Primary Care

Balancing accuracy and cost in machine learning models for detecting medial vascular calcification in chronic kidney disease: a pilot study.

Machine learning algorithms that integrate multiple biomarkers are increasingly used in disease detection, yet economic considerations are often overlooked. Medial vascular calcification (mVC), a pathology associated with elevated cardiovascular risk in chronic kidney disease (CKD), requires cost-effective diagnostic approaches. This pilot study evaluated the cost-effectiveness of machine learning...

Integrating bioinformatics and machine learning to unravel shared mechanisms and biomarkers in chronic obstructive pulmonary disease and type 2 diabetes.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) and type 2 diabetes mellitus (T2DM) are on the rise. While there is evidence of a link between the two diseases, the pathophysiological mechanisms they share are not fully understood.

Large Language Models for Cancer Communication: Evaluating Linguistic Quality, Safety, and Accessibility in Generative AI

Effective communication about breast and cervical cancers remains a persistent health challenge, with significant gaps in public understanding of ca...

Sociodemographic Profile of People with Diagnosed Pancreatic Cancer in the UK: Retrospective Sentinel Network Cohort Study.

Pancreatic cancer is a devasting disease which is an increasing cause of cancer mortality. The aim of this study was to characterise, using descriptiv...

Artificial Intelligence Powered Audiomics: The Futuristic Biomarker in Pulmonary Medicine - A State-of-the-Art Review.

AI-driven "audiomics" leverages voice and respiratory sounds as non-invasive biomarkers to diagnose and manage pulmonary conditions, including COVID-1...

Long Short-Term Memory Network for Accelerometer-Based Hypertension Classification.

This study investigates the application of a Long Short-Term Memory (LSTM) architecture for classifying hypertension using accelerometer data, specifi...

Integrating bioinformatics and machine learning to identify glomerular injury genes and predict drug targets in diabetic nephropathy.

Diabetes mellitus (DM) is a chronic metabolic disorder that poses significant challenges to public health. Among its various complications, diabetic n...

Effects of neonicotinoid pesticide exposure in the first trimester on gestational diabetes mellitus based on interpretable machine learning.

BACKGROUND: Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications and seriously threatens the health of mothers and of...

Ethical Aspects of the Use of Social Robots in Elderly Care -- A Systematic Qualitative Review

Background: The use of social robotics in elderly care is increasingly discussed as one way of meeting emerging care needs due to scarce resources. ...

Machine-Learning-Guided Screening of Advantageous Solvents for Solid Polymer Electrolytes in Lithium Metal Batteries.

Trace residual solvents in solid polymer electrolytes (SPEs) significantly affect electrolyte and interface properties, where optimal selection enhanc...

The gut microbiome and metabolome in children with a first febrile urinary tract infection: a pilot study.

BACKGROUND: Urinary tract infection (UTI) is a common bacterial infection in the pediatric population. Febrile urinary tract infection (fUTI) can lead...

Predicting overactive bladder from inflammatory markers: A machine learning approach using NHANES 2005-2020.

Overactive bladder (OAB), a prevalent condition characterized by urgency and nocturia, imposes significant burdens on both quality of life and healthc...

Exploring Bio-Impedance Sensing for Intelligent Wearable Devices.

The rapid growth of wearable technology has opened new possibilities for smart health-monitoring systems. Among various sensing methods, bio-impedance...

Assessing artificial intelligence in breast screening with stratified results on 306 839 mammograms across geographic regions, age, breast density and ethnicity: A Retrospective Investigation Evaluating Screening (ARIES) study.

OBJECTIVES: Evaluate an Artificial Intelligence (AI) system in breast screening through stratified results across age, breast density, ethnicity and s...

Artificial Intelligence and Assistive Robotics in Healthcare Services: Applications in Silver Care.

Artificial intelligence (AI) and assistive robotics can transform older-person care by offering new, personalised solutions for an ageing population. ...

AI-Driven Drug Target Screening Platform Identified Oncogene CACNA2D1 Activated by Enhancer Infestation in Epstein-Barr Virus-Associated Nasopharyngeal Carcinoma.

The management of nasopharyngeal cancer (NPC) is rapidly evolving, with immune checkpoint inhibitors emerging as a prominent treatment approach. Howev...

Multimodal Fusion of Glucose Monitoring and Food Imagery for Caloric Content Prediction

Effective dietary monitoring is critical for managing Type 2 diabetes, yet accurately estimating caloric intake remains a major challenge. While con...

Continuous Temporal Learning of Probability Distributions via Neural ODEs with Applications in Continuous Glucose Monitoring Data

Modeling the continuous--time dynamics of probability distributions from time--dependent data samples is a fundamental problem in many fields, inclu...

Integrating Natural Language Processing and Exercise Monitoring for Early Diagnosis of Metabolic Syndrome: A Deep Learning Approach

Metabolic syndrome (MetS) is a medication condition characterized by abdominal obesity, insulin resistance, hypertension and hyperlipidemia. It incr...

Predicting dementia in people with Parkinson's disease.

Parkinson's disease (PD) exhibits a variety of symptoms, with approximately 25% of patients experiencing mild cognitive impairment and 45% developing ...