Hospital-Based Medicine

Predicting Outcomes of Traumatic Brain Injury Using Machine Learning Models Among Patients at Kilimanjaro Christian Medical Centre, Tanzania: A Registry-Based Cohort Study.

BACKGROUND: Traumatic brain injury (TBI) remains a major global health burden, disproportionately affecting low- and middle-income countries (LMICs) where access to neurocritical care is limited. Accurate and context-appropriate prognostic models are crucial to guide early clinical decision-making and optimize resource allocation in such settings. This study aims to develop and evaluate machine le...

Global, multilingual, physician-invited, patient-completed digital registry enabling pre-consultation engagement and supporting more efficient care pathways in venous disease.

Lower limb venous diseases are prevalent chronic conditions that require standardized, efficient, and patient-centered clinical assessment across diverse healthcare settings. Despite increasing digitalization of healthcare, scalable multilingual digital infrastructures capable of supporting standardized pre-consultation data collection and physician-validated real-world evidence generation remain ...

Physics-informed hybrid GR4J-XGBoost model for streamflow prediction: integrating conceptual states, SHAP interpretability, and uncertainty analysis.

Accurate streamflow prediction remains challenging due to the nonlinear and dynamic nature of rainfall-runoff processes. Conceptual hydrological model...

Smartphone-Based Physical Performance and Multidimensional Determinants of Self-Reported Knee Pain in Community-Dwelling Older Adults: Cross-Sectional Machine Learning and Network Analysis Study.

BACKGROUND: Knee pain affects 22.9% of individuals aged 40 years and over globally and is associated with body function, activity, environmental, and ...

The Influence of Patient Persona and Affective Framing on Management Recommendations of ChatGPT, Gemini and Claude for Unruptured Intracranial Aneurysms: A Comparative Benchmarking Study.

Patients increasingly consult large language models (LLMs) before specialist review and may do so in widely differing emotional registers. We examined...

Clinical and Echocardiographic Factors Associated With AI-Estimated Atrial Fibrillation Likelihood During Sinus Rhythm in Patients With Documented Paroxysmal Atrial Fibrillation.

BACKGROUND: Artificial intelligence-enabled electrocardiography (AI-ECG) has emerged as a promising tool for identifying patients with atrial fibrilla...

Optimizing Cancer Drug Treatments Using Big Data Integration of Genomic and Clinical Data for Personalized Medicine.

Personalized cancer care depends on the seamless integration of genetic profiles, medical histories, and continuous patient monitoring to optimize the...

Delay and Attrition in Severe Aortic Stenosis: A Four-Checkpoint Systematic Review of Pathway Bottlenecks and Interventions.

The care pathway for severe aortic stenosis (AS) remains vulnerable to diagnostic delay, referral inertia, undertreatment, and procedural waiting time...

Prediction of coronary atherosclerosis progression in type 2 diabetes mellitus based on AI-derived CCTA parameters and clinical factors: a follow-up study.

OBJECTIVE: To develop a predictive model for coronary atherosclerosis progression in patients with type 2 diabetes mellitus (T2DM) based on Artificial...

HBV-Specific T Cell Responses for Short-Term Hepatitis Progression Prediction: A Comparative, Interpretable Machine Learning Study.

BACKGROUND & AIMS: As central determinants of viral control and immunopathology, hepatitis B virus (HBV)-specific T-cell responses provide more clinic...

Development and Validation of an XGBoost-SHAP Model for Predicting Adverse Outcomes in Elderly Cardiovascular Patients With Polypharmacy: A Retrospective Cohort Study.

This study aims to develop and validate an interpretable machine learning model using Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlan...

Predicting Next-Day Passive Suicidal Ideation in At-Risk Youth.

INTRODUCTION: Passive suicidal ideation (SI) is a well-established risk factor for suicidal behavior but has received less attention than active SI. A...

A Data-Driven Medication Regimen Complexity Score for Critically Ill Patients: MRC-ICU 2.0.

BACKGROUND: The 2019 medication regimen complexity-intensive care unit (MRC-ICU) score is associated with patient outcomes, ICU complications, and cri...

The Sydney Triage to Admission Risk Tool With Artificial Intelligence (START-AI) to Support Decision Making in Emergency Departments: Model Explainability and Feature Importance Analysis.

OBJECTIVE: Evaluate the importance of specific variables contributing to a recently reported Artificial Intelligence (AI) prediction model called Sydn...

Development and Validation of an Interpretable Machine Learning-Based Clinical Prediction Model for Short-Term Mortality in Intracerebral Hemorrhage With Thrombocytopenia: A Multicenter Study.

BACKGROUND: Intracerebral hemorrhage (ICH) with thrombocytopenia is associated with poor outcomes, but early risk prediction tools for this subgroup a...

Predicting premature treatment termination in inpatient psychotherapy: A machine learning approach.

OBJECTIVE: The study aimed to quantify types of premature treatment termination in a psychosomatic hospital and to investigate if patient characterist...

User perceptions of machine learning models as decision support for colorectal cancer multidisciplinary team conferences (AID-SIM-2): a qualitative simulation study.

BACKGROUND: Multidisciplinary team (MDT) conferences are considered a cornerstone of decision-making in cancer diagnostics and care. However, the curr...

Advances in peptide applications for hard tissue repair and regeneration: multidisciplinary perspectives.

The rising prevalence of bone and dental diseases, compounded by an ageing population, underscores the urgent need for advanced materials in hard tiss...

Standard-of-care vs. machine learning-recommended discharge destinations for geriatric surgical inpatients: algorithm development and internal validation.

PURPOSE: Older adults undergoing surgery are at high risk for adverse outcomes due to age-related vulnerabilities. Comprehensive geriatric assessment ...

Artificial intelligence in medicine: a clinician-oriented guide and operational framework to evaluate new AI devices.

BACKGROUND: Artificial Intelligence (AI) is rapidly transitioning from experimental research to daily medical practice, yet the medical community's un...