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This resource defines the Hierarchical Event Descriptor (HED) specification, including the core specification with detailed rules about the handling of the vocabulary, tool behavior, and errors. This specification lays out the rules that HED-compliant tools must follow to correctly handle HED annotations. It is meant for tool developers and for users who need to understand the details of the behavior. If you are new to HED, please visit the HED homepage or the HED resources site. The current, officially released specification can also be browsed in HTML format. The HED specification is maintained in the GitHub hed-specification repository which is part of the GitHub HED Standard organization. HED is the annotation standard for events and other tabular metadata in the Brain Imaging Data Structure (BIDS) standard. Release 3.2.0 added the Inset tag for labeling intermediate points as well as partnered and rooted library schemas and the curly braces notation for sidecars..

Accuracy of medication data in electronic health records (EHRs) is crucial for patient care and research, but many studies have shown that medication lists frequently contain errors. In contrast, physicians often pay more attention to the clinical notes and record medication information in them. The medication information in notes may be used for medication reconciliation to improve the medication lists' accuracy. However, accurately extracting patient's current medications from free-text narratives is challenging. In this study, we first explored the discrepancies between medication documentation in medication lists and progress notes for glaucoma patients by manually reviewing patients' charts. Next, we developed and validated a named entity recognition model to identify current medication and adherence from progress notes. Lastly, a prototype tool for medication reconciliation using the developed model was demonstrated. In the future, the model has the potential to be incorporated into the EHR system to help with realtime medication reconciliation.

With the arrival of effective COVID-19 vaccines, the fight against the ongoing global pandemic entered a new stage. The question shifted towards how to vaccinate as many people as quickly as possible. Understanding the reasons people choose to get vaccinated or not is integral in informing immunization campaigns. But who are the unvaccinated? This is determined by using cross-national data from the Survey of Health, Ageing and Retirement in Europe (SHARE). We investigate the interrelation of the (un)willingness to be vaccinated with social and health aspects as well as the economic situation of our sample of adults in Europe, aged 50 and above. We find that respondents’ economic situation as well as diagnosed physical illnesses and education show the strongest relation to vaccination hesitancy. Our results provide a comprehensive picture of influential factors of older adults’ vaccination behaviour that can advance the success of the immunisation campaigns in Europe.

With the arrival of effective COVID-19 vaccines, the fight against the ongoing global pandemic entered a new stage. The question shifted towards how to vaccinate as many people as quickly as possible. Understanding the reasons people choose to get vaccinated or not is integral in informing immunization campaigns. But who are the unvaccinated? This is determined by using cross-national data from the Survey of Health, Ageing and Retirement in Europe (SHARE). We investigate the interrelation of the (un)willingness to be vaccinated with social and health aspects as well as the economic situation of our sample of adults in Europe, aged 50 and above. We find that respondents' economic situation as well as diagnosed physical illnesses and education show the strongest relation to vaccination hesitancy. Our results provide a comprehensive picture of influential factors of older adults' vaccination behaviour that can advance the success of the immunisation campaigns in Europe. Online-Appendix: https://doi.org/10.15464/easy.2022.01a Dank effektiver COVID-19-Impfstoffe begann eine neue Phase des Kampfes gegen die Pandemie. Die Frage lautet nun: Wie können wir viele Menschen schnellstmöglich impfen? Hierbei ist im Hinblick auf die Verbesserung der Impfkampagnen entscheidend zu verstehen, warum Menschen sich für oder gegen eine Impfung entscheiden. Aber wer sind die Ungeimpften? Das soll anhand einer Untersuchung der Beziehung zwischen der Impfbereitschaft von älteren Befragten und sozialen, gesundheitlichen und wirtschaftlichen Faktoren festgestellt werden. Basierend auf Daten des Survey of Health, Ageing and Retirement in Europe (SHARE), kommen wir zu dem Schluss, dass die finanzielle Lage von Befragten, diagnostizierte physische Krankheiten und der Bildungsstand die stärksten Zusammenhänge zur Impfskepsis aufweisen. Unsere Ergebnisse stellen ein umfassendes Bild der relevanten Faktoren für die ältere Bevölkerung dar und könnten zu wachsendem Erfolg der Impfkampagnen in Europa beitragen. Online-Appendix: s. https://doi.org/10.15464/easy.2022.01a

In the first half of the twentieth century, the rate of death from infectious disease in the United States fell precipitously. Although this decline is well-known and well-documented, there is surprisingly little evidence about whether it took place uniformly across the regions of the United States. We use data on infectious disease deaths from all reporting U.S. cities to describe regional patterns in the decline of urban infectious mortality from 1900 to 1948. We report three main results. First, urban infectious mortality was higher in the South in every year from 1900 to 1948. Second, infectious mortality declined later in southern cities than in cities in the other regions. Third, comparatively high infectious mortality in southern cities was driven primarily by extremely high infectious mortality among African Americans. From 1906 to 1920, African Americans in cities experienced a rate of death from infectious disease that was greater than what urban whites experienced during the 1918 flu pandemic.

Social media presents a rich opportunity to gather health information with limited intervention through the analysis of completely unstructured and unlabeled microposts. We sought to estimate the health-related quality of life (HRQOL) of Twitter users using automated semantic processing methods. We collected tweets from 878 Twitter users recruited through online solicitation and in-person contact with patients. All participants completed the four-item Centers for Disease Control Healthy Days Questionnaire at the time of enrollment and 30 days later to measure "ground truth" HRQOL. We used a combination of document frequency analysis, sentiment analysis, topic analysis, and concept mapping to extract features from tweets, which we then used to estimate dichotomized HRQOL ("high" vs. "low") using logistic regression. Binary HRQOL status was estimated with moderate performance (AUC = 0.64). This result indicates that free-range social media data only offers a window into HRQOL, but does not afford direct access to current health status.

PICO (Population/problem, Intervention, Comparison, and Outcome) is widely adopted for formulating clinical questions to retrieve evidence from the literature. It plays a crucial role in Evidence-Based Medicine (EBM). This paper contributes a scalable deep learning method to extract PICO statements from RCT articles. It was trained on a small set of richly annotated PubMed abstracts using an LSTM-CRF model. By initializing our model with pretrained parameters from a large related corpus, we improved the model performance significantly with a minimal feature set. Our method has advantages in minimizing the need for laborious feature handcrafting and in avoiding the need for large shared annotated data by reusing related corpora in pretraining with a deep neural network.

The adverse events of the dietary supplements should be subject to scrutiny due to their growing clinical application and consumption among U.S. adults. An effective method for mining and grouping the adverse events of the dietary supplements is to evaluate product labeling for the rapidly increasing number of new products available in the market. In this study, the adverse events information was extracted from the product labels stored in the Dietary Supplement Label Data-base (DSLD) and analyzed by topic modeling techniques, specifically Latent Dirichlet Allocation (LDA). Among the 50 topics generated by LDA, eight topics were manually evaluated, with topic relatedness ranging from 58.8% to 100% on the product level, and 57.1% to 100% on the ingredient level. Five out of these eight topics were coherent groupings of the dietary supplements based on their adverse events. The results demonstrated that LDA is able to group supplements with similar adverse events based on the dietary supplement labels. Such information can be potentially used by consumers to more safely use dietary supplements.