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This study addresses the significant challenges posed by emerging SARS-CoV-2 variants, particularly in developing diagnostics and therapeutics. Drug repurposing is investigated by identifying critical regulatory proteins impacted by the virus, providing rapid and effective therapeutic solutions for better disease management.

To pilot a digital health technologies ecosystem known as project SingaporeWALK (Wearables and Apps for Community Living and Knowledge) that build capacity in older adults, senior center managers, health coaches, and caregivers in using health technologies (eg, wearables, apps, exergames) collaboratively in a gamified way for active aging.

The resurgence of syphilis in the United States presents a significant public health challenge. Much of the information needed for syphilis surveillance resides in electronic health records (EHRs). In this manuscript, we describe a surveillance platform for automating the extraction of EHR data, known as SmartChart Suite, and the results from a pilot.

To detect and classify features of stigmatizing and biased language in intensive care electronic health records (EHRs) using natural language processing techniques.

To assess the potential to adapt an existing technology regulatory model, namely the Clinical Laboratory Improvement Amendments (CLIA), for clinical artificial intelligence (AI).

Mild cognitive impairment and early-stage dementia significantly impact healthcare utilization and costs, yet more than half of affected patients remain underdiagnosed. This study leverages audio-recorded patient-nurse verbal communication in home healthcare settings to develop an artificial intelligence-based screening tool for early detection of cognitive decline.

To be usable, useful, and sustainable for families of children with medically complex conditions (CMC), digital interventions must account for the complex sociotechnical context in which these families provide care. CMC experience higher neighborhood socioeconomic disadvantage than other child populations, which has associations with CMC health. Neighborhoods may influence the structure and function of the array of caregivers CMC depend upon (ie, the caregiving network).

The automatic detection of stance on social media is an important task for public health applications, especially in the context of health crises. Unfortunately, existing models are typically trained on English corpora. Considering the benefits of extending research to other widely spoken languages, the goal of this study is to develop stance detection models for social media posts in Spanish.

To enhance the accuracy of information retrieval from pharmacovigilance (PV) databases by employing Large Language Models (LLMs) to convert natural language queries (NLQs) into Structured Query Language (SQL) queries, leveraging a business context document.

Digital health (patient portals, remote monitoring devices, video visits) is a routine part of health care, though the digital divide may affect access.