Hospital-Based Medicine

Anticipating health and care trajectories from routinely collected social care records.

Predictive modelling in healthcare has advanced rapidly, yet social care systems, despite their central role in supporting vulnerable populations, remain underexplored in this domain. In this study, we apply machine learning to a large, pseudonymised dataset of social care records from 27,590 adults in Oxfordshire, encompassing around 90% of individuals receiving care in the region. We developed m...

The Forcing Factors That Predict Obesity: A Cross-Sectional Multilevel Machine Learning Model of US County-Level Prevalence.

OBJECTIVE: Variables predicting obesity are not limited to individual-level risk factors. The purpose of this study is to assess multilevel predictors of obesity prevalence. METHODS: US county-level datasets incorporating 34 variables were analyzed cross-sectionally using explainable artificial intelligence (XAI) analytical methods. A Light Gradient Boosting Machine Model was trained to predict ob...

Postgraduate Education in Artificial Intelligence: A Proposal from the Artificial Intelligence Commission of the Portuguese Medical Association.

Artificial intelligence (AI) is entering clinical practice through decision-support systems, predictive tools, generative models, and clinical documen...

Development and validation of a machine learning-based model for predicting fall-related injury risk in hospitalized patients.

BACKGROUND: Falls are among the most common adverse events in hospitalized patients, with about 30% leading to injury. We developed a machine learning...

Machine-based classification of epileptiform activity in selected EEG excerpts from genetic generalised epilepsy.

OBJECTIVES: To evaluate whether the deep learning model IGENet-TS, a time-domain convolutional neural network (CNN), can classify expert-selected EEG ...

Machine learning-based prediction of fall within 6 months of stroke onset: A prospective study.

OBJECTIVE: To develop and evaluate machine learning-based models for predicting fall risk within 6 months of stroke onset. METHODS: This prospective s...

Beyond predictive performance: Interpretability challenges and feature importance bias in XGBoost-based readmission models.

Song et al. report a machine-learning framework based on the eXtreme Gradient Boosting (XGBoost) algorithm for predicting 1-year unplanned readmission...

Multimodal artificial intelligence-based long-term mortality prediction after transcatheter aortic valve implantation: a multicentre development, validation, and testing study.

BACKGROUND: Appropriate risk prediction is essential to inform long-term management in patients with symptomatic severe aortic stenosis after transcat...

Ovarian borderline tumors and minimally invasive surgery: a narrative review.

Borderline ovarian tumors (BOTs) are a distinct subgroup of epithelial ovarian neoplasms that commonly affect women of reproductive age and are associ...

Development and evaluation of machine learning models to predict mechanical restraint and related coercive measures in hospital psychiatry.

PURPOSE: Coercive practices in psychiatric hospitals present clinical and ethical challenges. Aiming to support prevention, we developed and evaluated...

Development and Validation of an Interpretable Machine Learning Model for Predicting In-Hospital Mortality in Diabetic Patients with Sepsis-Associated Acute Kidney Injury.

BackgroundSepsis-associated acute kidney injury (SA-AKI) is a common and severe complication in critically ill patients, with poor prognosis. Diabetes...

Part III: Digital Dermatopathology Playbook: Change Management, AI Integration, and Strategic Lessons Beyond Technology.

Part III of our three-part series addresses the human, strategic, and forward-looking considerations associated with the adoption of digital pathology...

Biodiversity and chemical regulation: Status quo, data gaps, and recommendations for future action.

Global biodiversity is declining, driven by multiple, interacting pressures including the 5 most prominent of resource use, habitat loss, invasive spe...

From Human-Centered Results to Real-World Impact: Medical Humanities Cross-disciplinary Research.

This collaboratively written paper presents three case studies where researchers from the humanities and from biomedicine collaborated on topics relat...

Assessing the performance of three large language models in thyroid cancer tumour board decision-making.

Large language models (LLMs) have potential to support clinical decision-making, but their role in thyroid cancer multidisciplinary team (MDT) meeting...

AI Adoption in US Cancer Centers: National Cross-Sectional Study of Institutional and Policy Determinants.

BACKGROUND: AI is increasingly being integrated into cancer screening, treatment, and patient care. However, AI adoption across cancer centers varies,...

Association between body composition and recurrence in stage II-III colon cancer: a retrospective cohort study.

INTRODUCTION: Colorectal Cancer (CRC) is a common cause of cancer death and prognostic factors are used to determine management. Patients with advance...

Development and validation of machine learning model for selecting the optimal population pharmacokinetic model for vancomycin.

INTRODUCTION: Therapeutic drug monitoring (TDM) of vancomycin is recommended based on the area under the concentration-time curve (AUC). The Practical...

Explainable artificial intelligence reveals key surgical parameters in robot-assisted and open radical prostatectomy.

Preoperative risk stratification for radical prostatectomy is crucial, yet predicting the wide range of postoperative outcomes remains a significant c...

[Development and validation of an explainable machine learning-based model for predicting mortality risk in polytrauma patients].

Polytrauma is commonly defined as multisystem trauma involving at least two body regions with an Abbreviated Injury Scale (AIS) score ≥ 3, characteriz...