Mining social media data to investigate patient perceptions regarding DMARD pharmacotherapy for rheumatoid arthritis.

OBJECTIVES: We hypothesise that patients have a positive sentiment regarding biological/targeted synthetic disease modifying anti-rheumatic drugs (b/tsDMARDs) and a negative sentiment towards conventional synthetic agents (csDMARDs). We analysed discussions on social media platforms regarding DMARDs to understand the collective sentiment expressed towards these medications. METHODS: Treato analytics were used to download all available posts on social media about DMARDs in the context of rheumatoid arthritis. Strict filters ensured that user generated content was downloaded. The sentiment (positive or negative) expressed in these posts was analysed for each DMARD using sentiment analysis. We also analysed the reason(s) for this sentiment for each DMARD, looking specifically at efficacy and side effects. RESULTS: Computer algorithms analysed millions of social media posts and included 54 742 posts about DMARDs. We found that both classes had an overall positive sentiment. The ratio of positive to negative posts was higher for b/tsDMARDs (1.210) than for csDMARDs (1.048). Efficacy was the most commonly mentioned reason in posts with a positive sentiment and lack of efficacy was the most commonly mentioned reason for a negative sentiment. These were followed by the presence/absence of side effects in negative or positive posts, respectively. CONCLUSIONS: Public opinion on social media is generally positive about DMARDs. Lack of efficacy followed by side effects were the most common themes in posts with a negative sentiment. There are clear reasons why a DMARD generates a positive or negative sentiment, as the sentiment analysis technology becomes more refined, targeted studies could be done to analyse these reasons and allow clinicians to tailor DMARDs to match patient needs.

Electronic Skin Wearable Sensors for Detecting Lumbar-Pelvic Movements.

BACKGROUND: A nanomaterial-based electronic-skin (E-Skin) wearable sensor has been successfully used for detecting and measuring body movements such as finger movement and foot pressure. The ultrathin and highly sensitive characteristics of E-Skin sensor make it a suitable alternative for continuously out-of-hospital lumbar-pelvic movement (LPM) monitoring. Monitoring these movements can help medical experts better understand individuals' low back pain experience. However, there is a lack of prior studies in this research area. Therefore, this paper explores the potential of E-Skin sensors to detect and measure the anatomical angles of lumbar-pelvic movements by building a linear relationship model to compare its performance to clinically validated inertial measurement unit (IMU)-based sensing system (ViMove). METHODS: The paper first presents a review and classification of existing wireless sensing technologies for monitoring of body movements, and then it describes a series of experiments performed with E-Skin sensors for detecting five standard LPMs including flexion, extension, pelvic tilt, lateral flexion, and rotation, and measure their anatomical angles. The outputs of both E-Skin and ViMove sensors were recorded during each experiment and further analysed to build the comparative models to evaluate the performance of detecting and measuring LPMs. RESULTS: E-Skin sensor outputs showed a persistently repeating pattern for each movement. Due to the ability to sense minor skin deformation by E-skin sensor, its reaction time in detecting lumbar-pelvic movement is quicker than ViMove by ~1 s. CONCLUSIONS: E-Skin sensors offer new capabilities for detecting and measuring lumbar-pelvic movements. They have lower cost compared to commercially available IMU-based systems and their non-invasive highly stretchable characteristic makes them more comfortable for long-term use. These features make them a suitable sensing technology for developing continuous, out-of-hospital real-time monitoring and management systems for individuals with low back pain.

Understanding the drivers on medical workloads: an analysis of spectators at the Australian Football League.

OBJECTIVE: The present study was designed to further understand the psychosocial drivers of crowds impacting on the demand for healthcare. This involved analysing different spectator crowds for medical usage at mass gatherings; more specifically, did different football team spectators (of the Australian Football League) generate different medical usage rates. METHODS: In total, 317 games were analysed from 10 venues over 2 years. Data were analysed by the ANOVA and Pearson correlation tests. RESULTS; Spectators who supported different football teams generated statistically significant differences in patient presentation rates (PPR) (F15, 618=1.998, P=0.014). The present study confirmed previous findings that there is a positive correlation between the crowd size and PPR at mass gatherings but found a negative correlation between density and PPR (r = -0.206, n=317, P<0.0005). CONCLUSIONS: The present study has attempted to scientifically explore psychosocial elements of crowd behaviour as a driver of demand for emergency medical care. In measuring demand for emergency medical services there is a need to develop a more sophisticated understanding of a variety of drivers in addition to traditional metrics such as temperature, crowd size and other physical elements. In this study we saw that spectators who supported different football teams generated statistically significant differences in PPR. What is known about this topic? Understanding the drivers of emergency medical care is most important in the mass gathering setting. There has been minimal analysis of psychological 'crowd' variables. What does this paper add? This study explores the psychosocial impact of supporting a different team on the PPR of spectators at Australian Football League matches. The value of collecting and analysing these types of data sets is to support more balanced planning, better decision support and knowledge management, and more effective emergency medical demand management. What are the implications for practitioners? This information further expands the body of evidence being created to understand the drivers of emergency medical demand and usage. In addition, it supports the planning and management of emergency medical and health-related requirements by increasing our understanding of the effect of elements of 'crowd' that impact on medical usage and emergency healthcare.

A Graph-Based Hierarchical Attention Model for Movement Intention Detection from EEG Signals.

An EEG-based Brain-Computer Interface (BCI) is a system that enables a user to communicate with and intuitively control external devices solely using the user's intentions. Current EEG-based BCI research usually involves a subject-specific adaptation step before a BCI system is ready to be employed by a new user. However, the subject-independent scenario, in which a well-trained model can be directly applied to new users without pre-calibration, is particularly desirable yet rarely explored. Considering this critical gap, our focus in this paper is the subject-independent scenario of EEG-based human intention recognition. We present a G raph-based H ierarchical A ttention M odel (G-HAM) that utilizes the graph structure to represent the spatial information of EEG sensors and the hierarchical attention mechanism to focus on both the most discriminative temporal periods and EEG nodes. Extensive experiments on a large EEG dataset containing 105 subjects indicate that our model is capable of exploiting the underlying invariant EEG patterns across different subjects and generalizing the patterns to new subjects with better performance than a series of state-of-the-art and baseline approaches.