Hospitalists

Synthetic data-augmented machine learning for 30-day readmission prediction in patients with chronic conditions: a retrospective real-world study.

OBJECTIVES: To develop and evaluate an explainable machine learning framework enhanced with synthetic data generation to predict unplanned 30-day hospital readmissions among patients with chronic obstructive pulmonary disease (COPD), heart failure (HF) and type 2 diabetes mellitus (T2DM), and to identify key clinical and social predictors of readmission. DESIGN: A retrospective cohort study using ...

Predicting one-year mortality risk in ICU patients with ischemic stroke using multi-algorithm machine learning and a nomogram.

Introduction: Ischemic stroke is a leading cause of mortality, and patients requiring intensive care unit (ICU) admission carry a guarded prognosis. We aimed to develop and validate a predictive model for estimating one-year mortality risk in ICU-admitted ischemic stroke patients.MethodsIn this retrospective cohort study, data from 1974 ischemic stroke patients were extracted from the MIMIC-IV dat...

Parsimonious and explainable biomarker-based severity score for hospitalised patients with COVID-19-related respiratory infections: development, validation and XAI benchmarking.

BACKGROUND: Severity scoring systems are increasingly important tools for stratifying hospitalised patients, guiding treatment decisions, and enabling...

Sustainable methylene blue dye removal via bio-derived micro/micron-sized porous particles Zygophyllum coccineum and Calotropis procera: A machine learning-assisted study.

UNLABELLED: The discharge of synthetic dyes into aquatic ecosystems poses a critical threat to environmental and human health, necessitating urgent, c...

Ardent Health: An AI-Enabled Virtual Care Model, from Pilot to Production.

Healthcare leaders face sustained uncertainty: workforce volatility, financial pressure, and accelerating technology change. In 2024-2025, Ardent Heal...

Assessment of the risk of bacteremia in patients with hematologic malignancies in the emergency department: A comparative study between logistic regression and machine learning algorithms.

OBJECTIVE: To stratify the risk of bacteremia at the time of emergency department (ED) admission in patients with hematologic malignancies. To this en...

Dynamic machine learning prediction of persistent AKI after cardiac surgery with modifiable perioperative risk factors.

Early identification and prevention of persistent acute kidney injury (pAKI) remain challenging due to delayed biochemical markers and limited tools t...

Deep learning-based non-contrast CT imaging markers enhance post-transfer DWI core volume prediction.

BACKGROUND: Deep learning enables the extraction of ischemic lesion size and hypodensity imaging markers from noncontrast CT (DLNCCT) in patients with...

Machine learning approach for heavy metal source identification and spatial distribution in coastal sediments of Tiruchendur, Southern India.

This research focuses on the global issue of heavy metal (HM) contamination in coastal systems and provides the first baseline investigation of the su...

Combined use of serum KL-6 and respiratory functional parameters for identifying fibrotic lung damage in SARS-CoV-2-induced interstitial pneumonia.

BACKGROUND: Post-COVID19 pulmonary fibrosis (PCPF) has been reported in a significant proportion of patients who survive the acute SARS-CoV-2 infectio...

Photoelectrocatalytic Chemical Pathways in Photoassisted Lithium-Sulfur Batteries via a Multiscale Graph-Based Machine Learning Framework.

The rapid advancement of photorechargeable batteries is driven by the need for efficient solar energy utilization, with photoassisted lithium-sulfur b...

Biomarkers associated with future suicide risk enhance predictive performance in psychiatric inpatients.

OBJECTIVES: Suicide risk assessments currently rely on subjective clinical judgement, lacking objective measures. This study aimed to evaluate the ass...

Impact of an artificial intelligence-driven triage system on workflow and transfer efficiency: stratified analysis of 4548 admissions to four thrombectomy hubs receiving transfers from sixty spokes.

BACKGROUND: We aimed to evaluate the impact of implementing an artificial intelligence (AI)-enabled acute ischaemic stroke triage system on workflow e...

Pediatric Firearm Risk Prediction in Trauma Centers and After Discharge: A Machine Learning Analysis.

OBJECTIVE: We used machine learning (ML) to develop firearm risk prediction models for injured children and adolescents admitted to U.S. trauma center...

Preliminary evaluation of gunshot residue pattern analysis using flash-pulse infrared thermography and multi-task deep learning.

Gunshot residue pattern assessment can support forensic reconstruction, particularly for shooting distance evaluation, but commonly used approaches ma...

Letter to the Editor: "Evaluating the efficacy of large language models in predicting intensive care unit admission needs".

This letter comments on the study by Turan et al., which evaluates the efficacy of large language models (LLMs) in predicting ICU admission needs. Whi...

Optimizing Hospital Discharge Planning: Empirical Insights and Requirements of AI-Based Technologies From an Explorative Mixed Methods Field Study.

BACKGROUND: Discharge planning (DP) is crucial for care continuity after a hospital stay but remains complex due to organizational constraints, interp...

Machine learning-based prediction of surgical timing and discharge in infantile hypertrophic pyloric stenosis a multimodal predictive approach.

Machine learning-based models using preoperative imaging and clinical data to predict intraoperative rebleeding and functional recovery in intracerebral hemorrhage patients: a multicenter study.

BACKGROUND: Spontaneous intracerebral hemorrhage (ICH) is associated with high mortality and disability. This study aimed to develop and validate mach...

Population-Level Digital Stroke Surveillance: Building a Fair and Accurate ICD-10 Detection Model.

Background The International Classification of Diseases, 10th Revision (ICD-10), is widely used for clinical care, quality assurance, and stroke resea...