Information Technology

Attention and dilated convolutions inclusive deep-CNN with multiplexed texture features to diagnose Pathological and High Myopia.

In today's era, precise and timely diagnosis of ocular diseases are crucial as these disorders jeopardize millions of visions. Early detection and proactive management can minimize vision threatening complications from these disorders. High Myopia(HM) and Pathological Myopia(PM), are the globally prevalent ocular diseases, which can impair vision acuity and productivity across all age groups. Rout...

Integrating AI-driven wearable devices and biometric data into stroke risk assessment: A review of opportunities and challenges.

Stroke is a leading cause of morbidity and mortality worldwide, and early detection of risk factors is critical for prevention and improved outcomes. Traditional stroke risk assessments, relying on sporadic clinical visits, fail to capture dynamic changes in risk factors such as hypertension and atrial fibrillation (AF). Wearable technology (devices), combined with biometric data analysis, offers ...

Navigating the unseen peril: safeguarding medical imaging in the age of AI.

In response to the increasing significance of artificial intelligence (AI) in healthcare, there has been increased attention - including a Presidentia...

Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions.

OBJECTIVES: Increasing operational pressures on emergency departments (ED) make it imperative to quickly and accurately identify patients requiring ur...

Development and validation of electronic health record-based, machine learning algorithms to predict quality of life among family practice patients.

Health-related quality of life (HRQol) is a crucial dimension of care outcomes. Many HRQoL measures exist, but methodological and implementation chall...

Multi-modal large language models in radiology: principles, applications, and potential.

Large language models (LLMs) and multi-modal large language models (MLLMs) represent the cutting-edge in artificial intelligence. This review provides...

Digital Information Ecosystems in Modern Care Coordination and Patient Care Pathways and the Challenges and Opportunities for AI Solutions.

The integration of digital technologies into health care has significantly enhanced the efficiency and effectiveness of care coordination. Our perspec...

Integrating graph and reinforcement learning for vaccination strategies in complex networks.

Pandemics like COVID-19 have a huge impact on human society and the global economy. Vaccines are effective in the fight against these pandemics but of...

Predicting the physiological effects of multiple drugs using electronic health record.

Various computational models have been developed to understand the physiological effects of drug-drug interactions, which can contribute to more effec...

A randomized controlled trial on evaluating clinician-supervised generative AI for decision support.

BACKGROUND: The integration of generative artificial intelligence (AI) as clinical decision support systems (CDSS) into telemedicine presents a signif...

Developing a prediction model for cognitive impairment in older adults following critical illness.

BACKGROUND: New or worsening cognitive impairment or dementia is common in older adults following an episode of critical illness, and screening post-d...

Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation.

Cyanobacteria strains have the potential to produce bioactive compounds that can be used in therapeutics and bioremediation. Therefore, compiling all ...

Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring.

Non-contact heart monitoring is crucial in advancing telemedicine, fitness tracking, and mass screening. Remote photoplethysmography (rPPG) is a non-c...

Improving care for amyotrophic lateral sclerosis with artificial intelligence and affective computing.

BACKGROUND: Patients with ALS often face difficulties expressing emotions due to impairments in facial expression, speech, body language, and cognitiv...

Assessing the Usability and Effectiveness of an AI-Powered Telehealth Platform: Mixed Methods Study on the Perspectives of Patients and Providers.

BACKGROUND: Telemedicine has revolutionized health care by significantly enhancing accessibility and convenience, yet barriers remain, such as provide...

Predicting Asthma Exacerbations Using Machine Learning Models.

INTRODUCTION: Although clinical, functional, and biomarker data predict asthma exacerbations, newer approaches providing high accuracy of prognosis ar...

Mobile Accelerometer Applications in Core Muscle Rehabilitation and Pre-Operative Assessment.

Individual physiotherapy is crucial in treating patients with various pain and health issues, and significantly impacts abdominal surgical outcomes an...

A model for integrating palliative care into Eastern Mediterranean health systems with a primary care approach.

BACKGROUND AND AIMS: Palliative care in the Eastern Mediterranean Region (EMR) faces challenges despite the high number of patients in need. To provid...

Extracting social determinants of health from inpatient electronic medical records using natural language processing.

BACKGROUND: Social determinants of health (SDOH) have been shown to be important predictors of health outcomes. Here we developed methods to extract t...

Transformer-based deep learning model for the diagnosis of suspected lung cancer in primary care based on electronic health record data.

BACKGROUND: Due to its late stage of diagnosis lung cancer is the commonest cause of death from cancer in the UK. Existing epidemiological risk models...