Hospitalists

Predictability of Same-Day Discharge Candidacy After Bariatric Surgery with Machine Learning.

INTRODUCTION: Despite growing interest, same-day discharge (SDD) after bariatric surgery remains uncommon due to challenges with patient selection. In this study, we seek to evaluate whether Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) database provides sufficient information for predicting SDD candidacy METHODS: In this retrospective cohort study, we ide...

Does the Timing of USMLE Step 1 Affect Performance? A Comparison of Pre- and Post-Clerkship Timing in the Pass/Fail Era.

In 2022, Step 1 of the United States Medical Licensing Examination transitioned to pass/fail scoring, removing a major performance-oriented incentive that historically shaped how and why students prepared for the exam. While Step 1 is typically taken before clerkships, some medical schools have shifted the exam to after core clerkships, citing potential benefits for learning and integration. This ...

Early emergency department decision support for heart failure hospitalization using triage-level unstructured and structured data: a retrospective cohort study.

BACKGROUND: Early identification of patients at risk for heart failure (HF) hospitalization in the emergency department (ED) is challenging because de...

Evaluation of an AI-Driven Risk Stratification System for Clinical Deterioration in Geriatric Hip Fracture Patients.

OBJECTIVES: To determine whether an artificial-intelligence-driven Clinical Deterioration Index (CDI) could identify geriatric hip-fracture patients a...

Terabyte scale dataset for partial discharge detection in covered conductors via contact galvanic method.

This article introduces a dataset designed for the detection of partial discharges in transmission power lines using covered conductors through a cont...

Selective and Sustainable Recovery of High-Value Metals from e-Waste via a Glucose-Mediated Hydrothermal Process.

The rapid fabrication of artificial intelligence-based hardware worldwide is intensifying the electronic waste (e-waste) crisis, threatening ecosystem...

Chemical dosage optimization in kitchen wastewater treatment via an integrated bio-chemical model.

To address the challenges of achieving organic compliance in kitchen wastewater treatment and the high cost of chemical dosing, this study established...

Machine Learning Prediction of Hospital Stay in Pediatric Typhoid Intestinal Perforation: Pilot Study.

INTRODUCTION: Typhoid intestinal perforation (TIP) remains a significant cause of pediatric morbidity in resource-limited settings, with prolonged hos...

Deep Learning Reconstruction Versus Hybrid Iterative Reconstruction for Acute Cerebral Infarction Detection on 135 kVp Non-Contrast Brain CT.

PURPOSE: Deep learning reconstruction (DLR) is useful to reduce image noise and improve contrast resolution compared with hybrid iterative reconstruct...

Optimizing temporal windows for wearable-augmented post-discharge risk prediction: a methods study.

OBJECTIVE: Traditional readmission risk models relying on static discharge data have limited predictive performance and fail to capture patients' reco...

Rehospitalization in Preterm Infants: Machine Learning Prediction Model & Associated Risk Factors.

BACKGROUND AND OBJECTIVES: Preterm birth is a major global health concern, with preterm infants (PI) at increased risk of morbidity and rehospitalizat...

Genetic algorithm-based daily power output forecasting for energy storage power stations.

Accurate day-ahead power generation forecasting is crucial for improving the operational efficiency of energy storage power stations and enhancing the...

Development and External Validation of a Deep Learning Model to Predict Mortality in Aneurysmal Subarachnoid Hemorrhage Using Admission Computed Tomography.

BACKGROUND AND OBJECTIVES: Traditional prognostication after aneurysmal subarachnoid hemorrhage depends on subjective clinical grading and radiologica...

Can machine learning support infection control measures by predicting carbapenemase-producing Enterobacterales colonization at admission?

BACKGROUND: Early identification of patients with carbapenemase-producing Enterobacterales (CPE) colonization is crucial for infection control; howeve...

Classifying American Society of Anesthesiologists Physical Status With a Low-Rank-Adapted Large Language Model: Development and Validation Study.

BACKGROUND: The American Society of Anesthesiologists Physical Status (ASA-PS) classification is integral to preoperative risk assessment; yet, assign...

Detection of calcified plaques: comparison between coronary CT angiography and thin-slice non-contrast CT with deep learning-aided image registration.

OBJECTIVES: To investigate whether coronary CT angiography (CCTA) misses calcified plaques detected by thin-slice non-contrast CT (NCCT). MATERIALS AN...

The future of follow-up: technology-enabled medical and developmental follow-up care for NICU graduates.

Premature infants are at higher risk of long-term clinical and developmental sequelae compared to their term counterparts. Specialized, multidisciplin...

Interpretable machine learning for river salinity dynamics in arid basins.

Managing salinity in arid rivers is impeded by sparse monitoring, relying on low-frequency grab samples that miss hydrological event dynamics. Here, i...

Power transformer online monitoring and state assessment method based on 2DCNN-BiLSTM multi-source feature fusion.

As key power system equipment, real-time monitoring and accurate assessment of power transformers' operating status are critical for grid safety. To a...

Non-European migrants with schizophrenia spectrum disorders in Swiss forensic and general psychiatric care facilities - A comparative study using machine learning.

Personal history of migration poses an important risk factor for schizophrenia spectrum disorders (SSD), which are also associated with a higher rate ...