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Knowledge gained from cohort studies has dramatically advanced both public and precision health. The All of Us Research Program seeks to enroll 1 million diverse participants who share multiple sources of data, providing unique opportunities for research. It is important to understand the phenomic profiles of its participants to conduct research in this cohort.

To measure pediatrician adherence to evidence-based guidelines in the treatment of young children with attention-deficit/hyperactivity disorder (ADHD) in a diverse healthcare system using natural language processing (NLP) techniques.

Environmental health (EH) services in the United States lag behind other areas of public health and health care with respect to information system interoperability and data sharing. This is partly due to an absence of well-defined use cases, the lack of direct economic drivers and resources to improve, the multiple jurisdictional elements that govern EH services across the United States, and no central organization to drive modernization of EH data [...]

To enhance the Business Process Management (BPM)+ Healthcare language portfolio by incorporating knowledge types not previously covered and to improve the overall effectiveness and expressiveness of the suite to improve Clinical Knowledge Interoperability.

The aim of the Social Media Mining for Health Applications (#SMM4H) shared tasks is to take a community-driven approach to address the natural language processing and machine learning challenges inherent to utilizing social media data for health informatics. In this paper, we present the annotated corpora, a technical summary of participants' systems, and the performance results.

Long-term breast cancer survivors (BCS) constitute a complex group of patients, whose number is estimated to continue rising, such that, a dedicated long-term clinical follow-up is necessary.

COVID-19, since its emergence in December 2019, has globally impacted research. Over 360 000 COVID-19-related manuscripts have been published on PubMed and preprint servers like medRxiv and bioRxiv, with preprints comprising about 15% of all manuscripts. Yet, the role and impact of preprints on COVID-19 research and evidence synthesis remain uncertain.

To develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches.

Question answering (QA) systems have the potential to improve the quality of clinical care by providing health professionals with the latest and most relevant evidence. However, QA systems have not been widely adopted. This systematic review aims to characterize current medical QA systems, assess their suitability for healthcare, and identify areas of improvement.