Neck and Shoulder Pain with Scapular Dyskinesis in Computer Office Workers.

Background and Objectives: Computer office workers spend long periods in front of a computer, and neck and shoulder pain are common. Scapular dyskinesis (SD) is associated with neck and shoulder pain. However, SD in computer office workers has not been elucidated. We aimed to investigate the prevalence of SD, neck and shoulder pain, disability, and working hours in computer office workers. Materials and Methods: In total, 109 computer office workers participated in this study. The results of a scapular dyskinesis test (SDT), lateral scapular slide test (LSST), neck disability index (NDI), shoulder pain and disability index (SPADI), visual analog scale (VAS) scores of the neck and shoulder, and working hours were recorded. Results: Ninety-eight computer office workers (89.9%) had SD. Computer office workers with SD had significantly higher NDI (p = 0.019), neck VAS (p = 0.041), and dominant shoulder VAS scores (p = 0.043). The LSST results showed a significantly greater distance (p = 0.016) in participants with SD. Conclusions: The prevalence of SD was very high in computer office workers, and neck and shoulder pain were more prevalent in workers with obvious SD.

GenHPF: General Healthcare Predictive Framework for Multi-task Multi-source Learning.

Despite the remarkable progress in the development of predictive models for healthcare, applying these algorithms on a large scale has been challenging. Algorithms trained on a particular task, based on specific data formats available in a set of medical records, tend to not generalize well to other tasks or databases in which the data fields may differ. To address this challenge, we propose General Healthcare Predictive Framework (GenHPF), which is applicable to any EHR with minimal preprocessing for multiple prediction tasks. GenHPF resolves heterogeneity in medical codes and schemas by converting EHRs into a hierarchical textual representation while incorporating as many features as possible. To evaluate the efficacy of GenHPF, we conduct multi-task learning experiments with single-source and multi-source settings, on three publicly available EHR datasets with different schemas for 12 clinically meaningful prediction tasks. Our framework significantly outperforms baseline models that utilize domain knowledge in multi-source learning, improving average AUROC by 1.2%P in pooled learning and 2.6%P in transfer learning while also showing comparable results when trained on a single EHR dataset. Furthermore, we demonstrate that self-supervised pretraining using multi-source datasets is effective when combined with GenHPF, resulting in a 0.6 pretraining. By eliminating the need for preprocessing and feature engineering, we believe that this work offers a solid framework for multi-task and multi-source learning that can be leveraged to speed up the scaling and usage of predictive algorithms in healthcare.1.

MaDeNet: Disentangling Individuality of EEG Signals through Feature Space Mapping and Detachment.

The cross-subject variability, or individuality, of electroencephalography (EEG) signals often has been an obstacle to extracting target-related information from EEG signals for classification of subjects' perceptual states. In this paper, we propose a deep learning-based EEG classification approach, which learns feature space mapping and performs individuality detachment to reduce subject-related information from EEG signals and maximize classification performance. Our experiment on EEG-based video classification shows that our method significantly improves the classification accuracy.

Emotional EEG classification using connectivity features and convolutional neural networks.

Convolutional neural networks (CNNs) are widely used to recognize the user's state through electroencephalography (EEG) signals. In the previous studies, the EEG signals are usually fed into the CNNs in the form of high-dimensional raw data. However, this approach makes it difficult to exploit the brain connectivity information that can be effective in describing the functional brain network and estimating the perceptual state of the user. We introduce a new classification system that utilizes brain connectivity with a CNN and validate its effectiveness via the emotional video classification by using three different types of connectivity measures. Furthermore, two data-driven methods to construct the connectivity matrix are proposed to maximize classification performance. Further analysis reveals that the level of concentration of the brain connectivity related to the emotional property of the target video is correlated with classification performance.