Latest AI and machine learning research in hospitalists for healthcare professionals.
Cardiogenic shock (CS) is a severe and frequent complication of acute myocardial infarction (AMI), necessitating rapid and accurate prognosis as-sessment to guide treatment and intensive care unit (ICU) resource allocation. We developed two machine learning models to predict 30-day outcomes following CS in AMI: an Admission model (using only data available at admission, like demography, comorbidit...
Missed opportunities for diagnosis (MODs), sometimes termed diagnostic errors, are a major cause of patient morbidity and mortality in the emergency department (ED). EDs have employed eTriggers, rule-based case collections likely to have a higher than average error rate (e.g. 72 hour returns with admission), but their utility is limited by low error yields. Large language models (LLMs) offer new o...
Emergency Department (ED) overcrowding, often exacerbated by prolonged patient length of stay (LOS), is a global challenge. Patients presenting with s...
Non-contrast CT head scans (NCCTH) are the most frequently requested cross-sectional imaging in the Emergency Department. While AI tools have been dev...
Large language models (LLMs) have been investigated for clinical documentation, with concerns about hallucinations and factual errors. Clinician revie...
Frailty and multimorbidity are common in older adults and contribute substantially to prolonged hospitalizations, readmissions, and mortality. Yet, ex...
Manual inpatient screening for substance misuse is labor-intensive and inconsistently applied. Evaluation of artificial intelligence (AI)–assisted scr...
In active vision, the brain receives and encodes discontinuous streams of visual information gated by saccadic eye movements. Saccadic modulation of n...
Timely intensive rehabilitation is crucial to contrast the negative escalation of events that follow a stroke injury, to promote tissue regeneration, ...
Quantification of ischemic brain tissue on non-contrast CT (NCCT) in acute ischemic stroke is challenging in the acute setting. To compare the spatial...
Recent advances in deep learning have enabled the integration of heterogeneous data modalities for clinical prediction, allowing models to exploit com...
MAX-EVAL-11 is constructed by converting MIMIC-III discharge summaries from ICD-9 to ICD-11 codes through systematic mapping, creating a synthetic dia...
Objective: The Phoenix sepsis criteria define sepsis in children with suspected or confirmed infection who have ≥2 in the Phoenix Sepsis Score. The ad...
BACKGROUND: Acute pancreatitis (AP) is a prevalent pathological condition of abdomen characterized by sudden onset, high incidence and complex progres...
In the context of Chinese clinical texts, this paper aims to propose a deep learning algorithm based on Bidirectional Encoder Representation from Tra...
Although advances in brain surgery techniques have led to fewer postoperative complications requiring Intensive Care Unit (ICU) monitoring, the rout...
Hospital readmission prediction is critical for clinical decision support, aiming to identify patients at risk of returning within 30 days post-disc...
Sepsis is a severe condition that causes the body to respond incorrectly to an infection. This reaction can subsequently cause organ failure, a majo...
Student dropout is a significant concern for educational institutions due to its social and economic impact, driving the need for risk prediction sy...
The decomposition of high-density surface electromyography (HD-sEMG) signals into motor unit discharge patterns has become a powerful tool for inves...