Information Technology

Stacking Ensemble Deep Learning for Real-Time Intrusion Detection in IoMT Environments.

The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling advanced patient car...

Guardian-BERT: Early detection of self-injury and suicidal signs with language technologies in electronic health reports.

Mental health disorders, including non-suicidal self-injury (NSSI) and suicidal behavior, represent ...

Classifying Unstructured Text in Electronic Health Records for Mental Health Prediction Models: Large Language Model Evaluation Study.

BACKGROUND: Prediction models have demonstrated a range of applications across medicine, including u...

Piloting an automated query and scoring system to facilitate APDS patient identification from health systems.

INTRODUCTION: Patients with activated PI3Kδ syndrome (APDS) may elude diagnoses for nearly a decade....

Identification of an ANCA-associated vasculitis cohort using deep learning and electronic health records.

BACKGROUND: ANCA-associated vasculitis (AAV) is a rare but serious disease. Traditional case-identif...

The promise of AI in healthcare: transforming communication and decision-making for patients.

By addressing communication gaps, the integration of AI tools in healthcare has a greater ability to...

Investigating the performance of multivariate LSTM models to predict the occurrence of Distributed Denial of Service (DDoS) attack.

In the current cybersecurity landscape, Distributed Denial of Service (DDoS) attacks have become a p...

Neurological history both twinned and queried by generative artificial intelligence.

BACKGROUND AND OBJECTIVES: We propose the use of GPT-4 to facilitate initial history-taking in neuro...

Classification performance and reproducibility of GPT-4 omni for information extraction from veterinary electronic health records.

Large language models (LLMs) can extract information from veterinary electronic health records (EHRs...

The Role of Artificial Intelligence in Health Care.

Artificial intelligence (AI) plays a leading role in transmuting the field of healthcare. Numerous a...

Homo Sapiens Chromosomal Location Ontology: A Framework for Genomic Data in Biomedical Knowledge Graphs.

The Homo sapiens Chromosomal Location Ontology (HSCLO) is designed to facilitate the integration of ...

Generative artificial intelligence in graduate medical education.

Generative artificial intelligence (GenAI) is rapidly transforming various sectors, including health...

Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review.

BACKGROUND: Natural language processing (NLP) and machine learning (ML) techniques may help harness ...

Harnessing NLP to investigate biomarker interactions and CVD risks in elderly chronic kidney disease patients.

Chronic kidney disease (CKD) significantly increases the risk of CVD diseases, particularly among el...

Multimodal deep learning for predicting in-hospital mortality in heart failure patients using longitudinal chest X-rays and electronic health records.

Amid an aging global population, heart failure has become a leading cause of hospitalization among o...

The Use of Artificial Intelligence for Endoscopic Evaluation of the Small Bowel.

There remains great potential for widespread implementation of artificial intelligence (AI) in manag...

AI-based models to predict decompensation on traumatic brain injury patients.

Traumatic Brain Injury (TBI) is a form of brain injury caused by external forces, resulting in tempo...

Utilizing natural language processing to identify pediatric patients experiencing status epilepticus.

PURPOSE: Compare the identification of patients with established status epilepticus (ESE) and refrac...

Editorial: Cybersecurity and artificial intelligence: advances, challenges, opportunities, threats.

Predicting and Ranking Diabetic Ketoacidosis Risk Among Youth with Type 1 Diabetes with a Clinic-to-Clinic Transferrable Machine Learning Model.

To use electronic health record (EHR) data to develop a scalable and transferrable model to predict...

Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring.

Sepsis remains a leading cause of morbidity and mortality worldwide due to its rapid progression and...