Information Technology

A machine learning model using clinical notes to estimate PHQ-9 symptom severity scores in depressed patients.

BACKGROUND: Lack of widespread use of the Patient Health Questionnaire 9-item (PHQ-9) in clinical practice inhibits measurement of treatment follow-up for patients with major depressive disorder (MDD). This study developed, validated and applied a machine learning model to estimate PHQ-9 scores for MDD patients using relevant notes from electronic medical records (EMR).

Chaotic gradient based optimization with fuzzy temporal optimized CNN for heart failure prediction.

Heart failure is a leading cause of premature death, especially among individuals with a sedentary lifestyle. Early and accurate detection is essential to prevent the progression of this situation. However, many existing prediction systems failed to detect early and accurately, also taking more time to detect. To address these issues, we propose an advanced heart failure detection model that combi...

Enhancing Security Operations Center: Wazuh Security Event Response with Retrieval-Augmented-Generation-Driven Copilot.

The sophistication of cyberthreats demands more efficient and intelligent tools to support Security Operations Centers (SOCs) in managing and mitigati...

EHR-ML: A data-driven framework for designing machine learning applications with electronic health records.

OBJECTIVE: The healthcare landscape is experiencing a transformation with the integration of Artificial Intelligence (AI) into traditional analytic wo...

Discovering patient groups in sequential electronic healthcare data using unsupervised representation learning.

INTRODUCTION: Unsupervised feature learning methods inspired by natural language processing (NLP) models are capable of constructing patient-specific ...

Enhanced Phenotype Identification of Common Ocular Diseases in Real-World Datasets.

OBJECTIVE: For studies using real-world data, accurately identifying patients with phenotypes of interest is challenging. To identify cohorts of inter...

Generative adversarial local density-based unsupervised anomaly detection.

Anomaly detection is crucial in areas such as financial fraud identification, cybersecurity defense, and health monitoring, as it directly affects the...

Deep learning based prediction of depression and anxiety in patients with type 2 diabetes mellitus using regional electronic health records.

BACKGROUND: Depression and anxiety are prevalent mental health conditions among individuals with type 2 diabetes mellitus (T2DM), who exhibit unique v...

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 care through interconnected medical devices and syste...

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 a growing global concern. Early detection of these...

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 using electronic health record (EHR) data to identi...

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. Methods to hasten the identification of these pat...

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-identification methods using claims data can be time-inte...

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 improve decision-making and to empower patients w...

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 prevalent form of cybercrime. These attacks are rel...

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 neurology and other medical specialties. A large langua...

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), but performance differences between models, the...

The Role of Artificial Intelligence in Health Care.

Artificial intelligence (AI) plays a leading role in transmuting the field of healthcare. Numerous aspects of AI have been incorporated into the healt...

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 human genomic features into biomedical knowledge g...

Generative artificial intelligence in graduate medical education.

Generative artificial intelligence (GenAI) is rapidly transforming various sectors, including healthcare and education. This paper explores the potent...