Hospital-Based Medicine

'Healthy menopause': The European Menopause and Andropause Society (EMAS) 2026 framework for precision, digital, and interdisciplinary midlife health.

INTRODUCTION: On average, women now spend more than one-third of their lives in the post-reproductive period, yet menopause care across Europe remains fragmented and uneven. Since the first publication of its model of care by the European Menopause and Andropause Society (EMAS) in 2016, major advances in precision medicine, digital phenotyping, biomarkers derived from data collected by wearable de...

Multiscale Spatial Fusion Feature-Driven Characterization of Gastric Cancer Invasive Margins: A Multicenter Cohort Study for Preoperative Accurate Differentiation Between T4a and T4b Subtypes.

Accurate preoperative differentiation of gastric cancer T4a/b stages is crucial for surgical planning and prognosis. However, conventional CT assessments often yield suboptimal staging accuracy due to visual limitations and inadequate peritumoral microinvasion quantification. This study developed a multi-scale spatial feature fusion model based on extended regions of interest (eROI) for precise pr...

Video-Algorithmic Patient Monitoring in Mental Health Inpatient Settings: Qualitative Study of Patient or Consumer, Clinician, and Vendor Perspectives.

BACKGROUND: Video-algorithmic patient monitoring (VAPM) combines remote, noncontact sensors and algorithmic analysis and is increasingly trialed in ac...

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of mana...

Generating Patient Documents from Electronic Health Records Using Generative Artificial Intelligence: A Feasibility Study in a Japanese Cancer Center.

OBJECTIVE: Clinical documentation consumes substantial clinician time, potentially detracting from patient care. Generative artificial intelligence (A...

Artificial intelligence for pediatric rare disease diagnosis: a multimethod study integrating published evidence and clinician interviews.

BACKGROUND: Pediatric rare diseases are highly heterogeneous and are frequently associated with missed or delayed diagnosis, creating substantial burd...

[Overview and conclusions of the STROKE data platform of the National Laboratory for Translational Neuroscience and the associated clinical studies].

Acute ischemic and hemorrhagic stroke are among the leading causes of mortality and long-term disability worldwide. In addition to the results of rand...

ChatGPT vs. multidisciplinary team decisions in auditory implantation: a concordance analysis of implant type and side selection.

INTRODUCTION: Auditory implantation is a standard rehabilitation strategy for patients with severe hearing loss, and candidacy determination requires ...

Heart Failure sub-phenotyping and in-hospital and 28-day mortality prediction based on mean arterial pressure trajectory modeling.

BACKGROUND: Acute decompensated heart failure patients follow a broad range of clinical pathways during hospitalization. Efficient patient sub-phenoty...

MAGMED: A clinically grounded LLM-guided synthetic augmentation framework for admission-time ICU mortality prediction.

BACKGROUND: Mortality risk prediction for elderly intensive care unit (ICU) patients with severe infections remains challenging due to limited sample ...

SSA(BBO)-optimized neural networks for remaining useful life estimation and health monitoring of lithium-ion batteries.

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is crucial for advancing battery health management, enhancing safety, ...

Stage-specific predictors of exercise adherence after percutaneous coronary intervention: a prospective multicenter study.

BACKGROUND: The efficacy of exercise after Percutaneous Coronary Intervention (PCI) is compromised by poor long-term adherence. Although multiple infl...

Construction and validation of a machine learning-based risk prediction model for venous thromboembolism in older adult patients: A multifactorial analysis of 28,231 cases.

Venous thromboembolism (VTE) is a leading preventable cause of in-hospital mortality in older adults, yet early risk stratification remains a key clin...

Predicting severe COVID-19 in elderly patients using routine laboratory indicators: Diagnostic accuracy of machine learning models.

To enable early warning of severe 2019 coronavirus disease in elderly patients, this study collected routine laboratory indicators and clinical parame...

The Science of Preventing Delirium: Trends, Knowledge Gaps, and Future Directions Revealed Through Bibliometric Insights.

PURPOSE: To examine global research trends in delirium prevention through a bibliometric analysis and provide a structured overview of its development...

Computer-Vision Approach to Triaging Patient-Submitted Photos of Intestinal Ostomies.

BACKGROUND: Surgeons and ostomy nurses receive a high volume of stoma photos from patients. OBJECTIVE: This study aimed to develop and validate an aut...

Machine-learning-guided control of a pulsed cold atmospheric plasma jet reveals a voltage-dependent discharge transition relevant to diabetic wound healing.

Cold atmospheric plasma (CAP) technology is promising for biomedical treatment, but its translation is limited by the difficulty of identifying operat...

First-episode psychosis and mood disorder onset in individuals with epilepsy.

BACKGROUND: Psychiatric disorders represent a major burden for patients with epilepsy (PwE). This study examined how demographic, epilepsy-related, an...

Deployment process for artificial intelligence applications in radiology practice.

Successful deployment of medical artificial intelligence (AI) systems should start with formulating clear goals and understanding organisational workf...

Development and external validation of AI-ECG models in athlete pre-participation screening: Performance, limitations, and clinical implications.

BACKGROUND: Pre-participation screening (PPS) in competitive athletes aims to identify cardiovascular diseases associated with sudden cardiac death (S...