Hospital-Based Medicine

Large language model discharge summary preparation using real-world electronic medical record data shows promise.

The efficacy of large language models (LLMs) in discharge summary preparation using real clinical documentation remains novel. Our study aimed to test the efficacy of two LLMs to generate DC summaries which were scored using a validated discharge summary scoring metric. The models performed nearly identically, with the llama3:instruct model having a mean score of 19.1/31 (SD: 2.42) compared to 19....

Diagnosis and management of Candida endophthalmitis and chorioretinitis.

BACKGROUND: Candida endophthalmitis (CE) and chorioretinitis are uncommon but potentially devastating complications of candidemia, associated with significant risks of vision loss and long-term morbidity. Effective management relies on timely diagnosis and targeted antifungal therapy. However, diagnostic and therapeutic approaches are hindered by inconsistent guidelines, limited evidence, and vari...

Functional connectome-based predictive modeling of suicidal ideation.

Suicide represents an egregious threat to society despite major advancements in medicine, in part due to limited knowledge of the biological mechanism...

Future Perspectives of Liver Research in the Asia-Pacific Region: Focus on Hepatitis B and C.

The Asia-Pacific region faces serious liver health challenges, primarily because of the comparatively high prevalence of hepatitis B virus (HBV) and h...

Deep learning on brief interictal intracranial recordings can accurately characterize seizure onset zones.

OBJECTIVE: Epilepsy is a debilitating disorder affecting more than 50 million people worldwide, and one third of patients continue to have seizures de...

Sensors Innovations for Smart Lithium-Based Batteries: Advancements, Opportunities, and Potential Challenges.

Lithium-based batteries (LiBs) are integral components in operating electric vehicles to renewable energy systems and portable electronic devices, tha...

Assessing climate and land use impacts on surface water yield using remote sensing and machine learning.

Climate and land use changes are critical factors affecting watershed water yields, with significant implications for water resources at both local an...

Remote monitoring in heart failure: artificial intelligence and the use of remote speech analysis to detect worsening heart failure events.

Globally, heart failure (HF) is a leading cause of hospitalization and mortality, primarily among the elderly, and is estimated to affect more than 64...

Electronic Health (eHealth) and Artificial Intelligence-based Tools to Optimize In-hospital Patient Flow: A Scoping Review.

OBJECTIVES: Congested hospitals are increasingly common. Electronic health (eHealth) and artificial intelligence (AI)-based tools may improve in-hospi...

Automated biodiversity research critically requires multidisciplinary expertise.

Data-driven computational methods in taxonomy, ecology, and conservation biology are revolutionising these disciplines, from data collection to analys...

Continuous glucose monitoring combined with artificial intelligence: redefining the pathway for prediabetes management.

Prediabetes represents an early stage of glucose metabolism disorder with significant public health implications. Although traditional lifestyle inter...

Predicting Mortality in Atrial Fibrillation Patients Treated with Direct Oral Anticoagulants: A Machine Learning Study Based on the MIMIC-IV Database.

Atrial fibrillation (AF) is a common arrhythmia linked to increased mortality and significant healthcare burden, especially in the elderly. Direct Or...

AI-Based Predictive Models for Cardiogenic Shock in STEMI: Real-World Data for Early Risk Assessment and Prognostic Insights.

Cardiogenic shock (CS) is a life-threatening complication of ST-elevation myocardial infarction (STEMI) and remains the leading cause of in-hospital ...

The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy.

Motor activity alterations are key symptoms of psychiatric disorders like schizophrenia. Actigraphy, a non-invasive monitoring method, shows promise i...

Predictive machine learning model for 30-day hospital readmissions in a tertiary healthcare setting.

MOTIVATION: Hospital readmissions represent a major challenge for healthcare systems due to their impact on patient outcomes and associated costs. As ...

Nucleation and Growth Mechanisms of Micro/Nano Structural Manganese-Trimesic Acid Coordinations for Aqueous Zinc-Ion Batteries.

Nucleation and growth of metal-organic frameworks (MOFs) are critical for controlling their morphology, size, and performance. Guided by the crystal n...

Reply to: Optimizing external validation of deep neural networks for interictal discharge detection.

Optimizing external validation of deep neural networks for interictal discharge detection.

Association between fibrinogen levels and prognosis in critically bleeding patients: exploration of the optimal therapeutic threshold.

BACKGROUND: Severe bleeding is a leading cause of ICU admission and mortality. Fibrinogen plays a crucial role in prognosis, yet optimal thresholds an...

Prediction and optimization of hardness in AlSi10Mg alloy produced by laser powder bed fusion using statistical and machine learning approaches.

The primary objective of this study is to evaluate the influence of critical process parameters on the hardness of AlSi10Mg alloy fabricated via the L...