Transformer Encoder with Multiscale Deep Learning for Pain Classification Using Physiological Signals

Pain is a serious worldwide health problem that affects a vast proportion of the population. For efficient pain management and treatment, accurate classification and evaluation of pain severity are necessary. However, this can be challenging as pain is a subjective sensation-driven experience. Traditional techniques for measuring pain intensity, e.g. self-report scales, are susceptible to bias and unreliable in some instances. Consequently, there is a need for more objective and automatic pain intensity assessment strategies. In this paper, we develop PainAttnNet (PAN), a novel transfomer-encoder deep-learning framework for classifying pain intensities with physiological signals as input. The proposed approach is comprised of three feature extraction architectures: multiscale convolutional networks (MSCN), a squeeze-and-excitation residual network (SEResNet), and a transformer encoder block. On the basis of pain stimuli, MSCN extracts short- and long-window information as well as sequential features. SEResNet highlights relevant extracted features by mapping the interdependencies among features. The third module employs a transformer encoder consisting of three temporal convolutional networks (TCN) with three multi-head attention (MHA) layers to extract temporal dependencies from the features. Using the publicly available BioVid pain dataset, we test the proposed PainAttnNet model and demonstrate that our outcomes outperform state-of-the-art models. These results confirm that our approach can be utilized for automated classification of pain intensity using physiological signals to improve pain management and treatment

Uncertainty Quantification in Neural-Network Based Pain Intensity Estimation

Improper pain management can lead to severe physical or mental consequences, including suffering, and an increased risk of opioid dependency. Assessing the presence and severity of pain is imperative to prevent such outcomes and determine the appropriate intervention. However, the evaluation of pain intensity is challenging because different individuals experience pain differently. To overcome this, researchers have employed machine learning models to evaluate pain intensity objectively. However, these efforts have primarily focused on point estimation of pain, disregarding the inherent uncertainty and variability present in the data and model. Consequently, the point estimates provide only partial information for clinical decision-making. This study presents a neural network-based method for objective pain interval estimation, incorporating uncertainty quantification. This work explores three algorithms: the bootstrap method, lower and upper bound estimation (LossL) optimized by genetic algorithm, and modified lower and upper bound estimation (LossS) optimized by gradient descent algorithm. Our empirical results reveal that LossS outperforms the other two by providing a narrower prediction interval. As LossS outperforms, we assessed its performance in three different scenarios for pain assessment: (1) a generalized approach (single model for the entire population), (2) a personalized approach (separate model for each individual), and (3) a hybrid approach (separate model for each cluster of individuals). Our findings demonstrate the hybrid approach's superior performance, with notable practicality in clinical contexts. It has the potential to be a valuable tool for clinicians, enabling objective pain intensity assessment while taking uncertainty into account. This capability is crucial in facilitating effective pain management and reducing the risks associated with improper treatment.Comment: 26 pages, 5 figures, 9 table

Review and Analysis of Pain Research Literature through Keyword Co-occurrence Networks

Pain is a significant public health problem as the number of individuals with a history of pain globally keeps growing. In response, many synergistic research areas have been coming together to address pain-related issues. This work conducts a review and analysis of a vast body of pain-related literature using the keyword co-occurrence network (KCN) methodology. In this method, a set of KCNs is constructed by treating keywords as nodes and the co-occurrence of keywords as links between the nodes. Since keywords represent the knowledge components of research articles, analysis of KCNs will reveal the knowledge structure and research trends in the literature. This study extracted and analyzed keywords from 264,560 pain-related research articles indexed in IEEE, PubMed, Engineering Village, and Web of Science published between 2002 and 2021. We observed rapid growth in pain literature in the last two decades: the number of articles has grown nearly threefold, and the number of keywords has grown by a factor of 7. We identified emerging and declining research trends in sensors/methods, biomedical, and treatment tracks. We also extracted the most frequently co-occurring keyword pairs and clusters to help researchers recognize the synergies among different pain-related topics

Trends in Adopting Industry 4.0 for Asset Life Cycle Management for Sustainability: A Keyword Co-Occurrence Network Review and Analysis

With the potential of Industry 4.0 technologies to enable sustainable manufacturing, asset life cycle management (ALCM) has been gaining increasing attention in recent years. This study explores the evolution of Industry 4.0 technology applications to sustainable ALCM from 2002 to 2021. This study is based on keywords collected from 3896 ALCM-related scientific articles published in the Web of Science, IEEE Xplore and Engineering Village between 2002 and 2021. We conducted a review analysis of these keywords using a network science-based methodology, which unlike the tedious traditional literature review methods, gives the capability to analyze a huge number of scientific articles efficiently. We built keyword co-occurrence networks (KCNs) from the keywords and explored the network characteristics to uncover meaningful knowledge patterns, knowledge components, knowledge structure, and research trends in the body of literature at the intersection of ALCM and Industry 4.0. The network modeling and data analysis results identify the emerging Industry 4.0-related keywords in ALCM literature and indicate the recent explosion of connectivity among keywords. We found IoT, predictive maintenance and big data to be the top three most popular Industry 4.0-related keywords in ALCM literature. Furthermore, this study maps relevant ALCM keywords in contemporary literature to the nine pillars of Industry 4.0 to help the responsible manufacturing community identify research trends and emerging technologies for sustainability

Analysis of pain research literature through keyword Co-occurrence networks.

Pain is a significant public health problem as the number of individuals with a history of pain globally keeps growing. In response, many synergistic research areas have been coming together to address pain-related issues. This work reviews and analyzes a vast body of pain-related literature using the keyword co-occurrence network (KCN) methodology. In this method, a set of KCNs is constructed by treating keywords as nodes and the co-occurrence of keywords as links between the nodes. Since keywords represent the knowledge components of research articles, analysis of KCNs will reveal the knowledge structure and research trends in the literature. This study extracted and analyzed keywords from 264,560 pain-related research articles indexed in IEEE, PubMed, Engineering Village, and Web of Science published between 2002 and 2021. We observed rapid growth in pain literature in the last two decades: the number of articles has grown nearly threefold, and the number of keywords has grown by a factor of 7. We identified emerging and declining research trends in sensors/methods, biomedical, and treatment tracks. We also extracted the most frequently co-occurring keyword pairs and clusters to help researchers recognize the synergies among different pain-related topics

Validity and Reliability of the Turkish Version of DSM-5 Level 2 Anxiety Scale (Child Form for 11-17 Years and Parent Form for 6-17 Years)

Introduction: This study aimed to assess the validity and reliability of the Turkish Version of DSM-5 Level 2 Anxiety Scale's child and parent forms

Evolution and long-term outcomes of combined immunodeficiency due to CARMIL2 deficiency

Background Biallelic loss-of-function mutations in CARMIL2 cause combined immunodeficiency associated with dermatitis, inflammatory bowel disease (IBD), and EBV-related smooth muscle tumors. Clinical and immunological characterizations of the disease with long-term follow-up and treatment options have not been previously reported in large cohorts. We sought to determine the clinical and immunological features of CARMIL2 deficiency and long-term efficacy of treatment in controlling different disease manifestations. Methods The presenting phenotypes, long-term outcomes, and treatment responses were evaluated prospectively in 15 CARMIL2-deficient patients, including 13 novel cases. Lymphocyte subpopulations, protein expression, regulatory T (Treg), and circulating T follicular helper (cT(FH)) cells were analyzed. Three-dimensional (3D) migration assay was performed to determine T-cell shape. Results Mean age at disease onset was 38 +/- 23 months. Main clinical features were skin manifestations (n = 14, 93%), failure to thrive (n = 10, 67%), recurrent infections (n = 10, 67%), allergic symptoms (n = 8, 53%), chronic diarrhea (n = 4, 27%), and EBV-related leiomyoma (n = 2, 13%). Skin manifestations ranged from atopic and seborrheic dermatitis to psoriasiform rash. Patients had reduced proportions of memory CD4(+) T cells, Treg, and cT(FH) cells. Memory B and NK cells were also decreased. CARMIL2-deficient T cells exhibited reduced T-cell proliferation and cytokine production following CD28 co-stimulation and normal morphology when migrating in a high-density 3D collagen gel matrix. IBD was the most severe clinical manifestation, leading to growth retardation, requiring multiple interventional treatments. All patients were alive with a median follow-up of 10.8 years (range: 3-17 years). Conclusion This cohort provides clinical and immunological features and long-term follow-up of different manifestations of CARMIL2 deficiency

Evolution and long-term outcomes of combined immunodeficiency due to CARMIL2 deficiency

Background Biallelic loss-of-function mutations in CARMIL2 cause combined immunodeficiency associated with dermatitis, inflammatory bowel disease (IBD), and EBV-related smooth muscle tumors. Clinical and immunological characterizations of the disease with long-term follow-up and treatment options have not been previously reported in large cohorts. We sought to determine the clinical and immunological features of CARMIL2 deficiency and long-term efficacy of treatment in controlling different disease manifestations. Methods The presenting phenotypes, long-term outcomes, and treatment responses were evaluated prospectively in 15 CARMIL2-deficient patients, including 13 novel cases. Lymphocyte subpopulations, protein expression, regulatory T (Treg), and circulating T follicular helper (cT(FH)) cells were analyzed. Three-dimensional (3D) migration assay was performed to determine T-cell shape. Results Mean age at disease onset was 38 +/- 23 months. Main clinical features were skin manifestations (n = 14, 93%), failure to thrive (n = 10, 67%), recurrent infections (n = 10, 67%), allergic symptoms (n = 8, 53%), chronic diarrhea (n = 4, 27%), and EBV-related leiomyoma (n = 2, 13%). Skin manifestations ranged from atopic and seborrheic dermatitis to psoriasiform rash. Patients had reduced proportions of memory CD4(+) T cells, Treg, and cT(FH) cells. Memory B and NK cells were also decreased. CARMIL2-deficient T cells exhibited reduced T-cell proliferation and cytokine production following CD28 co-stimulation and normal morphology when migrating in a high-density 3D collagen gel matrix. IBD was the most severe clinical manifestation, leading to growth retardation, requiring multiple interventional treatments. All patients were alive with a median follow-up of 10.8 years (range: 3-17 years). Conclusion This cohort provides clinical and immunological features and long-term follow-up of different manifestations of CARMIL2 deficiency