽  
Lower Back ◽  
The Impact

scholarly journals Emotional and Neurobehavioural Status in Chronic Pain Patients

2011 ◽  
Vol 16 (1) ◽  
pp. 41-43 ◽  
Author(s):  
He Shuchang ◽  
He Mingwei ◽  
Jia Hongxiao ◽  
Wu Si ◽  
Yang Xing ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Back Pain ◽  
Lower Back Pain ◽  
Mood State ◽  
Control Group ◽  
Chronic Lower Back Pain ◽  
Control Subjects ◽  
Lower Back ◽  
Chronic Pain Patients ◽  
Pain Patients

OBJECTIVE: To investigate the emotional and neurobehavioural status of patients suffering from chronic pain.METHODS: Fifteen male patients with chronic lower back pain and 15 healthy control subjects were studied for approximately six months. Pain was measured using a visual analogue scale. The WHO Neurobehavioral Core Test Battery (NCTB) was used to assess neurobehavioural effects of environmental and occupational exposures.RESULTS: Visual analogue scale results demonstrated a modest range of reported pain (mean [± SD] 62.0±10.8) in chronic pain patients, whereas control subjects reported no measurable pain. With the NCTB, it was found that scores of negative mood state, including anger-hostility, depression-dejection, fatigue-inertia and tension-anxiety in pain patients were significantly higher than scores in the control subjects. By contrast, scores of positive mood state (vigour-activity) in chronic pain patients were lower than those in the control group. The NCTB scores of the Santa Ana Dexterity and Pursuit Aiming II tests in chronic lower back pain patients were lower than those of the control group. Scores for other NCTB sub-tests, including the Digit Span, Benton Visual Retention and Digit Symbol tests, were not significantly different compared with controls.CONCLUSIONS: Chronic lower back pain patients had more negative mood and less positive mood than controls. These patients also demonstrated neuromotor deficits in coordination and reaction time. Further studies are required to examine possible neurological mechanisms and research potential intervention strategies for patients suffering from chronic pain.

Sleep efficiency and physical performance differences between pain-free individuals and individuals with chronic lower back pain

2021 ◽  
Vol 22 (5) ◽  
pp. 610-611
Author(s):  
Katherine O'Neal ◽  
Deanna Rumble ◽  
Demario Overstreet ◽  
Terence Penn ◽  
Pamela Jackson ◽  
...  
Keyword(s):  
Back Pain ◽  
Physical Performance ◽  
Lower Back Pain ◽  
Sleep Efficiency ◽  
Chronic Lower Back Pain ◽  
Lower Back ◽  
Performance Differences
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