Objective Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media.Methods We introduce ADRMine, a machine learning-based concept extraction system that uses conditional random fields (CRFs). ADRMine utilizes a variety of features, including a novel feature for modeling words’ semantic similarities. The similarities are modeled by clustering words based on unsupervised, pretrained word representation vectors (embeddings) generated from unlabeled user posts in social media using a deep learning technique.Results ADRMine outperforms several strong baseline systems in the ADR extraction task by achieving an F-measure of 0.82. Feature analysis demonstrates that the proposed word cluster features significantly improve extraction performance.Conclusion It is possible to extract complex medical concepts, with relatively high performance, from informal, user-generated content. Our approach is particularly scalable, suitable for social media mining, as it relies on large volumes of unlabeled data, thus diminishing the need for large, annotated training data sets.

Objective Automatic monitoring of Adverse Drug Reactions (ADRs), defined as adverse patient outcomes caused by medications, is a challenging research problem that is currently receiving significant attention from the medical informatics community. In recent years, user-posted data on social media, primarily due to its sheer volume, has become a useful resource for ADR monitoring. Research using social media data has progressed using various data sources and techniques, making it difficult to compare distinct systems and their performances. In this paper, we perform a methodical review to characterize the different approaches to ADR detection/extraction from social media, and their applicability to pharmacovigilance. In addition, we present a potential systematic pathway to ADR monitoring from social media. Methods We identified studies, describing approaches for ADR detection from social media from the Medline, Embase, Scopus and Web of Science databases, and the Google Scholar search engine. Studies that met our inclusion criteria were those that attempted to utilize ADR information posted by users on any publicly available social media platform. We categorized the studies into various dimensions such as primary ADR detection approach, size of data, source(s), availability, evaluation criteria, and so on. Results Twenty-two studies met our inclusion criteria, with fifteen (68.2%) published within the last two years. The survey revealed a clear trend towards the usage of annotated data with eleven of the fifteen (73.3%) studies published in the last two years relying on expert annotations. However, publicly available annotated data is still scarce, and we found only six (27.3%) studies that made the annotations used publicly available, making system performance comparisons difficult. In terms of algorithms, supervised classification techniques to detect posts containing ADR mentions, and lexicon-based approaches for extraction of ADR mentions from texts have been the most popular. Conclusion Our review suggests that interest in the utilization of the vast amounts of available social media data for ADR monitoring is increasing with time. In terms of sources, both health-related and general social media data have been used for ADR detection— while health-related sources tend to contain higher proportions of relevant data, the volume of data from general social media websites is significantly higher. There is still very limited publicly available annotated data available, and, as indicated by the promising results obtained by recent supervised learning approaches, there is a strong need to make such data available to the research community.

This study aimed to estimate the level of burnout in mental health professionals and to identify specific determinants of burnout in this population. A systematic search of MEDLINE/PubMed, PsychINFO/Ovid, Embase, CINAHL/EBSCO and Web of Science was conducted for original research published between 1997 and 2017. Sixty-two studies were identified as meeting the study criteria for the systematic review. Data on the means, standard deviations, and prevalence of the dimensions of burnout were extracted from 33 studies and included in the meta-analysis (n = 9409). The overall estimated pooled prevalence for emotional exhaustion was 40% (CI 31%-48%) for depersonalisation was 22% (CI 15%-29%) and for low levels of personal accomplishment was 19% (CI 13%-25%). The random effects estimate of the mean scores on the Maslach Burnout Inventory indicate that the average mental health professional has high levels of emotional exhaustion [mean 21.11 (95% CI 19.98, 22.24)], moderate levels of depersonalisation [mean 6.76 (95% CI 6.11, 7.42)] but retains reasonable levels of personal accomplishment [mean 34.60 (95% CI 32.99, 36.21)]. Increasing age was found to be associated with an increased risk of depersonalisation but also a heightened sense of personal accomplishment. Work-related factors such as workload and relationships at work, are key determinants for burnout, while role clarity, a sense of professional autonomy, a sense of being fairly treated, and access to regular clinical supervision appear to be protective. Staff working in community mental health teams may be more vulnerable to burnout than those working in some specialist community teams, e.g., assertive outreach, crisis teams.

IntroductionPrescription medication overdose is the fastest growing drug-related problem in the USA. The growing nature of this problem necessitates the implementation of improved monitoring strategies for investigating the prevalence and patterns of abuse of specific medications.ObjectivesOur primary aims were to assess the possibility of utilizing social media as a resource for automatic monitoring of prescription medication abuse and to devise an automatic classification technique that can identify potentially abuse-indicating user posts.MethodsWe collected Twitter user posts (tweets) associated with three commonly abused medications (Adderall®, oxycodone, and quetiapine). We manually annotated 6400 tweets mentioning these three medications and a control medication (metformin) that is not the subject of abuse due to its mechanism of action. We performed quantitative and qualitative analyses of the annotated data to determine whether posts on Twitter contain signals of prescription medication abuse. Finally, we designed an automatic supervised classification technique to distinguish posts containing signals of medication abuse from those that do not and assessed the utility of Twitter in investigating patterns of abuse over time.ResultsOur analyses show that clear signals of medication abuse can be drawn from Twitter posts and the percentage of tweets containing abuse signals are significantly higher for the three case medications (Adderall®: 23 %, quetiapine: 5.0 %, oxycodone: 12 %) than the proportion for the control medication (metformin: 0.3 %). Our automatic classification approach achieves 82 % accuracy overall (medication abuse class recall: 0.51, precision: 0.41, F measure: 0.46). To illustrate the utility of automatic classification, we show how the classification data can be used to analyze abuse patterns over time.ConclusionOur study indicates that social media can be a crucial resource for obtaining abuse-related information for medications, and that automatic approaches involving supervised classification and natural language processing hold promises for essential future monitoring and intervention tasks.

Data and systems for medication-related text classification and concept normalization from twitter: insights from the social media mining for health (smm4h)-2017 shared task.

BackgroundCarotid body (peripheral oxygen sensor) sensitisation is pivotal in the development of chronic intermittent hypoxia (CIH)-induced hypertension. We sought to determine if exposure to CIH, modelling human sleep apnoea, adversely affects cardiorespiratory control in guinea-pigs, a species with hypoxia-insensitive carotid bodies. We reasoned that CIH-induced disruption of gut microbiota would evoke cardiorespiratory morbidity.MethodsAdult male guinea-pigs were exposed to CIH (6.5% O2 at nadir, 6 cycles.hour−1) for 8 h.day−1 for 12 consecutive days.FindingsCIH-exposed animals established reduced faecal microbiota species richness, with increased relative abundance of Bacteroidetes and reduced relative abundance of Firmicutes bacteria. Urinary corticosterone and noradrenaline levels were unchanged in CIH-exposed animals, but brainstem noradrenaline concentrations were lower compared with sham. Baseline ventilation was equivalent in CIH-exposed and sham animals; however, respiratory timing variability, sigh frequency and ventilation during hypoxic breathing were all lower in CIH-exposed animals. Baseline arterial blood pressure was unaffected by exposure to CIH, but β-adrenoceptor-dependent tachycardia and blunted bradycardia during phenylephrine-induced pressor responses was evident compared with sham controls.InterpretationIncreased carotid body chemo-afferent signalling appears obligatory for the development of CIH-induced hypertension and elevated chemoreflex control of breathing commonly reported in mammals, with hypoxia-sensitive carotid bodies. However, we reveal that exposure to modest CIH alters gut microbiota richness and composition, brainstem neurochemistry, and autonomic control of heart rate, independent of carotid body sensitisation, suggesting modulation of breathing and autonomic homeostasis via the microbiota-gut-brainstem axis. The findings have relevance to human sleep-disordered breathing.FundingThe Department of Physiology, and APC Microbiome Ireland, UCC.

SummaryBackground: Natural Language Processing (NLP) methods are increasingly being utilized to mine knowledge from unstructured health-related texts. Recent advances in noisy text processing techniques are enabling researchers and medical domain experts to go beyond the information encapsulated in published texts (e.g., clinical trials and systematic reviews) and structured questionnaires, and obtain perspectives from other unstructured sources such as Electronic Health Records (EHRs) and social media posts. Objectives: To review the recently published literature discussing the application of NLP techniques for mining health-related information from EHRs and social media posts. Methods: Literature review included the research published over the last five years based on searches of PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers. We particularly focused on the techniques employed on EHRs and social media data. Results: A set of 62 studies involving EHRs and 87 studies involving social media matched our criteria and were included in this paper. We present the purposes of these studies, outline the key NLP contributions, and discuss the general trends observed in the field, the current state of research, and important outstanding problems.

Aim: Ensuring a seamless transition from child to adult mental health services poses challenges for services worldwide. This is an important process in the on-going care of young people with mental illness; therefore it is incumbent on all countries to probe their individual structures to assess the quality of mental health service delivery to this vulnerable cohort. To date, there have been no published studies on the transition from Child to Adult Mental Health Services in the Republic of Ireland. To this end, a nationwide survey of transition policies of community mental health teams in both services was conducted in order to compare best practice guidelines for transition with current process and experience in clinical practice. Method: Structured interviews were conducted with 57 consultant psychiatrists (representing 32 CAMHS teams and 25 AMHS teams) to obtain information on annual transition numbers, existing transition policies, and operational practice from the professional perspective. Results: Numbers of young people considered suitable for transfer to adult services (M=7.73, SD=9.86, n=25) were slightly higher than numbers who actually transferred (M=4.50, SD=3.33, n=20). There is a lack of standardised practice nationwide regarding the service transition boundary, an absence of written transition policies and protocols, and minimal formal interaction between child and adult services. Conclusions: The findings suggest that there are critical gaps between current operational practice and best practice guidelines. Future studies will investigate the impact this has on the transition experiences of young people, their carers and healthcare professionals.