Practice Management

Information Technology

Latest AI and machine learning research in information technology for healthcare professionals.

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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 ...

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-edg...

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 eff...

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 a...

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

Various computational models have been developed to understand the physiological effects of drug-dru...

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 ...

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...

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 therapeu...

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 screen...

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 faci...

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...

Predicting Asthma Exacerbations Using Machine Learning Models.

INTRODUCTION: Although clinical, functional, and biomarker data predict asthma exacerbations, newer ...

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 si...

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 desp...

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 healt...

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 canc...

Shareable artificial intelligence to extract cancer outcomes from electronic health records for precision oncology research.

Databases that link molecular data to clinical outcomes can inform precision cancer research into no...

AI solutions for overcoming delays in telesurgery and telementoring to enhance surgical practice and education.

Artificial intelligence (AI) has emerged as a transformative tool in surgery, particularly in telesu...

Mitigating Diagnostic Errors in Lung Cancer Classification: A Multi-Eyes Principle to Uncertainty Quantification.

In radiology, particularly in lung cancer diagnosis, diagnostic errors and cognitive biases pose sub...

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