Classifying neck pain status using scalar and functional biomechanical variables – development of a method using functional data boosting

Liew, Bernard X. W.; Rügamer, David; Stöcker, Almond; Nunzio, Alessandro Marco De

doi:10.1016/j.gaitpost.2019.12.008

Cited by 14 publications

(7 citation statements)

References 30 publications

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“…Out of the total of 27 studies that were identified for the topic of pain, 14 used a prospective cohort design, 197 198 199 200 201 202 203 204 205 206 207 208 209 210 11 used an observational design, 200 204 205 207 210 211 212 213 214 215 216 6 used a retrospective cohort design, 211 214 215 217 218 219 4 used a randomized control trial, 201 212 220 221 1 used a cross-sectional design, 222 and 1 used mixed methods. 223 Most studies used questionnaire/survey data, but eight used administrative databases, 206 207 208 210 212 220 221 222 seven used mobile devices/sensors, 200 203 204 205 210 216 220 and four used a data warehouse or registry. 198 203 208 214 Study populations were mostly done with adults in the outpatient setting but four were inpatient 197 201 211 223 and one was done with a pediatric population.…”

Section: Resultsmentioning

confidence: 99%

“…Studies explored various outcomes including surgical applications such as determination of postsurgical measures based on residual pain, 197 predicting patellofemoral pain 1 year after intervention, 201 predicting neuropathic pain, 202 predicting chronic pain of 7 to 10 years into the future, 217 predicting complex regional pain syndrome, 207 predicting pain relief for knee osteoarthritis patients, 209 detection of pain, 210 214 216 222 and pain intensity estimation/classification. 205 213 215 220 Other outcomes focused on pain as a predictor of anxiety and depression, coronary heart disease, 199 health status, 218 noncancer pain as predictor of brain aging, 208 and length of stay. 211 For patients with low back pain, societal cost, 212 and clinical and sociodemographic predictors of increased disability 221 have been studied.…”

Section: Resultsmentioning

confidence: 99%

“…Some outcomes focused on the data science method as a clinical tool such as NLP of pain context from clinical notes. 219 223 There were several novel data sources included, such as the use of physiologic signals from electroencephalograms (EEG), 213 electromyography, 204 220 spectrogram, 205 electrodermal activity, 216 sensor data, 200 MRIs, 198 208 214 222 kinematics/motion data, 203 210 220 and medical images. 197 198 208 210 214 215 222…”

Section: Resultsmentioning

confidence: 99%

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Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

et al. 2022

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Background The term “data science” encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications. Objectives This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature. Methods We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care–acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture. Results Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing. Conclusion This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

et al. 2022

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“…Functional variables are typically temporal and/or spatial variables, where each observation for each variable can take on multiple values [33]. The most common example of functional variables would be kinematic and muscle activation data, which are temporal variables [34,35]. Less common functional variables are cortical activation patterns and radiological images [36,37].…”

Section: Discussionmentioning

confidence: 99%

Machine learning versus logistic regression for prognostic modelling in individuals with non-specific neck pain

et al. 2022

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Purpose Prognostic models play an important clinical role in the clinical management of neck pain disorders. No study has compared the performance of modern machine learning (ML) techniques, against more traditional regression techniques, when developing prognostic models in individuals with neck pain. Methods A total of 3001 participants suffering from neck pain were included into a clinical registry database. Three dichotomous outcomes of a clinically meaningful improvement in neck pain, arm pain, and disability at 3 months follow-up were used. There were 26 predictors included, five numeric and 21 categorical. Seven modelling techniques were used (logistic regression, least absolute shrinkage and selection operator [LASSO], gradient boosting [Xgboost], K nearest neighbours [KNN], support vector machine [SVM], random forest [RF], and artificial neural networks [ANN]). The primary measure of model performance was the area under the receiver operator curve (AUC) of the validation set. Results The ML algorithm with the greatest AUC for predicting arm pain (AUC = 0.765), neck pain (AUC = 0.726), and disability (AUC = 0.703) was Xgboost. The improvement in classification AUC from stepwise logistic regression to the best performing machine learning algorithms was 0.081, 0.103, and 0.077 for predicting arm pain, neck pain, and disability, respectively. Conclusion The improvement in prediction performance between ML and logistic regression methods in the present study, could be due to the potential greater nonlinearity between baseline predictors and clinical outcome. The benefit of machine learning in prognostic modelling may be dependent on factors like sample size, variable type, and disease investigated.

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“…These properties make CWB a powerful method at the intersection of (explainable) statistical modelling and (black-box prediction) machine learning. For this reason, CWB is frequently used in medical research, e.g., for oral cancer prediction (Saintigny et al, 2011), detection of synchronization in bioelectrical signals (Rügamer et al, 2018), or classifying pain syndromes (Liew et al, 2020). In contrast, many other gradient boosting methods such as XGBoost (Chen and Guestrin, 2016) solely focus on predictive performance and (mainly) use tree-based base learners with higher-order interactions.…”

Section: Introductionmentioning

confidence: 99%

Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization

Schalk¹,

Bischl²,

Rügamer³

2021

Preprint

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Componentwise boosting (CWB), also known as model-based boosting, is a variant of gradient boosting that builds on additive models as base learners to ensure interpretability. CWB is thus often used in research areas where models are employed as tools to explain relationships in data. One downside of CWB is its computational complexity in terms of memory and runtime. In this paper, we propose two techniques to overcome these issues without losing the properties of CWB: feature discretization of numerical features and incorporating Nesterov momentum into functional gradient descent. As the latter can be prone to early overfitting, we also propose a hybrid approach that prevents a possibly diverging gradient descent routine while ensuring faster convergence. We perform extensive benchmarks on multiple simulated and real-world data sets to demonstrate the improvements in runtime and memory consumption while maintaining state-of-the-art estimation and prediction performance.

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Classifying neck pain status using scalar and functional biomechanical variables – development of a method using functional data boosting

Cited by 14 publications

References 30 publications

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

Machine learning versus logistic regression for prognostic modelling in individuals with non-specific neck pain

Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization

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