Information Technology

Deep learning empowered breast cancer diagnosis: Advancements in detection and classification.

Recent advancements in AI, driven by big data technologies, have reshaped various industries, with a strong focus on data-driven approaches. This has resulted in remarkable progress in fields like computer vision, e-commerce, cybersecurity, and healthcare, primarily fueled by the integration of machine learning and deep learning models. Notably, the intersection of oncology and computer science ha...

European beech spring phenological phase prediction with UAV-derived multispectral indices and machine learning regression.

Acquiring phenological event data is crucial for studying the impacts of climate change on forest dynamics and assessing the risks associated with the early onset of young leaves. Large-scale mapping of forest phenological timing using Earth observation (EO) data could enhance our understanding of these processes through an added spatial component. However, translating traditional ground-based phe...

Combining Federated Machine Learning and Qualitative Methods to Investigate Novel Pediatric Asthma Subtypes: Protocol for a Mixed Methods Study.

BACKGROUND: Pediatric asthma is a heterogeneous disease; however, current characterizations of its subtypes are limited. Machine learning (ML) methods...

Revolutionizing urogynecology: Machine learning application with patient-centric technology: Promise, challenges, and future directions.

In an epoch where digital innovation is redefining the medical landscape, electronic health records (EHRs) stand out as a pivotal transformative force...

Validation of an Electronic Health Record-Based Machine Learning Model Compared With Clinical Risk Scores for Gastrointestinal Bleeding.

BACKGROUND & AIMS: Guidelines recommend use of risk stratification scores for patients presenting with gastrointestinal bleeding (GIB) to identify ver...

Prospective Randomized Study on the Use of Robot-Assisted Postoperative Visits.

Robot-assisted visits, as part of telemedicine, can offer doctors the opportunity to take care of patients. Due to the COVID-19 pandemic, there has b...

Estimating the prevalence of diabetic retinopathy in electronic health records with massive missing labels.

OBJECTIVE: The paper aims to address the problem of massive unlabeled patients in electronic health records (EHR) who potentially have undiagnosed dia...

Adherence of studies involving artificial intelligence in the analysis of ophthalmology electronic medical records to AI-specific items from the CONSORT-AI guideline: a systematic review.

PURPOSE: In the context of ophthalmologic practice, there has been a rapid increase in the amount of data collected using electronic health records (E...

Climate change and artificial intelligence in healthcare: Review and recommendations towards a sustainable future.

The rapid advancement of artificial intelligence (AI) in healthcare has revolutionized the industry, offering significant improvements in diagnostic a...

Predictability of buprenorphine-naloxone treatment retention: A multi-site analysis combining electronic health records and machine learning.

BACKGROUND AND AIMS: Opioid use disorder (OUD) and opioid dependence lead to significant morbidity and mortality, yet treatment retention, crucial for...

Development of a quantitative index system for evaluating the quality of electronic medical records in disease risk intelligent prediction.

OBJECTIVE: This study aimed to develop and validate a quantitative index system for evaluating the data quality of Electronic Medical Records (EMR) in...

Application of a natural language processing artificial intelligence tool in psoriasis: A cross-sectional comparative study on identifying affected areas in patients' data.

Psoriasis is an immune-mediated skin disease affecting approximately 3% of the global population. Proper management of this condition necessitates the...

Advancements in intrusion detection: A lightweight hybrid RNN-RF model.

Computer networks face vulnerability to numerous attacks, which pose significant threats to our data security and the freedom of communication. This p...

EHR coding with hybrid attention and features propagation on disease knowledge graph.

And sentences associated with these attributes and relationships have been neglected. in this paper ►We propose an end-to-end model called Knowledge G...

A Responsible Framework for Applying Artificial Intelligence on Medical Images and Signals at the Point of Care: The PACS-AI Platform.

The potential of artificial intelligence (AI) in medicine lies in its ability to enhance clinicians' capacity to analyse medical images, thereby impro...

Artificial intelligence approaches for phenotyping heart failure in U.S. Veterans Health Administration electronic health record.

AIMS: Heart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical record...

Applying generative AI with retrieval augmented generation to summarize and extract key clinical information from electronic health records.

BACKGROUND: Malnutrition is a prevalent issue in aged care facilities (RACFs), leading to adverse health outcomes. The ability to efficiently extract ...

Artificial Intelligence Applications in Diabetic Retinopathy: What We Have Now and What to Expect in the Future.

Diabetic retinopathy (DR) is a major complication of diabetes mellitus and is a leading cause of vision loss globally. A prompt and accurate diagnosis...

Fiber-optics IoT healthcare system based on deep reinforcement learning combinatorial constraint scheduling for hybrid telemedicine applications.

Telemedicine is an emerging development in the healthcare domain, where the Internet of Things (IoT) fiber optics technology assists telemedicine appl...

Transformers and large language models in healthcare: A review.

With Artificial Intelligence (AI) increasingly permeating various aspects of society, including healthcare, the adoption of the Transformers neural ne...