Woman critically injured in ride'malfunction' 10 hours agoShareBBCOn Sunday tarpaulin was seen around one of the rides, although it is not clear which ride suffered the malfunction A woman in her 40s is in hospital with life-threatening injuries after a funfair ride malfunctioned at a country show in south London, the Met Police has said. The incident happened during Lambeth Country Show in Brockwell Park at about 18:20 BST on Saturday. A man in his 40s is also being treated for "potentially life-threatening injuries", the force said. Lambeth Council said the investigation would "determine the cause of the malfunction". Two other people, a man in his 50s and an 11-year-old girl, were injured in the incident and have since been discharged from hospital, the Met said.

Background: Electronic Health Records hold detailed longitudinal information about each patient's health status and general clinical history, a large portion of which is stored within the unstructured text. Existing approaches focus mostly on structured data and a subset of single-domain outcomes. We explore how temporal modelling of patients from free text and structured data, using deep generative transformers can be used to forecast a wide range of future disorders, substances, procedures or findings. Methods: We present Foresight, a novel transformer-based pipeline that uses named entity recognition and linking tools to convert document text into structured, coded concepts, followed by providing probabilistic forecasts for future medical events such as disorders, substances, procedures and findings. We processed the entire free-text portion from three different hospital datasets totalling 811336 patients covering both physical and mental health. Findings: On tests in two UK hospitals (King's College Hospital, South London and Maudsley) and the US MIMIC-III dataset precision@10 0.68, 0.76 and 0.88 was achieved for forecasting the next disorder in a patient timeline, while precision@10 of 0.80, 0.81 and 0.91 was achieved for forecasting the next biomedical concept. Foresight was also validated on 34 synthetic patient timelines by five clinicians and achieved relevancy of 97% for the top forecasted candidate disorder. As a generative model, it can forecast follow-on biomedical concepts for as many steps as required. Interpretation: Foresight is a general-purpose model for biomedical concept modelling that can be used for real-world risk forecasting, virtual trials and clinical research to study the progression of disorders, simulate interventions and counterfactuals, and educational purposes.

South London and Maudsley NHS Foundation Trust is working with Virgin Media O2 to switch on the UK's first 5G-connected hospital. The switch-on is part of Maudsley Digital Lab's series of digital health and innovation trials funded by NHS Digital. The trials are investigating the efficiency, safety and security benefits of using smart, 5G-connected technologies in NHS hospitals – including IoT (Internet of Things), AR (Augmented Reality) and AI (Artificial Intelligence). Trials are now live across two wards at Bethlem Royal Hospital in South London. These include dedicated, near-real-time connectivity to power e-Observations, where clinicians use handheld devices to update patient records.

Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of Information Extraction (IE) technologies to enable clinical analysis. We present the open source Medical Concept Annotation Toolkit (MedCAT) that provides: a) a novel self-supervised machine learning algorithm for extracting concepts using any concept vocabulary including UMLS/SNOMED-CT; b) a feature-rich annotation interface for customizing and training IE models; and c) integrations to the broader CogStack ecosystem for vendor-agnostic health system deployment. We show improved performance in extracting UMLS concepts from open datasets ( F1 0.467-0.791 vs 0.384-0.691). Further real-world validation demonstrates SNOMED-CT extraction at 3 large London hospitals with self-supervised training over ~8.8B words from ~17M clinical records and further fine-tuning with ~6K clinician annotated examples. We show strong transferability ( F1 >0.94) between hospitals, datasets and concept types indicating cross-domain EHR-agnostic utility for accelerated clinical and research use cases.

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