Latest AI and machine learning research in hospital-based medicine for healthcare professionals.
Automated detection of interictal epileptiform discharges in scalp electroencephalography (EEG) is clinically important, but recent high-performing deep-learning models often trade interpretability for accuracy. We introduce EEG-SpikeAgent, a closed-loop program-synthesis framework that uses a large language model (LLM) agentic system to generate signal-processing features for spike detection in s...
Plasma disruption is a critical threat to tokamak safety. Existing data-driven predictors mainly rely on time-series diagnostic signals, while visible images provide complementary spatial cues including plasma deformation, local brightening, and radiation-structure evolution. Although the image modality improves the model's discriminative capability, it also substantially increases the computation...
BackgroundHealth care organizations are increasingly required to make strategic decisions about artificial intelligence (AI) systems before their clin...
Recent image generation and editing models can produce visually appealing natural images, yet they remain unreliable when the target image is a knowle...
Medical practice is bottlenecked by the slow production of high-quality clinical evidence. Despite progress in automating selected stages, autonomous ...
Background: To develop and validate multiple Machine Learning (ML) algorithms that predict Mechanical Ventilation (MV) requirement in Guillain-Barre S...
Timely intensive care dictates survival, yet emergency infrastructure remains unevenly distributed across Sri Lanka. While pre-hospital services have ...
Cardiac discharge phenotyping informs post-discharge treatment and follow-up, but real-world records are often incomplete and class-imbalanced, increa...
Objective: Electronic health record (EHR) audit logs capture clinician-EHR interaction patterns, but most audit log research relies on aggregated meas...
Background: Large language models (LLMs) offer promise for systematic review data extraction, but performance in complex multidisciplinary domains and...
Most published clinical-AI results are single models on a single dataset, difficult to reproduce, and rarely validated outside their training hospital...
Despite contributing substantially to clinician burnout, nursing documentation lacks empirical evidence distinguishing clinically essential from admin...
BackgroundVentilator-associated pneumonia (VAP) is the most frequent nosocomial infection in critical care, affecting 20-36% of mechanically ventilate...
Importance: Machine-learning models for ischemic stroke risk prediction are rarely validated across ancestrally distinct cohorts, and the contribution...
Given the widespread prevalence of depression and its consequential impact on individuals and society, it is crucial to obtain objective measures for ...
**Background:** Acute pancreatitis (AP) is a common gastrointestinal emergency with a subset of patients progressing to severe acute pancreatitis (SAP...
Background: Manual review of 30-day hospital readmissions can identify actionable quality and safety problems, but it is labor-intensive. We developed...
Background: Guiding risk-appropriate inpatient thromboprophylaxis requires venous thromboembolism (VTE) risk stratification; however, reliable risk de...
Federated learning lets institutions train shared models without moving their data, which makes it a natural fit for health and life sciences research...
Evidence-based medicine demands clinical answers that are not only fluent and medically plausible, but also anchored in traceable evidence, tailored t...