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6 recordsLinked to original sources

PubMed2026

Enterocutaneous Fistula-Associated Sepsis and Mortality: Development and Validation of a Multimodal Artificial Intelligence Prediction Model.

BACKGROUND: Predicting enterocutaneous fistula (ECF)-associated sepsis and mortality poses significant challenges in digital health care due to the disease's complexity and heterogeneous clinical manifestations. Current approaches that rely on single-modal data or traditional scoring systems often fail to capture the intricate immune-inflammatory dynamics and multisystem involvement in patients with ECF. OBJECTIVE: This study aims to develop an artificial intelligence (AI)-driven multimodal fusion model integrating clinical, imaging, and transcriptomic data for early prediction of ECF-associated sepsis and 28-day mortality, addressing the limitations of conventional single-dimensional models. METHODS: This study leveraged publicly available datasets (Medical Information Mart for Intensive Care III [MIMIC-III], electronic Intensive Care Unit [eICU], and The Cancer Genome Atlas) to construct a multimodal framework. Clinical parameters were processed using Extreme Gradient Boosting, abdominal imaging features were extracted via convolutional neural networks, and transcriptomic profiles were analyzed with variational autoencoders. A Transformer-based fusion network was employed for joint prediction and validated through cross-validation and external testing. Key features were identified using Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations interpretability algorithms, while immune regulatory mechanisms were explored via weighted gene co-expression network analysis. RESULTS: The multimodal model achieved an area under the curve (AUC) of 0.89 for predicting sepsis and 28-day mortality, outperforming unimodal models (clinical-only model, AUC 0.72, and imaging-only model, AUC 0.78). Critical predictors included Sequential Organ Failure Assessment score, lactate levels, intra-abdominal free fluid on imaging, and immunoregulatory genes (programmed death-ligand 1 [PD-L1] and indoleamine 2,3-dioxygenase 1 [IDO1]). Mechanistic analysis revealed distinct immune reprogramming in patients with sepsis, characterized by increased regulatory T cells and M2 macrophages, along with downregulated cluster of differentiation 8+ (CD8+) T cells. CONCLUSIONS: This multimodal AI model offers an innovative digital solution in medical informatics, enabling precise early risk stratification for ECF-associated sepsis. By integrating multisource data and providing interpretable insights into immune-inflammatory pathways, the model enhances health care quality for patients with ECF and paves the way for personalized intervention strategies.

Humans↗

PubMed2026

CAUSAL artificial intelligence and data-driven decision intelligence in personalized medicine: a review of healthcare informatics systems.

This review examines the integration of causal artificial intelligence (AI) and data-driven decision intelligence within healthcare informatics systems to advance personalized medicine and clinical decision-making. A narrative review methodology was employed, synthesizing interdisciplinary literature from major databases, including PubMed, Scopus, Web of Science, IEEE Xplore, and ScienceDirect. Studies focusing on causal inference, decision intelligence, and healthcare informatics applications in personalized medicine were included. Data were extracted on methodological approaches, healthcare settings, analytical techniques, and clinical applications, followed by thematic synthesis. Findings indicate that causal AI enhances clinical decision support by enabling estimation of treatment effects and simulation of intervention outcomes at the individual patient level. Integration of multimodal health data such as electronic health records, genomic data, and real-time monitoring improves prediction accuracy and supports tailored treatment strategies. Additionally, causal models improve interpretability, fostering clinician trust and facilitating transparent decision-making. Robust healthcare informatics infrastructures, including interoperable systems and data warehouses, were identified as critical enablers of causal analytics. Overall, causal AI represents a transformative advancement in healthcare analytics, supporting more informed, individualized, and evidence-based clinical decisions. Its integration within healthcare informatics systems has significant potential to improve patient outcomes and guide the future of intelligent, personalized healthcare delivery.

Precision Medicine↗

PubMed2025

Implementing a training resource for large-scale genomic data analysis in the All of Us Researcher Workbench.

A lack of representation in genomic research and limited access to computational training create barriers for many researchers seeking to analyze large-scale genetic datasets. The All of Us Research Program provides an unprecedented opportunity to address these gaps by offering genomic data from a broad range of participants, but its impact depends on equipping researchers with the necessary skills to use it effectively. The All of Us Biomedical Researcher (BR) Scholars Program at Baylor College of Medicine aims to break down these barriers by providing early-career researchers with hands-on training in computational genomics through the All of Us Evenings with Genetics Research Program. The year-long program begins with the faculty summit, an in-person computational boot camp that introduces scholars to foundational skills for using the All of Us dataset via a cloud-based research environment. The genomics tutorials focus on genome-wide association studies (GWASs), utilizing Jupyter Notebooks and the Hail computing framework to provide an accessible and scalable approach to large-scale data analysis. Scholars engage in hands-on exercises covering data preparation, quality control, association testing, and result interpretation. By the end of the summit, participants will have successfully conducted a GWAS, visualized key findings, and gained confidence in computational resource management. This initiative expands access to genomic research by equipping early-career researchers from a variety of backgrounds with the tools and knowledge to analyze All of Us data. By lowering barriers to entry and promoting the study of representative populations, the program fosters innovation in precision medicine and advances equity in genomic research.

Humans↗

PubMed2026

AI In Leukemia Diagnostics: Complementing the Pathologist's Role.

Artificial intelligence (AI) is reshaping every stage of leukemia diagnostics, from digital morphology and multiparameter flow cytometry to next-generation sequencing, multi-omics analysis, and emerging computational frontiers such as quantum-inspired feature selection. This review outlines how contemporary AI tools can automate labor-intensive quantitation, flag diagnostically salient patterns, and standardize interpretation, while the pathologist or hematologist retains authority over validation, context-specific integration, and clinical decision-making. We present an illustrative "human-in-the-loop" workflow that embeds AI modules within current laboratory information systems, emphasizing points where expert oversight mitigates algorithmic bias and resolves discordant findings. We further map the validator-integrator role across morphology, flow cytometry, and genomic/multi-omic interpretation and provide practical training competencies and use cases for AI-assisted hematopathology. Beyond technical deployment, the article addresses the educational transformation required for sustainable adoption. Drawing on international competency frameworks, including the Digital Health Competencies in Medical Education Framework and recently proposed AI-specific Entrustable Professional Activities, we map core skills that future hematopathologists must master: data-science literacy, critical appraisal of AI outputs, and ethical governance. We highlight evaluated training models such as the Pathology Informatics Essentials for Residents curriculum, Stanford Artificial Intelligence in Machine and Imaging workshops, and College of American Pathologists bootcamps and propose integration strategies adaptable across resource settings. By pairing rigorous validation with targeted education, AI can elevate rather than eclipse the diagnostic role of the leukemia specialist, enabling more timely, reproducible, and personalized patient care.

Humans↗

PubMed2022

Sex Differences in Health Conditions Associated with Sexual Assault in a Large Hospital Population.

INTRODUCTION: Sexual assault is an urgent public health concern with both immediate and long-lasting health consequences, affecting 44% of women and 25% of men during their lifetimes. Large studies are needed to understand the unique healthcare needs of this patient population. METHODS: We mined clinical notes to identify patients with a history of sexual assault in the electronic health record (EHR) at Vanderbilt University Medical Center (VUMC), a large university hospital in the Southeastern USA, from 1989 to 2021 (N = 3,376,424). Using a phenome-wide case-control study, we identified diagnoses co-occurring with disclosures of sexual assault. We performed interaction tests to examine whether sex modified any of these associations. Association analyses were restricted to a subset of patients receiving regular care at VUMC (N = 833,185). RESULTS: The phenotyping approach identified 14,496 individuals (0.43%) across the VUMC-EHR with documentation of sexual assault and achieved a positive predictive value of 93.0% (95% confidence interval = 85.6-97.0%), determined by manual patient chart review. Out of 1,703 clinical diagnoses tested across all subgroup analyses, 465 were associated with sexual assault. Sex-by-trauma interaction analysis revealed 55 sex-differential associations and demonstrated increased odds of psychiatric diagnoses in male survivors. DISCUSSION: This case-control study identified associations between disclosures of sexual assault and hundreds of health conditions, many of which demonstrated sex-differential effects. The findings of this study suggest that patients who have experienced sexual assault are at risk for developing wide-ranging medical and psychiatric comorbidities and that male survivors may be particularly vulnerable to developing mental illness.

Clinical informatics↗

PubMed2026

RESCUE: An end-to-end multi-agent LLM system for proactive rare-disease patient screening in the EHR.

BACKGROUND: Rare diseases affect a significant portion of the global population, yet patients often endure a lengthy diagnostic odyssey, frequently missing the opportunity for timely diagnoses with exome or genome sequencing (ES/GS). Existing informatics tools often rely on pre-identified patients or rigid, institution-specific rule sets, failing to address the broader operational question of clinical utility and feasibility. METHODS: We introduce RESCUE (Rare Disease Detection and Escalation Support via a Learning Health System), an end-to-end, multi-agent LLM-powered workflow designed for proactive rare-disease diagnosis across the entire electronic health record (EHR). RESCUE utilizes a team of specialized agents including Ontology, Modeling, Screening, and Review, to automate the screening process to identify candidates for diagnostic testing based on their clinical features. The Ontology Agent classifies clinical data into a four-tier genetic-evidence taxonomy; the Modeling Agent builds a positive-unlabeled (PU) XGBoost classifier to identify potential cases; the Screening Agent applies these models across the EHR population; and the Review Agent evaluates candidates by sampling clinical notes to ensure medical necessity and operational feasibility for genomic testing. RESULTS: Using electronic medical record data from a pediatric hospital, our retrospective evaluation on a holdout set (n=12,591) demonstrates strong discrimination between patients who received diagnostic genomic testing and those who did not (AUC 0.808). Of nearly 500,000 patients in the institutional base, 175,842 met inclusion criteria for screening; among these, RESCUE-flagged candidates were 7.4-fold more likely to receive subsequent genomic assessments compared to controls. Blinded manual chart reviews confirmed that RESCUE identifies previously missed, medically appropriate patients for ES/GS with 80% precision, while simultaneously accounting for prior testing history. CONCLUSIONS: By decoupling expert roles into modular agents, RESCUE offers a flexible, scalable, and adaptable framework for screening patients for rare-disease diagnostic genomic testing. This approach overcomes the limitations of traditional rule-based methods and provides a reproducible, agentic pathway to reduce diagnostic delays and improve patient care at an institutional scale.

Journal Article↗