Information Technology

Using Domain Adaptation and Inductive Transfer Learning to Improve Patient Outcome Prediction in the Intensive Care Unit: Retrospective Observational Study.

BACKGROUND: Accurate patient outcome prediction in the intensive care unit (ICU) can potentially lead to more effective and efficient patient care. Deep learning models are capable of learning from data to accurately predict patient outcomes, but they typically require large amounts of data and computational resources. Transfer learning (TL) can help in scenarios where data and computational resou...

Innovation and challenges of artificial intelligence technology in personalized healthcare.

As the burgeoning field of Artificial Intelligence (AI) continues to permeate the fabric of healthcare, particularly in the realms of patient surveillance and telemedicine, a transformative era beckons. This manuscript endeavors to unravel the intricacies of recent AI advancements and their profound implications for reconceptualizing the delivery of medical care. Through the introduction of innova...

Technological Advances in the Diagnosis of Cardiovascular Disease: A Public Health Strategy.

This article reviews technological advances and global trends in the diagnosis, treatment, and monitoring of cardiovascular diseases. A bibliometric a...

Metabolic dysfunctions predict the development of Alzheimer's disease: Statistical and machine learning analysis of EMR data.

INTRODUCTION: The incidence of Alzheimer's disease (AD) and obesity rise concomitantly. This study examined whether factors affecting metabolism, race...

Assessment of EMR ML Mining Methods for Measuring Association between Metal Mixture and Mortality for Hypertension.

INTRODUCTION: There are limited data available regarding the connection between heavy metal exposure and mortality among hypertension patients.

Artificial Intelligence-Based Digital Biomarkers for Type 2 Diabetes: A Review.

Type 2 diabetes mellitus (T2DM), a complex metabolic disorder that burdens the health care system, requires early detection and treatment. Recent stri...

The revolution of metaverse in surgery: a mini-review with video.

The virtual reality (VR) is an application in which people can interact each other with their own avatars. Metaverse has already been tested in numero...

Machine learning characterization of a rare neurologic disease via electronic health records: a proof-of-principle study on stiff person syndrome.

BACKGROUND: Despite the frequent diagnostic delays of rare neurologic diseases (RND), it remains difficult to study RNDs and their comorbidities due t...

Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers.

BACKGROUND: Efforts are underway to capitalize on the computational power of the data collected in electronic medical records (EMRs) to achieve a lear...

Innovative approaches in stroke care: insights from recent studies on patient delay, telemedicine, artificial intelligence, and machine learning.

Multimodal deep learning models utilizing chest X-ray and electronic health record data for predictive screening of acute heart failure in emergency department.

BACKGROUND AND OBJECTIVES: Ambiguity in diagnosing acute heart failure (AHF) leads to inappropriate treatment and potential side effects of rescue med...

Communicating exploratory unsupervised machine learning analysis in age clustering for paediatric disease.

BACKGROUND: Despite the increasing availability of electronic healthcare record (EHR) data and wide availability of plug-and-play machine learning (ML...

Evaluating the effectiveness of a sliding window technique in machine learning models for mortality prediction in ICU cardiac arrest patients.

Extensive research has been devoted to predicting ICU mortality, to assist clinical teams managing critical patients. Electronic health records (EHR) ...

Advancements in AI based healthcare techniques with FOCUS ON diagnostic techniques.

Since the past decade, the interest towards more precise and efficient healthcare techniques with special emphasis on diagnostic techniques has increa...

An Innovative Device Based on Human-Machine Interface (HMI) for Powered Wheelchair Control for Neurodegenerative Disease: A Proof-of-Concept.

In the global context, advancements in technology and science have rendered virtual, augmented, and mixed-reality technologies capable of transforming...

Ethical Challenges and Opportunities in Applying Artificial Intelligence to Cardiovascular Medicine.

Much anticipation surrounds artificial intelligence's (AI) emergence as a promising tool in health care. It offers potential to revolutionise clinical...

MoCab: A framework for the deployment of machine learning models across health information systems.

BACKGROUND AND OBJECTIVE: Machine learning models are vital for enhancing healthcare services. However, integrating them into health information syste...

Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility.

Advances in artificial intelligence (AI) and machine learning systems promise faster, more efficient, and more personalized care. While many of these ...

Advancing Medical Imaging Research Through Standardization: The Path to Rapid Development, Rigorous Validation, and Robust Reproducibility.

Artificial intelligence (AI) has made significant advances in radiology. Nonetheless, challenges in AI development, validation, and reproducibility pe...

Rapid assessment of heavy metal accumulation capability of Sedum alfredii using hyperspectral imaging and deep learning.

Hyperaccumulators are the material basis and key to the phytoremediation of heavy metal contaminated soils. Conventional methods for screening hyperac...