Critical Care

A multi-level attention CNN-transformer based framework for the detection of brain tumor using regional dual-score explainability.

Deep learning models for brain tumor diagnosis often lack interpretability beyond qualitative visual heatmaps. Clinicians require not only tumor localization but also quantitative assessment of explanation quality and diagnostic relevance, capabilities absent in conventional explainability methods. This paper introduces a classification-explainability framework addressing these limitations. The Mu...

Multimodal data integration to determine viral and innate immune kinetics in human airway epithelium.

Understanding the mechanisms that govern viral spread in human airway epithelium (HAE) remains a major challenge, particularly with regard to identifying and quantifying key factors such as cell-type-specific infectivity, viral transmission paths, and the innate immune dynamics. Although mathematical models and experimental advances have provided valuable insights into respiratory infections, reve...

RADIANT: A fully configurable radiotherapy dose prediction framework.

Treatment planning is a multi-disciplinary effort that requires medical decision-making, specialized training, and access to specialized software. Rec...

Multicenter machine learning model for assessing the impact of malignancy on in-hospital mortality in heart failure patients: a clinical decision support system with interpretable artificial intelligence.

Heart failure (HF) and malignancy represent two major global health burdens that frequently coexist and lead to poor clinical outcomes. However, the s...

National trends and comparative outcomes of insulin versus non-insulin therapy in acute ischemic stroke patients treated with mechanical thrombectomy: a retrospective cohort study using the national inpatient sample.

Background diabetes mellitus is prevalent among patients with acute ischemic stroke (AIS). The prognostic significance of long-term insulin treatment ...

Application of an AI-Based Pediatric Early Warning Score in the Pediatric Emergency Department: Cross-Sectional Study.

BACKGROUND: Pediatric emergency departments see a high volume of patients. Given that children often cannot describe their condition and there is a sh...

Evaluation of a deep learning model applied to chest X-rays of patients with suspected pneumonia presenting to the emergency department designed to predict admission risk: a retrospective training and prospective non-intervention validation.

OBJECTIVES: To develop, train and test a deep learning model Image-based PROgnostication applied to Chest X Rays (IPRO-X) tool that predicts the inpat...

A real-time early warning system to anticipate respiratory disease outbreaks using transfer learning.

Respiratory disease outbreaks burden American healthcare systems with over one million hospitalizations annually, yet current surveillance systems lag...

Use of machine learning and voice for multiclass classification of Parkinson's disease, chronic obstructive pulmonary disease, and healthy controls.

Parkinson's disease (PD) and chronic obstructive pulmonary disease (COPD) are prevalent conditions with substantial impact on quality of life and heal...

Real-time prediction of cardiorespiratory deterioration during paediatric critical care transport using interpretable machine learning.

Interhospital transport of critically ill children carries inherent risks, including unexpected respiratory and cardiovascular deterioration. Early wa...

Artificial Intelligence for Molecular Subtyping in Unresectable Gallbladder Cancer: A Proof-of-Concept Study for CT-based HER2 Status Prediction.

BACKGROUND/AIMS: Human epidermal growth factor receptor 2 (HER2) overexpression is a critical therapeutic target in gallbladder cancer (GBC), but dete...

Clinical sentiment analysis of ICU notes: a dataset and a comparison of lexicon and clinical language model methods.

BACKGROUND: General-domain sentiment models have been found ineffective in distinguishing positivity and negativity in Intensive Care Unit (ICU) clini...

Multi-source data integration through pooling and transfer learning improves generalizability and specialization of deep learning models for ICU mortality and length of stay prediction: a four-database external validation study.

BACKGROUND: Most Artificial Intelligence prognostic models in intensive care are trained and validated on a single source, limiting their reliability ...

Evaluating expert opinions on using a virtual patient app based on artificial intelligence for teaching brief smoking cessation advice to medical students: a cross-sectional study.

BACKGROUND: Smoking is one of the main preventable causes of premature disease and death. Anti-smoking counseling continues to be a challenge to cover...

Air pollution-related health impacts from domestic waste burning and associated interventions: the merits of a traditional versus machine learning scoping review methodology.

Regularly updating repositories of air pollution impacts on health is essential for evidence-based policies, interventions, and progress monitoring. H...

Prediction of venous thromboembolism after spontaneous intracerebral hemorrhage based on machine learning.

Patients with intracerebral hemorrhage (ICH) are at high risk of venous thromboembolism (VTE). Current risk assessment tools are limited and not tailo...

Global whole-genome, phylodynamic, and machine-learning analysis of Glaesserella parasuis serovars 2, 5, and 12.

Glaesserella parasuis is a respiratory pathogen of swine and the causative agent of Glässer's disease. Among the 15 serotypes, serotypes 2, 5, and 12 ...

Open circuit fault localization in dual active bridge based simultaneous battery charging systems using multi label classification.

This paper presents a multi-label fault localization framework for open-circuit fault diagnosis in a three-port Dual Active Bridge (DAB) converter usi...

HybridTrust: on-device federated learning with crypto-agile security for legacy and quantum-safe medical devices.

The process of migration of IoMT systems in healthcare into post-quantum cryptographic systems is expected to be a gradual one. Here, existing ECC-bas...

Supervised machine learning models for predicting sepsis-associated acute kidney injury in children: a real-world evaluation.

BACKGROUND: Sepsis-associated acute kidney injury (S-AKI) substantially increases mortality. The recent Phoenix criteria have redefined pediatric seps...