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COVID-19 has given rise to a lot of malicious content online, including hate speech, online abuse, and misinformation. British MPs have also received abuse and hate on social media during this time. To understand and contextualise the level of abuse MPs receive, we consider how ministers use social media to communicate about the pandemic, and the citizen engagement that this generates. The focus of the paper is on a large-scale, mixed-methods study of abusive and antagonistic responses to UK politicians on Twitter, during the pandemic from early February to late May 2020. We find that pressing subjects such as financial concerns attract high levels of engagement, but not necessarily abusive dialogue. Rather, criticising authorities appears to attract higher levels of abuse during this period of the pandemic. In addition, communicating about subjects like racism and inequality may result in accusations of virtue signalling or pandering by some users. This work contributes to the wider understanding of abusive language online, in particular that which is directed at public officials., Competing Interests: Conflicts of interestNo conflicts of interest or competing interests., (© The Author(s) 2020.)

Background: Traditional health information systems are generally devised to support clinical data collection at the point of care. However, as the significance of the modern information economy expands in scope and permeates the healthcare domain, there is an increasing urgency for healthcare organisations to offer information systems that address the expectations of clinicians, researchers and the business intelligence community alike. Amongst other emergent requirements, the principal unmet need might be defined as the 3R principle (right data, right place, right time) to address deficiencies in organisational data flow while retaining the strict information governance policies that apply within the UK National Health Service (NHS). Here, we describe our work on creating and deploying a low cost structured and unstructured information retrieval and extraction architecture within King's College Hospital, the management of governance concerns and the associated use cases and cost saving opportunities that such components present., Results: To date, our CogStack architecture has processed over 300 million lines of clinical data, making it available for internal service improvement projects at King's College London. On generated data designed to simulate real world clinical text, our de-identification algorithm achieved up to 94% precision and up to 96% recall., Conclusion: We describe a toolkit which we feel is of huge value to the UK (and beyond) healthcare community. It is the only open source, easily deployable solution designed for the UK healthcare environment, in a landscape populated by expensive proprietary systems. Solutions such as these provide a crucial foundation for the genomic revolution in medicine.

Objective: Unlocking the data contained within both structured and unstructured components of electronic health records (EHRs) has the potential to provide a step change in data available for secondary research use, generation of actionable medical insights, hospital management, and trial recruitment. To achieve this, we implemented SemEHR, an open source semantic search and analytics tool for EHRs., Methods: SemEHR implements a generic information extraction (IE) and retrieval infrastructure by identifying contextualized mentions of a wide range of biomedical concepts within EHRs. Natural language processing annotations are further assembled at the patient level and extended with EHR-specific knowledge to generate a timeline for each patient. The semantic data are serviced via ontology-based search and analytics interfaces., Results: SemEHR has been deployed at a number of UK hospitals, including the Clinical Record Interactive Search, an anonymized replica of the EHR of the UK South London and Maudsley National Health Service Foundation Trust, one of Europe's largest providers of mental health services. In 2 Clinical Record Interactive Search-based studies, SemEHR achieved 93% (hepatitis C) and 99% (HIV) F-measure results in identifying true positive patients. At King's College Hospital in London, as part of the CogStack program (github.com/cogstack), SemEHR is being used to recruit patients into the UK Department of Health 100 000 Genomes Project (genomicsengland.co.uk). The validation study suggests that the tool can validate previously recruited cases and is very fast at searching phenotypes; time for recruitment criteria checking was reduced from days to minutes. Validated on open intensive care EHR data, Medical Information Mart for Intensive Care III, the vital signs extracted by SemEHR can achieve around 97% accuracy., Conclusion: Results from the multiple case studies demonstrate SemEHR's efficiency: weeks or months of work can be done within hours or minutes in some cases. SemEHR provides a more comprehensive view of patients, bringing in more and unexpected insight compared to study-oriented bespoke IE systems. SemEHR is open source, available at https://github.com/CogStack/SemEHR.

Objectives: We sought to use natural language processing to develop a suite of language models to capture key symptoms of severe mental illness (SMI) from clinical text, to facilitate the secondary use of mental healthcare data in research., Design: Development and validation of information extraction applications for ascertaining symptoms of SMI in routine mental health records using the Clinical Record Interactive Search (CRIS) data resource; description of their distribution in a corpus of discharge summaries., Setting: Electronic records from a large mental healthcare provider serving a geographic catchment of 1.2 million residents in four boroughs of south London, UK., Participants: The distribution of derived symptoms was described in 23 128 discharge summaries from 7962 patients who had received an SMI diagnosis, and 13 496 discharge summaries from 7575 patients who had received a non-SMI diagnosis., Outcome Measures: Fifty SMI symptoms were identified by a team of psychiatrists for extraction based on salience and linguistic consistency in records, broadly categorised under positive, negative, disorganisation, manic and catatonic subgroups. Text models for each symptom were generated using the TextHunter tool and the CRIS database., Results: We extracted data for 46 symptoms with a median F1 score of 0.88. Four symptom models performed poorly and were excluded. From the corpus of discharge summaries, it was possible to extract symptomatology in 87% of patients with SMI and 60% of patients with non-SMI diagnosis., Conclusions: This work demonstrates the possibility of automatically extracting a broad range of SMI symptoms from English text discharge summaries for patients with an SMI diagnosis. Descriptive data also indicated that most symptoms cut across diagnoses, rather than being restricted to particular groups., Competing Interests: RJ, HS and RS have received research funding from Roche, Pfizer, J&J and Lundbeck., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.)

Objectives: To identify negative symptoms in the clinical records of a large sample of patients with schizophrenia using natural language processing and assess their relationship with clinical outcomes., Design: Observational study using an anonymised electronic health record case register., Setting: South London and Maudsley NHS Trust (SLaM), a large provider of inpatient and community mental healthcare in the UK., Participants: 7678 patients with schizophrenia receiving care during 2011., Main Outcome Measures: Hospital admission, readmission and duration of admission., Results: 10 different negative symptoms were ascertained with precision statistics above 0.80. 41% of patients had 2 or more negative symptoms. Negative symptoms were associated with younger age, male gender and single marital status, and with increased likelihood of hospital admission (OR 1.24, 95% CI 1.10 to 1.39), longer duration of admission (β-coefficient 20.5 days, 7.6-33.5), and increased likelihood of readmission following discharge (OR 1.58, 1.28 to 1.95)., Conclusions: Negative symptoms were common and associated with adverse clinical outcomes, consistent with evidence that these symptoms account for much of the disability associated with schizophrenia. Natural language processing provides a means of conducting research in large representative samples of patients, using data recorded during routine clinical practice., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

In the present study, the incidence of severe brain injury was 600 percent higher for patients riding without a helmet and the incidence of all brain injuries was nearly twice as high in the nonhelmeted riders. All surviving patients with severe brain injury sustained residual long-term disability. The incidence of injury and death was much higher for motorcyclists than for occupants of automobiles involved in accidents. Riding a motorcycle is dangerous and riding without a helmet is fool-hardy. Helmets also protect the face, as facial fractures were twice as common in the nonhelmeted riders. There were no significant differences between nonhelmeted and helmeted motorcyclists in terms of overall injury as measured by an injury severity score of 16 or greater. Orthopedic injuries, in this study, were so common that orthopedic surgeons performed more major operations than all other surgical specialists combined. Depth of orthopedic coverage is essential to treat significant numbers of injured motorcyclists. Neurosurgeons are key members of a trauma care team. Helmet laws would help us utilize our limited neurosurgical capacity more effectively by reducing the incidence of brain injury. Medical professionals must educate the public regarding the societal and personal cost of unhelmeted motorcycle riding. Legislation mandating helmet usage for motorcycle riders must be sought.