Hospital-Based Medicine

A PCA-enhanced XGBoost framework for combined SOC and SOH estimation in lithium-ion batteries.

Accurate estimation of State of Charge (SOC) and State of Health (SOH) is critical for safe and reliable operation of lithium-ion batteries under temperature variations and long-term aging. Most existing approaches estimate SOC and SOH independently using separate models validated under limited thermal conditions, restricting practical applicability. This paper argues that battery aging through a ...

Single lead electrocardiographic detection of left ventricular systolic dysfunction in pediatric and congenital heart disease.

Portable, scalable, and accessible artificial intelligence (AI)-enabled smartwatch technology shows promise as a cardiovascular risk stratification strategy in the general adult population. The challenges of lifelong care in congenital heart disease (CHD)-inclusive of regional and socioeconomic disparities worldwide-underscore the need for similar solutions tailored to this population. Herein, we ...

Explainable early prediction of short-term severe deterioration in gallstone-related biliary disease using MIMIC-IV, eICU, and local real-world data.

BACKGROUND: Patients with gallstone-related biliary disease may deteriorate rapidly before definitive source control. This study aimed to develop an i...

Machine learning driven modeling of synergistic perinatal risk profiles in early onset pediatric cerebral palsy.

AIM: To develop and evaluate machine learning (ML) models for early cerebral palsy (CP) prediction and identify synergistic perinatal risk factors in ...

Build fair machine learning models to predict adverse outcomes for heart failure patients with preserved ejection fraction and with reduced ejection fraction.

OBJECTIVES: Develop and validate subtype-specific, fairness-aware machine learning (ML) models that integrate clinical and social determinants of heal...

Exploring life's hidden majority: microbial dark matter symposium highlights.

The Microbial Dark Matter Symposium held on August 28-29, 2025, in Laguna Beach, Orange County, CA, convened a multidisciplinary group of scientists t...

[Tele-emergency physicians-a cost-benefit analysis].

Telemedical applications are increasing-ranging from patients contacting a general practitioner to tele-emergency medicine and tele-intensive care for...

Machine learning prediction of local control after Gamma Knife radiosurgery to post-resection cavities from brain metastases: a proof-of-concept study.

BACKGROUND: Large symptomatic brain metastases require initial surgical resection. However, local control (LC) after Gamma Knife radiosurgery (GKRS) t...

Evaluation of an AI Medical Scribe After 236,153 Notes Generated Across Care Levels in a European Health System: Mixed Methods Retrospective Observational Study.

BACKGROUND: Clinicians spend a substantial share of their working hours on documentation, contributing to workflow inefficiencies, reduced patient-fac...

De novo synthesis of large-scale metabolic pathways for natural products and their optimization strategies.

Natural products constitute a vital source for drug discovery, yet extraction from producers is inefficient and chemical synthesis involves complex, l...

Pulmonary nodule prediction in the multi-omics era: integrating radiomics, AI, liquid biopsy, and airway classifiers.

Low-dose CT (LDCT) lung cancer screening significantly reduces mortality but has dramatically increased the detection of pulmonary nodules. Most of th...

Development of a machine learning-based model for predicting adverse pregnancy outcomes in women with polycystic ovary syndrome (PCOS): a retrospective observational study protocol.

INTRODUCTION: Polycystic ovary syndrome (PCOS) is a prevalent endocrine condition in reproductive-aged women, which is associated with adverse materna...

A Multimodal Multitask Deep Learning Model Based on Ultrasound RF Signals for Joint Assessment of Breast Masses and Axillary Lymph Nodes Status.

PURPOSE: Developing a deep learning model to simultaneously evaluate lymph node status and distinguish between benign and malignant breast masses has ...

Simulation of covariate and concept drift in machine learning hospital admission prediction from emergency triage.

Machine learning models that predict hospital admission at triage may support patient flow forecasting, yet the effects of covariate drift, concept dr...

Development of a spatial radiomics nomogram for predicting unfavorable outcomes after acute ischemic stroke: A multicenter study.

OBJECTIVE: To develop and externally validate a spatial radiomics nomogram integrating infarct location, lesion radiomics, and clinical predictors for...

Machine learning-based prediction of 3-6-month post-stroke cognitive impairment using acute-phase clinical data: a two-center retrospective prognostic modeling study.

BACKGROUND: Post-stroke cognitive impairment (PSCI) is common and disabling, but identifying patients at risk early remains difficult. We developed an...

Recovery trajectories across intensive psychiatric settings: a multivariate analysis characterized by baseline profiles.

BACKGROUND: Understanding recovery variability in intensive psychiatric care is crucial, as variable-centered analyses often obscure individual differ...

Multidisciplinary research priorities for artificial intelligence in mental health: a call to action.

The use of artificial intelligence (AI) is anticipated to transform mental health care. However, the rapid research growth in this field has outpaced ...

Development and validation of a disease-syndrome integrated assessment model for AECOPD severity: protocol for a nationwide multicentre cross-sectional study.

INTRODUCTION: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) accelerate disease progression and increase mortality. Accurate se...

Deep learning integration of initial abdominal radiography and early clinical information for predicting hospitalization of patients with abdominal symptoms.

Abdominal symptoms are a common reason for emergency department visits, yet early admission decisions remain challenging due to limited diagnostic inf...