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Introduction There are few studies assessing how data on adverse drug events from consumers on social media compare with other sources. Aim The aim of this study was to assess the consistency of adverse event data of statin medications from social media as compared with other sources. Methods We collected data on the adverse events of statins from Twitter, the US FDA Adverse Event Reporting System (FAERS), the UK Medicines and Healthcare products Regulatory Agency (MHRA), drug information databases (DIDs) and systematic reviews. We manually annotated 12,649 tweets collected between June 2013 and August 2018. We collected 45,447 reports from FAERS, 10,415 from MHRA, identified 17 systematic reviews with relevant data and extracted data from Facts and Comparisons® and Clinical Pharmacology®. We compared the proportion, relative frequencies and rank of each category of adverse event from each source using MedDRA® primary System Organ Class codes. Results Compared with other sources, patients on social media are proportionally far more likely to complain about musculoskeletal symptoms than other adverse events. Most adverse events showed a high level of agreement between Twitter and regulatory data. DIDs tend to demonstrate similar patterns but not as strongly. Systematic reviews tend to examine pre-specified adverse events or those reported by trial investigators. Conclusions Combining the data from multiple sources, albeit challenging, may provide a broader safety profile of any medication. Systematically collected social media reports may be able to contribute information on the most pertinent adverse effects to patients. Electronic supplementary material The online version of this article (10.1007/s40264-020-00998-1) contains supplementary material, which is available to authorized users.

In the last three decades, early intervention for psychosis (EIP) services have been established worldwide and have resulted in superior symptomatic and functional outcomes for people affected by psychotic disorders. These improved outcomes are a result of reducing delays to treatment and the provision of specialised, holistic interventions. The COVID-19 pandemic poses significant challenges to the delivery of these services, such as undetected cases or long delays to treatment. Furthermore, the COVID-19 pandemic will likely increase the mental health needs of communities, including the incidence of psychotic disorders. In this perspective piece, we provide suggestions as to how EIP services can adapt within this environment, such as utilising novel technologies. Finally, we argue that despite the economic consequences of the pandemic, the funding for mental health services, including EI services, should be increased in line with the need for these services during and beyond the pandemic.

Toll-like receptors (TLRs) are crucial transmembrane receptors that form part of the innate immune response. They play a role in the recognition of various microorganisms and their elimination from the host. TLRs have been proposed as vital immunomodulators in the regulation of multiple neonatal stressors that extend beyond infection such as oxidative stress and pain. The immune system is immature at birth and takes some time to become fully established. As such, babies are especially vulnerable to sepsis at this early stage of life. Findings suggest a gestational age-dependent increase in TLR expression. TLRs engage with accessory and adaptor proteins to facilitate recognition of pathogens and their activation of the receptor. TLRs are generally upregulated during infection and promote the transcription and release of proinflammatory cytokines. Several studies report that TLRs are epigenetically modulated by chromatin changes and promoter methylation upon bacterial infection that have long-term influences on immune responses. TLR activation is reported to modulate cardiorespiratory responses during infection and may play a key role in driving homeostatic instability observed during sepsis. Although complex, TLR signaling and downstream pathways are potential therapeutic targets in the treatment of neonatal diseases. By reviewing the expression and function of key Toll-like receptors, we aim to provide an important framework to understand the functional role of these receptors in response to stress and infection in premature infants.

Background COVID-19 during pregnancy is associated with an increased risk of maternal death, intensive care unit admission, and preterm birth; however, many people who are pregnant refuse to receive COVID-19 vaccination because of a lack of safety data. Objective The objective of this preliminary study was to assess whether Twitter data could be used to identify a cohort for epidemiologic studies of COVID-19 vaccination in pregnancy. Specifically, we examined whether it is possible to identify users who have reported (1) that they received COVID-19 vaccination during pregnancy or the periconception period, and (2) their pregnancy outcomes. Methods We developed regular expressions to search for reports of COVID-19 vaccination in a large collection of tweets posted through the beginning of July 2021 by users who have announced their pregnancy on Twitter. To help determine if users were vaccinated during pregnancy, we drew upon a natural language processing (NLP) tool that estimates the timeframe of the prenatal period. For users who posted tweets with a timestamp indicating they were vaccinated during pregnancy, we drew upon additional NLP tools to help identify tweets that reported their pregnancy outcomes. Results We manually verified the content of tweets detected automatically, identifying 150 users who reported on Twitter that they received at least one dose of COVID-19 vaccination during pregnancy or the periconception period. We manually verified at least one reported outcome for 45 of the 60 (75%) completed pregnancies. Conclusions Given the limited availability of data on COVID-19 vaccine safety in pregnancy, Twitter can be a complementary resource for potentially increasing the acceptance of COVID-19 vaccination in pregnant populations. The results of this preliminary study justify the development of scalable methods to identify a larger cohort for epidemiologic studies.

BackgroundCoronavirus Disease 2019 (Covid-19) during pregnancy is associated with an increased risk of maternal death, intensive care unit (ICU) admission, and preterm birth; however, many people who are pregnant refuse to receive Covid-19 vaccination because of a lack of safety data.ObjectiveThe objective of this preliminary study was to assess whether we could identify (1) users who have reported on Twitter that they received Covid-19 vaccination during pregnancy or the periconception period, and (2) reports of their pregnancy outcomes.MethodsWe searched for reports of Covid-19 vaccination in a large collection of tweets posted by users who have announced their pregnancy on Twitter. To help determine if users were vaccinated during pregnancy, we drew upon a natural language processing (NLP) tool that estimates the timeframe of the prenatal period. For users who posted tweets with a timestamp indicating they were vaccinated during pregnancy, we drew upon additional NLP tools to help identify tweets that report their pregnancy outcomes.ResultsUpon manually verifying the content of tweets detected automatically, we identified 150 users who reported on Twitter that they received at least one dose of Covid-19 vaccination during pregnancy or the periconception period. Among the 60 completed pregnancies, we manually verified at least one reported outcome for 45 (75%) of them.ConclusionsGiven the limited availability of data on Covid-19 vaccine safety in pregnancy, Twitter can be a complementary resource for potentially increasing the acceptance of Covid-19 vaccination in pregnant populations. Directions for future work include developing machine learning algorithms to detect a larger number of users for observational studies.

Background Pre-exposure prophylaxis (PrEP) is highly effective at preventing the acquisition of HIV. There is a substantial gap, however, between the number of people in the United States who have indications for PrEP and the number of them who are prescribed PrEP. Although Twitter content has been analyzed as a source of PrEP-related data (eg, barriers), methods have not been developed to enable the use of Twitter as a platform for implementing PrEP-related interventions. Objective Men who have sex with men (MSM) are the population most affected by HIV in the United States. Therefore, the objectives of this study were to (1) develop an automated natural language processing (NLP) pipeline for identifying men in the United States who have reported on Twitter that they are gay, bisexual, or MSM and (2) assess the extent to which they demographically represent MSM in the United States with new HIV diagnoses. Methods Between September 2020 and January 2021, we used the Twitter Streaming Application Programming Interface (API) to collect more than 3 million tweets containing keywords that men may include in posts reporting that they are gay, bisexual, or MSM. We deployed handwritten, high-precision regular expressions—designed to filter out noise and identify actual self-reports—on the tweets and their user profile metadata. We identified 10,043 unique users geolocated in the United States and drew upon a validated NLP tool to automatically identify their ages. Results By manually distinguishing true- and false-positive self-reports in the tweets or profiles of 1000 (10%) of the 10,043 users identified by our automated pipeline, we established that our pipeline has a precision of 0.85. Among the 8756 users for which a US state–level geolocation was detected, 5096 (58.2%) were in the 10 states with the highest numbers of new HIV diagnoses. Among the 6240 users for which a county-level geolocation was detected, 4252 (68.1%) were in counties or states considered priority jurisdictions by the Ending the HIV Epidemic initiative. Furthermore, the age distribution of the users reflected that of MSM in the United States with new HIV diagnoses. Conclusions Our automated NLP pipeline can be used to identify MSM in the United States who may be at risk of acquiring HIV, laying the groundwork for using Twitter on a large scale to directly target PrEP-related interventions at this population.

Prematurity is the leading cause of neonatal morbidity and mortality worldwide. Premature infants often require extended hospital stays, with increased risk of developing infection compared with term infants. A picture is emerging of wide-ranging deleterious consequences resulting from innate immune system activation in the newborn infant. Those who survive infection have been exposed to a stimulus that can impose long-lasting alterations into later life. In this review, we discuss sepsis-driven alterations in integrated neuroendocrine and metabolic pathways and highlight current knowledge gaps in respect of neonatal sepsis. We review established biomarkers for sepsis and extend the discussion to examine emerging findings from human and animal models of neonatal sepsis that propose novel biomarkers for early identification of sepsis. Future research in this area is required to establish a greater understanding of the distinct neonatal signature of early and late-stage infection, to improve diagnosis, curtail inappropriate antibiotic use, and promote precision medicine through a biomarker-guided empirical and adjunctive treatment approach for neonatal sepsis. There is an unmet clinical need to decrease sepsis-induced morbidity in neonates, to limit and prevent adverse consequences in later life and decrease mortality.

The increase of social media usage across the globe has fueled efforts in public health research for mining valuable information such as medication use, adverse drug effects and reports of viral infections that directly and indirectly affect human health. Despite its significance, such information can be incredibly rare on social media. Mining such non-traditional sources for disease monitoring requires natural language processing techniques for extracting symptom mentions and normalizing them to standard terminologies for interpretability. In this work, we present the first version of a social media mining tool called SEED that detects symptom and disease mentions from social media posts such as Twitter and DailyStrength and further normalizes them into the UMLS terminology. Using multi-corpus training and deep learning models, the tool achieves an overall F1 score of 0.85 for extracting mentions of symptoms on a health forum dataset and an F1 score of 0.72 on a balanced Twitter dataset significantly improving over previously systems on the datasets. We apply the tool on recently collected Twitter posts that self-report COVID19 symptoms to observe if the SEED system can extract novel diseases and symptoms that were absent in the training data. By doing so, we describe the advantages and shortcomings of the tool and suggest techniques to overcome the limitations. The study results also draw attention to the potential of multi-corpus training for performance improvements and the need for continual training on newly obtained data for consistent performance amidst the ever-changing nature of the social media vocabulary.

BackgroundThere has been an increasing recognition of the influence of social, behavioral, economic, and environmental factors on overall patient health. The purpose of this project was to leverage the ICD-10 codes to identify and link social determinants of health (SDoH) to patients with a high probability of utilizing acute care services and to determine if social service intervention reduced care utilization.MethodsWe analyzed retrospective data for active patients at a Department of Veterans Affairs Medical Center (VAMC) from 2015-2017. Eleven categories of SDoH were developed based on existing literature of the social determinants; the relevant ICD-10 codes were divided among these categories. Emergency Room (ER) visits, hospital admissions, and social work visits were determined for each patient in the cohort.ResultsIn a cohort of 44,401 patients, the presence of ICD-10 codes within the EHR in the 11 SDoH categories was positively correlated with increased acute care utilization. Veterans with at least one SDoH risk factor were 71% (95%CI: 68% - 75%) more likely to use the ED and 71% (95%CI: 65%-77%) more likely to be admitted to the hospital. Utilization decreased with social service interventions.ConclusionThis project demonstrates a potentially meaningful method to capture patient social risk profiles through existing EHR data in the form of ICD-10 codes, which can be used to identify the highest risk patients for intervention with the understanding that not all SDoH codes are uniformly used and some SDoHs may not be captured.

Objectives:Service users with severe psychiatric illnesses, such as schizophrenia, major depressive disorder and bipolar disorder, are more likely to suffer from ill health. There is evidence that lifestyle interventions, for example, exercise, dietary advice and smoking cessation programmes for service users with severe mental illness can be of health benefit. This review was carried out to identify the literature pertaining to physical health interventions for service users who have experienced a first-episode psychosis (FEP), to examine the nature of the interventions which were carried out and to assess these interventions in terms of feasibility and efficacy.Methods:A narrative review was conducted in August 2019 by searching ‘Pubmed’ and ‘Embase’ electronic databases. Studies investigating the effect a physical health intervention had on service users who had experienced a FEP were included in the review.Results:Fifteen studies met inclusion criteria: 12 quantitative studies and 3 qualitative. Exercise, dietary advice, smoking cessation and motivational coaching were some of the physical health interventions utilised in the identified studies. Positive effects were seen in terms of physical health markers wherever they were investigated, particularly when the intervention was delivered early. The impact on psychiatric symptoms and longer-term impacts on health were less frequently assessed.Conclusions:Physical health interventions have a positive impact on service users who have experienced a FEP. More research is warranted in this area in Ireland. These studies should include controls, have longer follow-up periods and should assess the impact on psychiatric health.

Background Evidence is accruing to suggest that microbiota-gut-brain signalling plays a regulatory role in cardiorespiratory physiology. Chronic intermittent hypoxia (CIH), modelling human sleep apnoea, affects gut microbiota composition and elicits cardiorespiratory morbidity. We investigated if treatment with prebiotics ameliorates cardiorespiratory dysfunction in CIH-exposed rats. Methods Adult male rats were exposed to CIH (96 cycles/day, 6.0% O2 at nadir) for 14 consecutive days with and without prebiotic supplementation (fructo- and galacto-oligosaccharides) beginning two weeks prior to gas exposures. Findings CIH increased apnoea index and caused hypertension. CIH exposure had modest effects on the gut microbiota, decreasing the relative abundance of Lactobacilli species, but had no effect on microbial functional characteristics. Faecal short-chain fatty acid (SCFA) concentrations, plasma and brainstem pro-inflammatory cytokine concentrations and brainstem neurochemistry were unaffected by exposure to CIH. Prebiotic administration modulated gut microbiota composition and diversity, altering gut-metabolic (GMMs) and gut-brain (GBMs) modules and increased faecal acetic and propionic acid concentrations, but did not prevent adverse CIH-induced cardiorespiratory phenotypes. Interpretation CIH-induced cardiorespiratory dysfunction is not dependant upon changes in microbial functional characteristics and decreased faecal SCFA concentrations. Prebiotic-related modulation of microbial function and resultant increases in faecal SCFAs were not sufficient to prevent CIH-induced apnoea and hypertension in our model. Our results do not exclude the potential for microbiota-gut-brain axis involvement in OSA-related cardiorespiratory morbidity, but they demonstrate that in a relatively mild model of CIH, sufficient to evoke classic cardiorespiratory dysfunction, such changes are not obligatory for the development of morbidity, but may become relevant in the elaboration and maintenance of cardiorespiratory morbidity with progressive disease. Funding Department of Physiology and APC Microbiome Ireland, University College Cork, Ireland. APC Microbiome Ireland is funded by Science Foundation Ireland, through the Government's National Development Plan.

Background Prescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging—requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter. Methods We experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority “abuse/misuse” class. Results Our proposed fusion-based model performs significantly better than the best traditional model (F1-score [95% CI]: 0.67 [0.64–0.69] vs. 0.45 [0.42–0.48]). We illustrate, via experimentation using varying training set sizes, that the transformer-based models are more stable and require less annotated data compared to the other models. The significant improvements achieved by our best-performing classification model over past approaches makes it suitable for automated continuous monitoring of nonmedical PM use from Twitter. Conclusions BERT, BERT-like and fusion-based models outperform traditional machine learning and deep learning models, achieving substantial improvements over many years of past research on the topic of prescription medication misuse/abuse classification from social media, which had been shown to be a complex task due to the unique ways in which information about nonmedical use is presented. Several challenges associated with the lack of context and the nature of social media language need to be overcome to further improve BERT and BERT-like models. These experimental driven challenges are represented as potential future research directions.

There is clear evidence of physiological effects of the gut microbiota on whole-body function in health and disease. Microbiota-gut-brain axis signalling is recognised as a key player in behavioural disorders such as depression and anxiety. Recent evidence suggests that the gut microbiota affects neurocontrol networks responsible for homeostatic functions that are essential for life. We consider the evidence suggesting the potential for the gut microbiota to shape cardiorespiratory homeostasis. In various animal models of disease, there is an association between cardiorespiratory morbidity and perturbed gut microbiota, with strong evidence in support of a role of the gut microbiota in the control of blood pressure. Interventions that target the gut microbiota or manipulate the gut-brain axis, such as short-chain fatty acid supplementation, prevent hypertension in models of obstructive sleep apnoea. Emerging evidence points to a role for the microbiota-gut-brain axis in the control of breathing and ventilatory responsiveness, relevant to cardiorespiratory disease. There is also evidence for an association between the gut microbiota and disease severity in people with asthma and cystic fibrosis. There are many gaps in the knowledge base and an urgent need to better understand the mechanisms by which gut health and dysbiosis contribute to cardiorespiratory control. Nevertheless, there is a growing consensus that manipulation of the gut microbiota could prove an efficacious adjunctive strategy in the treatment of common cardiorespiratory diseases, which are the leading causes of morbidity and mortality.

The rapidly evolving COVID-19 pandemic presents challenges for actively monitoring its transmission. In this study, we extend a social media mining approach used in the US to automatically identify personal reports of COVID-19 on Twitter in England, UK. The findings indicate that natural language processing and machine learning framework could help provide an early indication of the chronological and geographical distribution of COVID-19 in England.

The rapidly evolving outbreak of COVID-19 presents challenges for actively monitoring its spread. In this study, we assessed a social media mining approach for automatically analyzing the chronological and geographical distribution of users in the United States reporting personal information related to COVID-19 on Twitter. The results suggest that our natural language processing and machine learning framework could help provide an early indication of the spread of COVID-19.

Background Social media data are being increasingly used for population-level health research because it provides near real-time access to large volumes of consumer-generated data. Recently, a number of studies have explored the possibility of using social media data, such as from Twitter, for monitoring prescription medication abuse. However, there is a paucity of annotated data or guidelines for data characterization that discuss how information related to abuse-prone medications is presented on Twitter. Objective This study discusses the creation of an annotated corpus suitable for training supervised classification algorithms for the automatic classification of medication abuse–related chatter. The annotation strategies used for improving interannotator agreement (IAA), a detailed annotation guideline, and machine learning experiments that illustrate the utility of the annotated corpus are also described. Methods We employed an iterative annotation strategy, with interannotator discussions held and updates made to the annotation guidelines at each iteration to improve IAA for the manual annotation task. Using the grounded theory approach, we first characterized tweets into fine-grained categories and then grouped them into 4 broad classes—abuse or misuse, personal consumption, mention, and unrelated. After the completion of manual annotations, we experimented with several machine learning algorithms to illustrate the utility of the corpus and generate baseline performance metrics for automatic classification on these data. Results Our final annotated set consisted of 16,443 tweets mentioning at least 20 abuse-prone medications including opioids, benzodiazepines, atypical antipsychotics, central nervous system stimulants, and gamma-aminobutyric acid analogs. Our final overall IAA was 0.86 (Cohen kappa), which represents high agreement. The manual annotation process revealed the variety of ways in which prescription medication misuse or abuse is discussed on Twitter, including expressions indicating coingestion, nonmedical use, nonstandard route of intake, and consumption above the prescribed doses. Among machine learning classifiers, support vector machines obtained the highest automatic classification accuracy of 73.00% (95% CI 71.4-74.5) over the test set (n=3271). Conclusions Our manual analysis and annotations of a large number of tweets have revealed types of information posted on Twitter about a set of abuse-prone prescription medications and their distributions. In the interests of reproducible and community-driven research, we have made our detailed annotation guidelines and the training data for the classification experiments publicly available, and the test data will be used in future shared tasks.

Background Although studies report that more than 90% of pregnant women utilize digital sources to supplement their maternal healthcare, little is known about the kinds of information that women seek from their peers during pregnancy. To date, most research has used self-report measures to elucidate how and why women to turn to digital sources during pregnancy. However, given that these measures may differ from actual utilization of online health information, it is important to analyze the online content pregnant women generate. Objective To apply machine learning methods to analyze online pregnancy forums, to better understand how women seek information from a community of online peers during pregnancy. Methods Data from seven WhatToExpect.com “birth club” forums (September 2018; January-June 2018) were scraped. Forum posts were collected for a one-year period, which included three trimesters and three months postpartum. Only initial posts from each thread were analyzed (n = 262,238). Automatic natural language processing (NLP) methods captured 50 discussed topics, which were annotated by two independent coders and grouped categorically. Results The largest topic categories were maternal health (45%), baby-related topics (29%), and people/relationships (10%). While pain was a popular topic all throughout pregnancy, individual topics that were dominant by trimester included miscarriage (first trimester), labor (third trimester), and baby sleeping routine (postpartum period). Conclusion More than just emotional or peer support, pregnant women turn to online forums to discuss their health. Dominant topics, such as labor and miscarriage, suggest unmet informational needs in these domains. With misinformation becoming a growing public health concern, more attention must be directed toward peer-exchange outlets.

This paper documents and analyzes the 2,277 witnesses who testified before the U.S. House of Representatives Committee on Ways and Means (Ways & Means) and the U.S. Senate Committee on Finance (Finance) for the 110th (2007-2008), 111th (2009-2010) and the 112th Congresses (2011-2012). This allows us to measure the frequency of women as witness before committees controlled by Democrats (Nancy Pelosi as Speaker (2007-2010); Harry Reid as Senate Majority Leader (2007-2012)), and Republicans (John Boehner as Speaker (2011-2012)). We construct a unique Congressional Record Representation Dataset (CRRD) using publicly-available committee reports and hearing transcripts to identify witnesses testifying before these committees to measure each committee as to its efforts to represent women and persons of color. This paper is the initial summary of our findings for witness representation before the tax-writing committees in terms of gender for the 110th through the 112th Congresses (the Review Period), and is the preliminary analysis of a larger project to measure and study the impact of women and persons of color testifying before multiple congressional committees from the 110th through the 115th Congresses. We presented this paper at the 2020 Annual Meeting of the Southern Political Science Association. Extended versions of the CRRD are included in a forthcoming article by Caroline Bruckner titled, Doubling Down on a Billion Dollar Blind Spot: Women Business Owners and Tax Reform, which the American University Washington College of Law Business Law Review is publishing in Spring 2020.

Introduction Adverse effects of medications taken during pregnancy are traditionally studied through post-marketing pregnancy registries, which have limitations. Social media data may be an alternative data source for pregnancy surveillance studies. Objective The objective of this study was to assess the feasibility of using social media data as an alternative source for pregnancy surveillance for regulatory decision making. Methods We created an automated method to identify Twitter accounts of pregnant women. We identified 196 pregnant women with a mention of a birth defect in relation to their baby and 196 without a mention of a birth defect in relation to their baby. We extracted information on pregnancy and maternal demographics, medication intake and timing, and birth defects. Results Although often incomplete, we extracted data for the majority of the pregnancies. Among women that reported birth defects, 35% reported taking one or more medications during pregnancy compared with 17% of controls. After accounting for age, race, and place of residence, a higher medication intake was observed in women who reported birth defects. The rate of birth defects in the pregnancy cohort was lower (0.44%) compared with the rate in the general population (3%). Conclusions Twitter data capture information on medication intake and birth defects; however, the information obtained cannot replace pregnancy registries at this time. Development of improved methods to automatically extract and annotate social media data may increase their value to support regulatory decision making regarding pregnancy outcomes in women using medications during their pregnancies.

Introduction Adverse drug reactions (ADRs) are associated with significant health-related and financial burden, and multiple sources are currently utilized to actively discover them. Social media has been proposed as a potential resource for monitoring ADRs, but drug-specific analytical studies comparing social media with other sources are scarce. Objectives Our objective was to develop methods to compare ADRs mentioned in social media with those in traditional sources: the US FDA Adverse Event Reporting System (FAERS), drug information databases (DIDs), and systematic reviews. Methods A total of 10,188 tweets mentioning adalimumab collected between June 2014 and August 2016 were included. ADRs in the corpus were extracted semi-automatically and manually mapped to standardized concepts in the Unified Medical Language System. ADRs were grouped into 16 biologic categories for comparisons. Frequencies, relative frequencies, disproportionality analyses, and rank ordering were used as metrics. Results There was moderate agreement between ADRs in social media and traditional sources. “Local and injection site reactions” was the top ADR in Twitter, DIDs, and systematic reviews by frequency, ranked frequency, and index ranking. The next highest ADR in Twitter—fatigue—ranked fifth and seventh in FAERS and DIDs. Conclusion Social media posts often express mild and symptomatic ADRs, but rates are measured differently in scientific sources. ADRs in FAERS are reported as absolute numbers, in DIDs as percentages, and in systematic reviews as percentages, risk ratios, or other metrics, which makes comparisons challenging; however, overlap is substantial. Social media analysis facilitates open-ended investigation of patient perspectives and may reveal concepts (e.g. anxiety) not available in traditional sources. Electronic supplementary material The online version of this article (10.1007/s40264-018-0707-6) contains supplementary material, which is available to authorized users.

Reactive oxygen species (ROS) are proposed as mediators of chronic intermittent hypoxia (CIH)-induced respiratory plasticity. We sought to determine if NADPH oxidase 2 (NOX2)-derived ROS underpin CIH-induced maladaptive changes in respiratory control. Adult male mice (C57BL/6 J) were assigned to one of three groups: normoxic controls (sham); chronic intermittent hypoxia-exposed (CIH, 12 cycles/hour, 8 h/day for 14 days); and CIH + apocynin (NOX2 inhibitor, 2 mM) given in the drinking water throughout exposure to CIH. In addition, we studied sham and CIH-exposed NOX2-null mice (B6.129S-CybbTM1Din/J). Whole-body plethysmography was used to measure breathing and metabolic parameters. Ventilation (VI/VCO2) during normoxia was unaffected by CIH, but apnoea index was increased, which was prevented by apocynin, but not by NOX2 deletion. The ventilatory response to hypercapnia following exposure to CIH was potentiated in NOX2-null mice. Our results reveal ROS-dependent influences on the control of breathing and point to antioxidant intervention as a potential adjunctive therapeutic strategy in respiratory control disorders.

Family support is a core component of the Early Psychosis Intervention (EPI) model, yet it continues to have relatively low rates of implementation in practice. This paper reports results of a literature review on facilitators and barriers to delivering family interventions in EPI programmes. A search was conducted of 4 electronic databases, Medline, EMBASE, PsycINFO and Joanna Briggs, from 2000 to 2015 using terms related to early onset psychosis, family work and implementation. Four thousand four hundred and two unique studies were identified, 7 of which met inclusion criteria. Barriers and facilitators were coded and aggregated to higher-level themes using a consensus approach. Five of 7 studies examined structured multifamily psychoeducation. Uptake by families was affected by: family/client interest and readiness to participate; ability to access supports; and support needs/preferences. Implementation by programmes was affected by staff access to training and resources to provide family support. A key finding across the identified studies was that families have different needs and preferences regarding the timing, length, intensity and content of the intervention. One size does not fit all and many families do not require the intensive psychoeducational programmes typically provided. The reviewed literature suggests that flexible, tiered approaches to care may better meet family needs and increase rates of uptake of family support. However, more research is needed on the effectiveness of different models of family support in early psychosis and how they can be successfully implemented.

Summary We present GeoBoost2, a natural language-processing pipeline for extracting the location of infected hosts for enriching metadata in nucleotide sequences repositories like National Center of Biotechnology Information’s GenBank for downstream analysis including phylogeography and genomic epidemiology. The increasing number of pathogen sequences requires complementary information extraction methods for focused research, including surveillance within countries and between borders. In this article, we describe the enhancements from our earlier release including improvement in end-to-end extraction performance and speed, availability of a fully functional web-interface and state-of-the-art methods for location extraction using deep learning. Availability and implementation Application is freely available on the web at https://zodo.asu.edu/geoboost2. Source code, usage examples and annotated data for GeoBoost2 is freely available at https://github.com/ZooPhy/geoboost2. Supplementary information Supplementary data are available at Bioinformatics online.

Opioid use disorder (OUD) is a public health emergency in the United States. Over 47,000 overdose-related deaths in 2017 involved opioids. Medication-assisted treatment (MAT), in particular, buprenorphine and buprenorphine combination products such as Suboxone®, is the most effective, evidence-based treatment for OUD. However, there are a limited number of conclusive scientific studies that provide guidance to medical professionals about strategies for using buprenorphine to achieve stable recovery. In this study, we used data-driven natural language processing methods to mine a total of 16,146 posts about buprenorphine from 1933 unique users on the anonymous social network Reddit. Analysis of a sample of these posts showed that 74% of the posts described users’ personal experiences and that the top three topics included advice on using Suboxone® (55.0%), Suboxone® dosage information (35.5%) and information about Suboxone® tapering (32.0%). Based on two models, one that incorporated ‘upvoting’ by other members and one that did not, we found that Reddit users reported more successful recovery with longer tapering schedules, particularly from 2.0 mg to 0.0 mg (median: 93 days; mean: 95 days), as compared to shorter tapering schedules investigated in past clinical trials. Diarrhea, insomnia, restlessness, and fatigue were commonly reported adverse events. Physical exercise, clonidine, and Imodium® were frequently reported to help during the recovery process. Due to the difficulties of conducting longer-term clinical trials involving patients with OUD, clinicians should consider other information sources including peer discussions from the abundant, real-time information available on Reddit.Significance StatementOpioid use disorder (OUD) is a national crisis in the United States and buprenorphine is one of the most effective evidence-based treatments. However, few studies have explored successful strategies for using and tapering buprenorphine to achieve stable recovery, particularly due to the difficulties of conducting long-term studies involving patients with OUD. In this study, we show that discussions on the anonymous social network Reddit may be leveraged, via automatic text mining methods, to discover successful buprenorphine use and tapering strategies. We discovered that longer tapering schedules, compared to those investigated in past clinical trials, may lead to (self-reported) sustained recovery. Furthermore, Reddit posts also provide key information regarding buprenorphine withdrawal, cravings, adjunct medications for withdrawal symptoms and relapse prevention strategies.

Preterm birth is a risk factor for the development of cardiorespiratory complications. Infants that are born prematurely face myriad challenges due to physiological immaturity. Respiratory control impairments in early life including apnoea of prematurity with resultant disruption to systemic oxygen status can provoke long-term disability, including increased propensity to develop morbidities in later life such as sleep-disordered breathing. In rodents, exposure to intermittent hypoxia mimicking recurrent episodes of oxygen desaturation that are characteristic of apnoeas, provokes plasticity at multiple sites of the respiratory control network culminating in breathing instabilities, altered chemoreflex control of breathing and impaired respiratory motor nerve and muscle function. Persistent effects of stressors presenting during critical periods of early development may be sustained by epigenetic mechanisms. Such changes may be especially relevant to perinatal exposure to intermittent hypoxia since it is established that hypermethylation of genes encoding antioxidant enzymes underlies carotid body chemoreceptor sensitization and respiratory instability following exposure to intermittent hypoxia during postnatal development.

ObjectivesTo evaluate if having an early intervention service (EIS), which is embedded within a home-based treatment team (HBTT), is associated with (1) shorter duration of untreated psychosis (DUP), (2) lower rates of hospital admissions at first presentation, (3) a lesser number of hospital admissions within 6 months of presentation and (4) a reduced mean bed usage for the first 6 months.MethodsThe files of those who presented with a first-episode psychosis (FEP) to the South Lee Mental Health Service from January 2016 to February 2017 were identified and a retrospective case review was carried out. The demographics, clinical characteristics and hospital admissions were compared for those admitted to either the EIS or community mental health teams.ResultsForty patients were assessed. DUP was found to be longer for those who presented to the EIS (U= 121,p= 0.03). There were fewer admissions at first presentation (χ2(1) = 6.51p= 0.01), fewer admissions within the first 6 months of presentation (χ2(1) = 5.56p= 0.02) and less bed usage overall (U= 131,p= 0.047) for those who presented to the EIS.ConclusionThe study provides a baseline clinical and demographic profile of patients with FEP in an Irish mental health service and demonstrates current pathways to care. EIS embedded within an HBTT was associated with fewer hospital admissions and less bed usage. It is unclear whether these findings may have occurred due to the EIS or due to the benefits provided by an HBTT.

Objective: After years of research, Twitter posts are now recognized as an important source of patient-generated data, providing unique insights into population health. A fundamental step to incorporating Twitter data in pharmacoepidemiological research is to automatically recognize medication mentions in tweets. Given that lexical searches for medication names may fail due to misspellings or ambiguity with common words, we propose a more advanced method to recognize them. Methods: We present Kusuri, an Ensemble Learning classifier, able to identify tweets mentioning drug products and dietary supplements. Kusuri ("medication" in Japanese) is composed of two modules. First, four different classifiers (lexicon-based, spelling-variant-based, pattern-based and one based on a weakly-trained neural network) are applied in parallel to discover tweets potentially containing medication names. Second, an ensemble of deep neural networks encoding morphological, semantical and long-range dependencies of important words in the tweets discovered is used to make the final decision. Results: On a balanced (50-50) corpus of 15,005 tweets, Kusuri demonstrated performances close to human annotators with 93.7% F1-score, the best score achieved thus far on this corpus. On a corpus made of all tweets posted by 113 Twitter users (98,959 tweets, with only 0.26% mentioning medications), Kusuri obtained 76.3% F1-score. There is not a prior drug extraction system that compares running on such an extremely unbalanced dataset. Conclusion: The system identifies tweets mentioning drug names with performance high enough to ensure its usefulness and ready to be integrated in larger natural language processing systems., This is a pre-copy-editing, author-produced PDF of an article accepted for publication in JAMIA following peer review. The definitive publisher-authenticated version is "D. Weissenbacher, A. Sarker, A. Klein, K. O'Connor, A. Magge, G. Gonzalez-Hernandez, Deep neural networks ensemble for detecting medication mentions in tweets, Journal of the American Medical Informatics Association, ocz156, 2019"

We present the SemEval-2019 Task 12 which focuses on toponym resolution in scientific articles. Given an article from PubMed, the task consists of detecting mentions of names of places, or toponyms, and mapping the mentions to their corresponding entries in GeoNames.org, a database of geospatial locations. We proposed three subtasks. In Subtask 1, we asked participants to detect all toponyms in an article. In Subtask 2, given toponym mentions as input, we asked participants to disambiguate them by linking them to entries in GeoNames. In Subtask 3, we asked participants to perform both the detection and the disambiguation steps for all toponyms. A total of 29 teams registered, and 8 teams submitted a system run. We summarize the corpus and the tools created for the challenge. They are freely available at https://competitions.codalab.org/competitions/19948. We also analyze the methods, the results and the errors made by the competing systems with a focus on toponym disambiguation.

peer-reviewed To date, Ireland has been a leading light in the provision of youth mental health services. However, cognisant of the efforts of governmental and non-governmental agencies working in youth mental health, there is much to be done. Barriers into care as well as discontinuity of care across the spectrum of services remain key challenges. This editorial provides guidance for the next stage of development in youth mental care and support that will require significant national engagement and resource investment. peer-reviewed

Background: It is increasingly evident that perturbations to the diversity and composition of the gut microbiota have significant consequences for the regulation of integrative physiological systems. There is growing interest in the potential contribution of microbiota-gut-brain signalling to cardiorespiratory control in health and disease. Methods: In adult male rats, we sought to determine the cardiorespiratory effects of manipulation of the gut microbiota following a 4-week administration of a cocktail of antibiotics. We subsequently explored the effects of administration of faecal microbiota from pooled control (vehicle) rat faeces, given by gavage to vehicle- and antibiotic-treated rats. Findings: Antibiotic intervention depressed the ventilatory response to hypercapnic stress in conscious animals, owing to a reduction in the respiratory frequency response to carbon dioxide. Baseline frequency, respiratory timing variability, and the expression of apnoeas and sighs were normal. Microbiota-depleted rats had decreased systolic blood pressure. Faecal microbial transfer to vehicle- and antibiotic-treated animals also disrupted the gut microbiota composition, associated with depressed ventilatory responsiveness to hypercapnia. Chronic antibiotic intervention or faecal microbial transfer both caused significant disruptions to brainstem monoamine neurochemistry, with increased homovanillic acid:dopamine ratio indicative of increased dopamine turnover, which correlated with the abundance of several bacteria of six different phyla. Interpretation: Chronic antibiotic administration and faecal microbial transfer disrupt gut microbiota, brainstem monoamine concentrations and the ventilatory response to hypercapnia. We suggest that aberrant microbiota-gut-brain axis signalling has a modulatory influence on respiratory behaviour during hypercapnic stress. Fund: Department of Physiology and APC Microbiome Ireland, University College Cork, Ireland., This project was funded by the Department of Physiology, and the APC Microbiome Ireland (funded by Science Foundation Ireland (SFI/12/RC/2273)), University College Cork, Ireland.

Background Diabetes mellitus is a major global public health issue where self-management is critical to reducing disease burden. Social media has been a powerful tool to understand public perceptions. Public perception of the drugs used for the treatment of diabetes may be useful for orienting interventions to increase adherence. Objective The aim of this study was to explore the public perceptions of anti-diabetic drugs through the analysis of health-related tweets mentioning such medications. Methods This study uses an infoveillance social listening approach to monitor public discourse using Twitter data. We coded 4000 tweets from January 1, 2019 to October 1, 2019 containing key terms related to anti-diabetic drugs by using qualitative content analysis. Tweets were coded for whether they were truly about an anti-diabetic drug and whether they were health-related. Health-related tweets were further coded based on who was tweeting, which anti-diabetic drug was being tweeted about, and the content discussed in the tweet. The main outcome of the analysis was the themes identified by analyzing the content of health-related tweets on anti-diabetic drugs. Results We identified 1664 health-related tweets on 33 anti-diabetic drugs. A quarter (415/1664) of the tweets were confirmed to have been from people with diabetes, 17.9% (298/1664) from people posting about someone else, and 2.7% (45/1664) from health care professionals. However, the role of the tweeter was unidentifiable in two-thirds of the tweets. We identified 13 themes, with the health consequences of the cost of anti-diabetic drugs being the most extensively discussed, followed by the efficacy and availability. We also identified issues that patients may conceal from health care professionals, such as purchasing medications from unofficial sources. Conclusions This study uses an infoveillance approach using Twitter data to explore public perceptions related to anti-diabetic drugs. This analysis gives an insight into the real-life issues that an individual faces when taking anti-diabetic drugs, and such findings may be incorporated into health policies to improve compliance and efficacy. This study suggests that there is a fear of not having access to anti-diabetic drugs due to cost or physical availability and highlights the impact of the sacrifices made to access anti-diabetic drugs. Along with screening for diabetes-related health issues, health care professionals should also ask their patients about any non–health-related concerns regarding their anti-diabetic drugs. The positive tweets about dietary changes indicate that people with type 2 diabetes may be more open to self-management than what the health care professionals believe.

Background In the United States, the rapidly evolving COVID-19 outbreak, the shortage of available testing, and the delay of test results present challenges for actively monitoring its spread based on testing alone. Objective The objective of this study was to develop, evaluate, and deploy an automatic natural language processing pipeline to collect user-generated Twitter data as a complementary resource for identifying potential cases of COVID-19 in the United States that are not based on testing and, thus, may not have been reported to the Centers for Disease Control and Prevention. Methods Beginning January 23, 2020, we collected English tweets from the Twitter Streaming application programming interface that mention keywords related to COVID-19. We applied handwritten regular expressions to identify tweets indicating that the user potentially has been exposed to COVID-19. We automatically filtered out “reported speech” (eg, quotations, news headlines) from the tweets that matched the regular expressions, and two annotators annotated a random sample of 8976 tweets that are geo-tagged or have profile location metadata, distinguishing tweets that self-report potential cases of COVID-19 from those that do not. We used the annotated tweets to train and evaluate deep neural network classifiers based on bidirectional encoder representations from transformers (BERT). Finally, we deployed the automatic pipeline on more than 85 million unlabeled tweets that were continuously collected between March 1 and August 21, 2020. Results Interannotator agreement, based on dual annotations for 3644 (41%) of the 8976 tweets, was 0.77 (Cohen κ). A deep neural network classifier, based on a BERT model that was pretrained on tweets related to COVID-19, achieved an F1-score of 0.76 (precision=0.76, recall=0.76) for detecting tweets that self-report potential cases of COVID-19. Upon deploying our automatic pipeline, we identified 13,714 tweets that self-report potential cases of COVID-19 and have US state–level geolocations. Conclusions We have made the 13,714 tweets identified in this study, along with each tweet’s time stamp and US state–level geolocation, publicly available to download. This data set presents the opportunity for future work to assess the utility of Twitter data as a complementary resource for tracking the spread of COVID-19.

Importance Statins are prescribed to help lower cholesterol levels but have poor adherence rates. Attitudes or beliefs toward medications are important to ascertain because they may be associated with patient behavior. Objective To identify health-related discussion in Twitter posts mentioning a statin and analyze the content within these posts. Design, Setting, and Participants This qualitative study included 11 852 posts related to 1 of 8 statins (7 licensed for use in the United States and 5 licensed in the United Kingdom) collected from May 10, 2013, to August 28, 2018. Twitter posts were identified as health related or not, and if health related, whether they were posted by a statin user, someone who knows a statin user, a health care professional, or undetermined. The health-related tweets were classified by the type of information posted, such as a belief about the medication, an adverse event, a question, or a reference to the cost of the medication. Data were analyzed from January 22 to November 19, 2019. Main Outcomes and Measures The number of posts by each user type identified and the categories identified by analyzing the content. Results Of the 11 852 Twitter posts about statins, 5201 (43.9%) were health related. The most frequent posts provided resource information, such as a link to a journal article (1824 of 5201 [35.1%]). The second most frequent type of health-related posts included personal beliefs or attitudes toward statins (1097 of 5201 [21.1%]). Personal experiences of adverse events (353 of 5201 [6.8%]), discussions about dosage (320 of 5201 [6.2%]), and questions about statins (191 of 5201 [3.7%]) followed. Posts indicated polarized beliefs and attitudes to statins from saving lives to causing death. Some beliefs, such as the direct confirmation that the use of statins mitigates the effects of an unhealthy diet, have not been extensively highlighted as common practice in the literature. Conclusion and Relevance This qualitative content analysis of Twitter posts about statins provides insights into beliefs about statins. Patient perspectives gathered from social media may help to inform research and improve public health messages and communication between health care professionals and patients.

HEART score (HS) has been validated and is being widely used as a tool for risk stratification of chest pain (CP) in the emergency department (ED). However, it has not been well studied in the community hospital setting. We sought to determine the prognostic accuracy of HS in predicting major

Risk stratification of suspected acute coronary syndromes (ACS) in patients presenting to the ED with chest pain (CP) remains one of the biggest challenges. Several scoring systems such as HEART, TIMI and GRACE scores have been used to help physicians estimate cardiac mortality risk. However, these

Background Carotid 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. Methods Adult 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. Findings CIH-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. Interpretation Increased 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. Funding The Department of Physiology, and APC Microbiome Ireland, UCC.

BackgroundThe past 15 years have seen a growing interest in early intervention and detection of psychosis before the onset of the first episode. Recent proposals to include a psychosis risk syndrome (PRS) in DSM 5 have focused attention on the evidence base achieved to date in this field.AimsThis article aims to (1) review the underlying principles of early identification and intervention during the pre-psychotic phase, (2) summarise the naturalistic follow-up studies conducted to date in this ‘at-risk’ population, (3) discuss the identified clinical risk factors for transition to psychosis, (4) summarise the interventional studies both psychological and pharmacological completed to date and (5) briefly discuss the controversy around the proposed inclusion of the PRS in DSM 5.MethodsElectronic databases EmBase, MedLine and PsychInfo were searched using the keywords ultra-high risk/at-risk mental state/risk syndrome/pre-psychotic/prodrome/prodromal and psychosis/schizophrenia.ResultsThe evidence suggests that it is possible to identify individuals who may be at risk of developing psychosis. Results from intervention studies, mostly involving second-generation antipsychotics and cognitive behavioural therapy, are currently insufficient to make treatment recommendations for this group. The emerging research with regard to possible neuroprotective factors such as omega fatty acids is promising, but will require replication in larger cohorts before it can be recommended.

Through a semi-automatic analysis of tweets, we show that Twitter users not only express Medication Non-Adherence (MNA) in social media but also their reasons for not complying; further research is necessary to fully extract automatically and analyze this information, in order to facilitate the use of this data in epidemiological studies.

Political elites provide a unique perspective on political socialization. By examining these individuals’ experiences and viewpoints, scholars have an opportunity to evaluate methods to increase political engagement among the mass public. This paper undertakes such an exploration using a 2013 mail survey of Electoral College members, specifically considering responses to an open-ended question about electors’ first political memories, accounting for respondents’ age, partisanship, and gender. This study finds that elites’ earliest political memories often involve electoral—particularly presidential—politics and note these findings’ implications for political socialization scholars.

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.

According to the National Institutes of Health (NIH), precision medicine is "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person." Although the text mining community has explored this realm for some years, the official endorsement and funding launched in 2015 with the Precision Medicine Initiative are beginning to bear fruit. This session sought to elicit participation of researchers with strong background in text mining and/or visualization who are actively collaborating with bench scientists and clinicians for the deployment of integrative approaches in precision medicine that could impact scientific discovery and advance the vision of precision medicine as a universal, accessible approach at the point of care.

Summary Background: 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. Conclusions: Over the recent years, there has been a continuing transition from lexical and rule-based systems to learning-based approaches, because of the growth of annotated data sets and advances in data science. For EHRs, publicly available annotated data is still scarce and this acts as an obstacle to research progress. On the contrary, research on social media mining has seen a rapid growth, particularly because the large amount of unlabeled data available via this resource compensates for the uncertainty inherent to the data. Effective mechanisms to filter out noise and for mapping social media expressions to standard medical concepts are crucial and latent research problems. Shared tasks and other competitive challenges have been driving factors behind the implementation of open systems, and they are likely to play an imperative role in the development of future systems.

Aim Ensuring a seamless transition from child to adult mental health services poses challenges for services worldwide. This is an important process in the ongoing 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 standardized 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 health-care professionals.

This paper examines whether there is a continued gender gap in the political interest and engagement of first-year college students enrolled in introductory American politics classes. Using data from a survey completed by over 2,000 students at 20 colleges and universities across the United States, we look for variations in students' plans to vote, parental interest in politics, attention to political events, plans to influence public policy, and plans to run for office. We present evidence that while male and female students register to vote and plan to go to the polls at similar rates, female students are less likely to follow politics, to have an interest in influencing public policy, and to consider running for office than their male peers. With these trends in mind, we offer a number of suggestions for instructors to adopt both in and outside the classroom in an effort to warm this chilly climate.