A change language for ontologies and knowledge graphs.

Ontologies and knowledge graphs (KGs) are general-purpose computable representations of some domain, such as human anatomy, and are frequently a crucial part of modern information systems. Most of these structures change over time, incorporating new knowledge or information that was previously missing. Managing these changes is a challenge, both in terms of communicating changes to users and providing mechanisms to make it easier for multiple stakeholders to contribute.

Clinical entity augmented retrieval for clinical information extraction.

Large language models (LLMs) with retrieval-augmented generation (RAG) have improved information extraction over previous methods, yet their reliance on embeddings often leads to inefficient retrieval. We introduce CLinical Entity Augmented Retrieval (CLEAR), a RAG pipeline that retrieves information using entities. We compared CLEAR to embedding RAG and full-note approaches for extracting 18 variables using six LLMs across 20,000 clinical notes.

National assessment on the frequency of pain medication prescribed for intrauterine device insertion procedures within the Veterans Affairs Health Care System.

Background: The intrauterine device (IUD) is a highly effective form of long-acting reversible contraception, widely recognized for its convenience and efficacy. Despite its benefits, many patients report moderate to severe pain during and after their IUD insertion procedure. Furthermore, reports suggest significant variability in pain control medications, including no adequate pain medication.

Toward expert-level medical question answering with large language models.

Large language models (LLMs) have shown promise in medical question answering, with Med-PaLM being the first to exceed a 'passing' score in United States Medical Licensing Examination style questions. However, challenges remain in long-form medical question answering and handling real-world workflows. Here, we present Med-PaLM 2, which bridges these gaps with a combination of base LLM improvements, medical domain fine-tuning and new strategies for improving reasoning and grounding through ensemble refinement and chain of retrieval.

Construction, Deployment, and Usage of the Human Reference Atlas Knowledge Graph for Linked Open Data.

The Human Reference Atlas (HRA) for the healthy, adult body is developed by a team of international, interdisciplinary experts across 20+ consortia. It provides standard terminologies and data structures for describing specimens, biological structures, and spatial positions of experimental datasets and ontology-linked reference anatomical structures (AS), cell types (CT), and biomarkers (B). We introduce the HRA Knowledge Graph (KG) as central data resource for HRA v2.

regionalpcs improve discovery of DNA methylation associations with complex traits.

We have developed the regionalpcs method, an approach for summarizing gene-level methylation. regionalpcs addresses the challenge of deciphering complex epigenetic mechanisms in diseases like Alzheimer's disease. In contrast to averaging, regionalpcs uses principal components analysis to capture complex methylation patterns across gene regions.

Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT-Based Radiomic Features in Non-Small Cell Lung Cancer.

Background: Non-small-cell lung cancer (NSCLC) remains a global health challenge, driving morbidity and mortality. The emerging field of radiogenomics utilizes statistical methods to correlate radiographic tumor features with genomic characteristics from biopsy samples. Radiomic techniques automate the precise extraction of imaging features from tumor regions in radiographic scans, which are subjected to machine learning (ML) to predict genomic attributes.

Hospitalization prediction from the emergency department using computer vision AI with short patient video clips.

In this study, we investigate the performance of computer vision AI algorithms in predicting patient disposition from the emergency department (ED) using short video clips. Clinicians often use "eye-balling" or clinical gestalt to aid in triage, based on brief observations. We hypothesize that AI can similarly use patient appearance for disposition prediction.

Multicohort Analysis of Bronchial Epithelial Cell Expression in Healthy Subjects and Patients with Asthma Reveals Four Clinically Distinct Clusters.

Asthma is a heterogeneous disease with variable presentation and characteristics. There is a critical need to identify underlying molecular endotypes of asthma. We performed the largest transcriptomic analysis of 808 bronchial epithelial cell (BEC) samples across 11 independent cohorts, including 3 cohorts from the Severe Asthma Research Program (SARP).

Establishing best practices in large language model research: an application to repeat prompting.

Objectives: We aimed to demonstrate the importance of establishing best practices in large language model research, using repeat prompting as an illustrative example.

Materials And Methods: Using data from a prior study investigating potential model bias in peer review of medical abstracts, we compared methods that ignore correlation in model outputs from repeated prompting with a random effects method that accounts for this correlation.

Results: High correlation within groups was found when repeatedly prompting the model, with intraclass correlation coefficient of 0.

Session Introduction: AI and Machine Learning in Clinical Medicine: Generative and Interactive Systems at the Human-Machine Interface.

Artificial Intelligence (AI) technologies are increasingly capable of processing complex and multilayered datasets. Innovations in generative AI and deep learning have notably enhanced the extraction of insights from both unstructured texts, images, and structured data alike. These breakthroughs in AI technology have spurred a wave of research in the medical field, leading to the creation of a variety of tools aimed at improving clinical decision-making, patient monitoring, image analysis, and emergency response systems.

Multi-modal analysis reveals tumor and immune features distinguishing EBV-positive and EBV-negative post-transplant lymphoproliferative disorders.

The oncogenic Epstein-Barr virus (EBV) can drive tumorigenesis with disrupted host immunity, causing malignancies including post-transplant lymphoproliferative disorders (PTLDs). PTLD can also arise in the absence of EBV, but the biological differences underlying EBV(+) and EBV(-) B cell PTLD and the associated host-EBV-tumor interactions remain poorly understood. Here, we reveal the core differences between EBV(+) and EBV(-) PTLD, characterized by increased expression of genes related to immune processes or DNA interactions, respectively, and the augmented ability of EBV(+) PTLD B cells to modulate the tumor microenvironment through elaboration of monocyte-attracting cytokines/chemokines.

Current Developments from Silicon Valley - How Artificial Intelligence is Changing Gynecology and Obstetrics.

Artificial intelligence (AI) has become an omnipresent topic in the media. Lively discussions are being held on how AI could revolutionize the global healthcare landscape. The development of innovative AI models, including in the medical sector, is increasingly dominated by large high-tech companies.

The text2term tool to map free-text descriptions of biomedical terms to ontologies.

There is an ongoing need for scalable tools to aid researchers in both retrospective and prospective standardization of discrete entity types-such as disease names, cell types, or chemicals-that are used in metadata associated with biomedical data. When metadata are not well-structured or precise, the associated data are harder to find and are often burdensome to reuse, analyze, or integrate with other datasets due to the upfront curation effort required to make the data usable-typically through retrospective standardization and cleaning of the (meta)data. With the goal of facilitating the task of standardizing metadata-either in bulk or in a one-by-one fashion, e.

International multi-cohort analysis identifies novel framework for quantifying immune dysregulation in critical illness: results of the SUBSPACE consortium.

Progress in the management of critical care syndromes such as sepsis, Acute Respiratory Distress Syndrome (ARDS), and trauma has slowed over the last two decades, limited by the inherent heterogeneity within syndromic illnesses. Numerous immune endotypes have been proposed in sepsis and critical care, however the overlap of the endotypes is unclear, limiting clinical translation. The SUBSPACE consortium is an international consortium that aims to advance precision medicine through the sharing of transcriptomic data.

Learning from the EHR to implement AI in healthcare.

The introduction of the electronic health record was heralded as a technology solution to improve care quality and efficiency, but these tools have contributed to increased administrative burden and burnout for clinicians. Today, artificial intelligence is receiving much of the same attention and promises as electronic health records. Can healthcare learn from the failures of electronic health records to maximize the potential of artificial intelligence?

Sialylated IgG induces the transcription factor REST in alveolar macrophages to protect against lung inflammation and severe influenza disease.

While most respiratory viral infections resolve with little harm to the host, severe symptoms arise when infection triggers an aberrant inflammatory response that damages lung tissue. Host regulators of virally induced lung inflammation have not been well defined. Here, we show that enrichment for sialylated, but not asialylated immunoglobulin G (IgG), predicted mild influenza disease in humans and was broadly protective against heterologous influenza viruses in a murine challenge model.

Sequence modeling and design from molecular to genome scale with Evo.

The genome is a sequence that encodes the DNA, RNA, and proteins that orchestrate an organism's function. We present Evo, a long-context genomic foundation model with a frontier architecture trained on millions of prokaryotic and phage genomes, and report scaling laws on DNA to complement observations in language and vision. Evo generalizes across DNA, RNA, and proteins, enabling zero-shot function prediction competitive with domain-specific language models and the generation of functional CRISPR-Cas and transposon systems, representing the first examples of protein-RNA and protein-DNA codesign with a language model.

Thick Data Analytics (TDA): An Iterative and Inductive Framework for Algorithmic Improvement.

A gap remains between developing risk prediction models and deploying models to support real-world decision making, especially in high-stakes situations. Human-experts' reasoning abilities remain critical in identifying potential improvements and ensuring safety. We propose a (TDA) framework for eliciting and combining expert-human insight into the evaluation of models.

Recommendations for Clinicians, Technologists, and Healthcare Organizations on the Use of Generative Artificial Intelligence in Medicine: A Position Statement from the Society of General Internal Medicine.

Generative artificial intelligence (generative AI) is a new technology with potentially broad applications across important domains of healthcare, but serious questions remain about how to balance the promise of generative AI against unintended consequences from adoption of these tools. In this position statement, we provide recommendations on behalf of the Society of General Internal Medicine on how clinicians, technologists, and healthcare organizations can approach the use of these tools. We focus on three major domains of medical practice where clinicians and technology experts believe generative AI will have substantial immediate and long-term impacts: clinical decision-making, health systems optimization, and the patient-physician relationship.

Large Language Model Influence on Diagnostic Reasoning: A Randomized Clinical Trial.

Importance: Large language models (LLMs) have shown promise in their performance on both multiple-choice and open-ended medical reasoning examinations, but it remains unknown whether the use of such tools improves physician diagnostic reasoning.

Objective: To assess the effect of an LLM on physicians' diagnostic reasoning compared with conventional resources.

Design, Setting, And Participants: A single-blind randomized clinical trial was conducted from November 29 to December 29, 2023.