More than 7000 rare diseases affect over 400 million people, posing significant challenges for medical research and healthcare. The integration of precision medicine with artificial intelligence offers promising solutions. This work introduces a classifier developed to discern whether research and news articles pertain to rare or non-rare diseases. Our methodology involves extracting 709 rare disease MeSH terms from Mondo and MeSH to improve rare disease categorization. We evaluate our classifier on abstracts from PubMed/MEDLINE and an expert-annotated news dataset, which includes news articles on four selected rare neurodevelopmental disorders (NDDs)-considered the largest category of rare diseases-from a total of 16 analyzed. We achieved F1 scores of 85% for abstracts and 71% for news articles, demonstrating robustness across both datasets and highlighting the potential of integrating artificial intelligence and ontologies to improve disease classification. Although the results are promising, they also indicate the need for further refinement in managing data heterogeneity. Our classifier improves the identification and categorization of medical information, essential for advancing research, enhancing information access, influencing policy, and supporting personalized treatments. Future work will focus on expanding disease classification to distinguish between attributes such as infectious and hereditary diseases, addressing data heterogeneity, and incorporating multilingual capabilities.

Interstitial lung disease is a common complication of anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV). It is seen most commonly in microscopic polyangiitis owing to the pathogenic effect of myeloperoxidase in the lung. Oxidative stress, neutrophil elastase release, and expression of inflammatory proteins by neutrophil extracellular traps result in fibroblast proliferation and differentiation and therefore fibrosis. Usually, interstitial pneumonia pattern fibrosis is common and associated with poor survival. Treatment for patients with AAV and interstitial lung disease lacks evidence, and those with vasculitis are treated with immunosuppression, whereas those with progressive fibrosis may well benefit from antifibrotic therapy.

Adaptive expertise represents the combination of both efficient problem-solving for clinical encounters with known solutions, as well as the ability to learn and innovate when faced with a novel challenge. Fostering adaptive expertise requires careful approaches to instructional design to emphasize deeper, more effortful learning. These teaching strategies are time-intensive, effortful, and challenging to implement in health professions education curricula. The authors are educators whose missions encompass the medical education continuum, from undergraduate through to organizational learning. Each has grappled with how to promote adaptive expertise development in their context. They describe themes drawn from educational experiences at these various learner levels to illustrate strategies that may be used to cultivate adaptive expertise.At Vanderbilt University School of Medicine, a restructuring of the medical school curriculum provided multiple opportunities to use specific curricular strategies to foster adaptive expertise development. The advantage for students in terms of future learning had to be rationalized against assessments that are more short-term in nature. In a consortium of emergency medicine residency programs, a diversity of instructional approaches was deployed to foster adaptive expertise within complex clinical learning environments. Here the value of adaptive expertise approaches must be balanced with the efficiency imperative in clinical care. At Mayo Clinic, an existing continuous professional development program was used to orient the entire organization towards an adaptive expertise mindset, with each individual making a contribution to the shift.The different contexts illustrate both the flexibility of the adaptive expertise conceptualization and the need to customize the educational approach to the developmental stage of the learner. In particular, an important benefit of teaching to adaptive expertise is the opportunity to influence individual professional identity formation to ensure that clinicians of the future value deeper, more effortful learning strategies throughout their careers.

BACKGROUND: Primary care has played a central role in the community response to the coronavirus disease-19 (COVID-19) pandemic. The use of the National Early Warning Score 2 (NEWS2) has been advocated as a tool to guide escalation decisions in the community. The performance of this tool applied in this context is unclear.
OBJECTIVE: To evaluate the process of escalation of care to the hospital within a primary care assessment centre (PCAC) designed to assess patients with suspected COVID-19 in the community.
METHODS: A retrospective service evaluation of all adult patients assessed between 30 March and 22 April 2020 within a COVID-19 primary care assessment centre within Sandwell West Birmingham CCG.
METHODS: A database of patient demographics, healthcare interactions and physiological observations was constructed. NEWS2 and CRB65 scores were calculated retrospectively. The proportion of patients escalated was within risk groups defined by NHSE guidelines in place during the evaluation period was determined.
RESULTS: A total of 150 patients were identified. Following assessment 13.3% (n = 20) patients were deemed to require escalation. The proportion of patients escalated with a NEWS2 greater than or equal to 3 was 46.9% (95% CI 30.8-63.6%). The proportion of patients escalated to secondary care using NHSE defined risk thresholds was 0% in the green group, 22% (n = 4) in the amber group, and 81.3% (n = 13) in the red group.
CONCLUSIONS: Clinical decisions to escalate care to the hospital did not follow initial guidance written for the COVID-19 outbreak but were demonstrated to be safe.
In most cases, coronavirus disease-19 (COVID-19) is a mild illness that resolves on its own. Some patients develop severe disease requiring hospital treatment. Identifying which patients are likely to need hospital treatment is a challenge. Many GP practices have developed specific services designed to assess patients with suspected COVID-19 and establish whether hospital treatment is necessary. We evaluated a service providing this function in Birmingham. We examined the care pathway of 150 patients assessed within the service to established factors associated with the need for hospital assessment. We found a national decision tool designed to aid the process was a poor descriptor of what happened in practice.

In the age of big data, the amount of scientific information available online dwarfs the ability of current tools to support researchers in locating and securing access to the necessary materials. Well-structured open data and the smart systems that make the appropriate use of it are invaluable and can help health researchers and professionals to find the appropriate information by, e.g., configuring the monitoring of information or refining a specific query on a disease.
We present an automated text classifier approach based on the MEDLINE/MeSH thesaurus, trained on the manual annotation of more than 26 million expert-annotated scientific abstracts. The classifier was developed tailor-fit to the public health and health research domain experts, in the light of their specific challenges and needs. We have applied the proposed methodology on three specific health domains: the Coronavirus, Mental Health and Diabetes, considering the pertinence of the first, and the known relations with the other two health topics.
A classifier is trained on the MEDLINE dataset that can automatically annotate text, such as scientific articles, news articles or medical reports with relevant concepts from the MeSH thesaurus.
The proposed text classifier shows promising results in the evaluation of health-related news. The application of the developed classifier enables the exploration of news and extraction of health-related insights, based on the MeSH thesaurus, through a similar workflow as in the usage of PubMed, with which most health researchers are familiar.

OBJECTIVE: Identifying new relations between medical entities, such as drugs, diseases, and side effects, is typically a resource-intensive task, involving experimentation and clinical trials. The increased availability of related data and curated knowledge enables a computational approach to this task, notably by training models to predict likely relations. Such models rely on meaningful representations of the medical entities being studied. We propose a generic features vector representation that leverages co-occurrences of medical terms, linked with PubMed citations.
METHODS: We demonstrate the usefulness of the proposed representation by inferring two types of relations: a drug causes a side effect and a drug treats an indication. To predict these relations and assess their effectiveness, we applied 2 modeling approaches: multi-task modeling using neural networks and single-task modeling based on gradient boosting machines and logistic regression.
RESULTS: These trained models, which predict either side effects or indications, obtained significantly better results than baseline models that use a single direct co-occurrence feature. The results demonstrate the advantage of a comprehensive representation.
CONCLUSIONS: Selecting the appropriate representation has an immense impact on the predictive performance of machine learning models. Our proposed representation is powerful, as it spans multiple medical domains and can be used to predict a wide range of relation types.
CONCLUSIONS: The discovery of new relations between various medical entities can be translated into meaningful insights, for example, related to drug development or disease understanding. Our representation of medical entities can be used to train models that predict such relations, thus accelerating healthcare-related discoveries.