Information Technology

Interpretable ensemble machine learning framework for cardiovascular disease prediction using EMR data and large language models in Ethiopia.

Cardiovascular diseases (CVDs) are leading causes of morbidity and mortality globally, with a growing burden in low- and middle-income countries such as Ethiopia. Early detection is limited by resource constraints, low screening uptake, and a lack of predictive tools tailored to local healthcare systems. This study presents an interpretable ensemble machine learning framework for predicting CVD ri...

Importance of genetic ancestry in pharmacogenomics for precision medicine.

Genetic ancestry refers to an individual's biogeographical origins inferred from correlated allele frequencies shared with individuals from similar ancestral regions. Understanding the complexities of genetic ancestry has proven beneficial in the field of pharmacogenomics (PGx), where personalized medication regimens are optimizing therapeutic outcomes while minimizing the risk of side effects. Wi...

Closing the loop: A systematic review of artificial intelligence in circular e-waste management.

The proliferation of technological advancements, knitted with volatile consumption patterns and poor end-of-life management of discarded electronics, ...

Advancing Psychiatric Safety With the Predictive Risk Identification for Mental Health Events Tool: Retrospective Cohort Study.

BACKGROUND: Patient safety incidents are a leading cause of harm in psychiatric settings, yet early warning systems (EWS) tailored to mental health re...

Enhancing nail disease diagnosis: a capsule network with SE attention and dual backbone models.

Nail diseases, including fungal infections and malignancies, pose significant health risks and may lead to severe complications if not accurately diag...

Explainable AI reveals temporal risk pathways in fall prediction: Extracting clinical insights from multi-horizon machine learning models.

Falls are a leading cause of injury in older adults, making risk prediction a clinical priority. While many machine learning (ML) models exist, they t...

Clinical Profile of Neurosurgical Consultations from the Emergency Department : A Retrospective Analysis.

OBJECTIVE: This study aimed to retrospectively analyze consultations requested from the emergency departments (EDs) to the neurosurgery (NS) departmen...

Development and validation of machine learning models for predicting operative duration in assisted reproductive technology procedures.

BACKGROUND: Accurate prediction of operative duration is essential for efficient scheduling and resource allocation in surgical settings. In assisted ...

A Character-level Convolutional Recurrent Interaction Network for joint traditional Chinese medicine clinical named entity recognition and relation extraction.

The electronic medical record (EMR) of traditional Chinese medicine (TCM) is a crucial document for recording patients' clinical data, structured arou...

Pathophysiological mechanisms of fatigue and multidisciplinary management strategies (Review).

Fatigue is a common clinical symptom, and its complex pathophysiological mechanisms markedly affect the quality of life and social function of patient...

The critical role of standards for AI in digital pathology: Digital Pathology Association Concept Paper.

BACKGROUND: The field of pathology has not yet fully realized the potential of artificial intelligence (AI) and digital pathology. Adoption must be dr...

K-MIMIC: a nationwide Korean multi-institutional Multimodal intensive care dataset.

BACKGROUND: Recent advancements in critical care have highlighted the need for comprehensive, multimodal datasets to support clinical decision-making ...

Digital twin-assisted surgery: technological architecture, integration across surgical phases, implementation challenges and future directions.

Digital twin-assisted surgery referred to the use of a dynamic, patient specific virtual model that mirrored physical patient in real time to enhance ...

FHGNet: A Feature-Centric Hierarchical Network with Graph Attention Layer for Supraventricular Tachycardia Classification.

Automated electrocardiogram (ECG) classification plays a critical role in arrhythmia diagnosis. However, current deep learning-based methodologies fre...

Developing a Multimodal Screening Algorithm for Mild Cognitive Impairment and Early Dementia in Home Health Care: Protocol for a Cross-Sectional Case-Control Study Using Speech Analysis, Large Language Models, and Electronic Health Records.

BACKGROUND: Mild cognitive impairment and early dementia (MCI-ED) are frequently unrecognized in routine care, particularly in home health care (HHC),...

Protocol for the PROSECCA study: a new approach for predicting radiotherapy outcome using artificial intelligence and electronic population-based healthcare data.

INTRODUCTION: Within the UK there are 33 deaths every day from prostate cancer, second only to lung cancer as the most common cause of cancer death in...

AnomLocal: A hybrid local-global anomaly detection model for network security using federated learning.

Securing distributed network infrastructures has become a major priority in modern cybersecurity, where diverse data sources and increasingly sophisti...

Rethinking Cardiopulmonary Bypass Management in The Digital Health Era.

Minimally invasive and robotic cardiac surgery have been developed to reduce surgical trauma, shorten recovery, and improve cosmetic and functional ou...

Electronic health record use in physiotherapy: adoption, perceived relevance and utilisation patterns-a cross-sectional study.

OBJECTIVES: This study aims to assess electronic health record (EHR) use in physiotherapy, identify factors influencing its adoption and evaluate phys...

Explainable Temporal Inference for Irregular Multivariate Time Series. A Case Study for Early Prediction of Multidrug Resistance.

OBJECTIVE: Many healthcare problems involve complex patient trajectories represented as Multivariate Time Series (MTS), with predictions often coming ...