Multi-modal prediction of extracorporeal support-a resource intensive therapy, utilizing a large national database.

Objective: Extracorporeal membrane oxygenation (ECMO) is among the most resource-intensive therapies in critical care. The COVID-19 pandemic highlighted the lack of ECMO resource allocation tools. We aimed to develop a continuous ECMO risk prediction model to enhance patient triage and resource allocation.

Uncovering novel functions of the enigmatic, abundant, and active in a salt marsh ecosystem.

, particularly uncultured representatives, are one of the most abundant microbial groups in coastal salt marshes, dominating the belowground rhizosphere, where over half of plant biomass production occurs. However, this class generally remains poorly understood, particularly in a salt marsh context. Here, novel metagenome-assembled genomes (MAGs) were generated from the salt marsh rhizosphere representing , , JAAYZQ01, B4-G1, JAFGEY01, UCB3, and orders.

BioMedGraphica: An All-in-One Platform for Biomedical Prior Knowledge and Omic Signaling Graph Generation.

Artificial intelligence (AI) is revolutionizing scientific discovery because of its super capability, following the neural scaling laws, to integrate and analyze large-scale datasets to mine knowledge. Foundation models, large language models (LLMs) and large vision models (LVMs), are among the most important foundations paving the way for general AI by pre-training on massive domain-specific datasets. Different from the well annotated, formatted and integrated large textual and image datasets for LLMs and LVMs, biomedical knowledge and datasets are fragmented with data scattered across publications and inconsistent databases that often use diverse nomenclature systems in the field of AI for Precision Health and Medicine (AI4PHM).

Estimation of prevalence of autoimmune diseases in the United States using electronic health record data.

Background: Previous epidemiologic studies of autoimmune diseases in the United States (US) have included a limited number of diseases or used meta-analyses that rely on different data collection methods and analyses for each disease.

Methods: To estimate the prevalence of autoimmune diseases in the US, we used electronic health record data from six large medical systems in the US. We developed a software program using common methodology to compute the estimated prevalence of autoimmune diseases alone and in aggregate that can be readily used by other investigators to replicate or modify the analysis over time.

Toward an artificial intelligence code of conduct for health and healthcare: implications for the biomedical informatics community.

Introduction: The rapid advancement of artificial intelligence (AI) has led to significant transformations in health and healthcare. As AI technologies continue to evolve, there is an urgent need to establish a unified framework that guides the design, implementation, and evaluation of AI-driven interventions across individual and population health contexts.

Approach: In response to this need, the National Academy of Medicine (NAM) has initiated the development of an AI code of conduct (AICC) through its Digital Health Action Collaborative.