Information Technology

MAP-CARE: Enhancing Cross-Lingual Medical Intervention Terms.

Traditional keyword-based or single-language systems are not able to align data from surgical and interventional procedures, especially from non-English healthcare systems. This study aims to develop a pipeline for cross-lingual retrieval and integration of medical procedures data. Methods: MAP-CARE is a novel framework that leverages Large Language Models (LLMs) for translating and transforming m...

A Novel Approach for Handling Missing Data in Multivariate Time Series Clustering: Case Study on Predicting Delayed Graft Function.

Single value imputation and Multiple Imputation Chained Equation (MICE) are widely used for handling missing data cross-sectionally. Although several predictive and generative deep learning based methods address missing multivariate time series (MTS) data under Missing at Random /Missing Completely at Random types of missingness, their performance banks on the interpretation of missingness. Missin...

From Records to Relationships: An Ontology-Based Knowledge Graph Framework for Cancer Data Interoperability.

Healthcare data's rapid growth and complexity reveal a significant challenge: we lack effective approaches to integrate multimodal information into un...

Exploring the Role of Robotics in Nursing Homes: Insights from Healthcare Professionals.

This paper reports findings from a qualitative descriptive study exploring the potential use of service robots in nursing homes. Participants in three...

Context-Free Grammar-Guided Generation of FHIR Resources Using Large Language Models.

Large Language Models (LLMs) have shown remarkable capabilities in medical information extraction and data transformation tasks. However, their unstru...

The Evolving Utility of Artificial Intelligence-Based Tools for the Detection of Heart Failure and Cardiomyopathies: From Potential to Implementation.

PURPOSE OF REVIEW: Artificial intelligence (AI) is poised to transform heart failure (HF) care across the clinical continuum, yet a substantial gap re...

Evaluating Postpartum Hemorrhage Transfusion Risk with a Machine Learning Model for Informed Consent: Retrospective Cohort Study.

BACKGROUND: Postpartum hemorrhage requiring a blood transfusion is a concern for patients and clinicians. Postpartum hemorrhage risk and mode of deliv...

A Practical Approach to Assessing the Completeness of Electronic Health Records for Medical Research: Data Quality Study.

BACKGROUND: Data quality is the degree to which data are fit for their intended purpose and is described using quality dimensions. The increased use o...

Beyond the Model: Practical Insights from Monitoring Predictive Models Across Diverse Clinical Workflows.

OBJECTIVES: To support the artificial intelligence (AI) lifecycle in an integrated academic health system, we implemented a modular monitoring system ...

Post-swallowing voice-based aspiration screening in dysphagia using a deep learning approach: insights from audio segmentation.

Dysphagia presents a serious risk of aspiration that requires continuous monitoring. This study introduces standardized 2 s voice segments for aspirat...

Dynamic Bayesian networks to predict loss of kidney function: a cross-institution use case in a large cohort with or at-risk of CKD.

BACKGROUND: Substantial loss of kidney function, measured as ≥40% decline in estimated glomerular filtration rate (eGFR) within a 2-year period, is as...

From patient notes to prognostication: The revolutionary potential of event-based foundation models in orthopaedics.

The integration of artificial intelligence (AI)-equipped tools into electronic health record (EHR) platforms may drive the evolution of orthopaedic di...

Extracting Signs and Symptoms of Hypertensive Disorders in Pregnancy from Clinical Notes Using Natural Language Processing.

PURPOSE: Hypertensive disorders in pregnancy (HDP) affect 16% of births in the United States. In this pilot study, we conducted a preliminary evaluati...

Multisource Machine Learning Model for Detecting Referral-Warranted Retinopathy of Prematurity.

PURPOSE: To develop a multisource machine learning model for detecting referral-warranted retinopathy of prematurity (RW-ROP) using retinal images and...

Technology-Enabled Crisis Care for Youth: Bridging the Gap.

The article discusses technological advancements in mental health care for youth in crisis, addressing workforce shortages and enhancing care delivery...

Reduction of Electronic Medical Record Documentation Time and Burden Using an Artificial Intelligence Scribe Tool in an Orthopaedic Clinic.

INTRODUCTION: With an aging population in the United States, the demand for joint arthroplasty procedures continues to rise. As patient volumes increa...

Extracting Social Determinants of Health From Electronic Health Records: Development and Comparison of Rule-Based and Large Language Model Methods.

BACKGROUND: Social determinants of health (SDoH) are critical drivers of health outcomes but are often underdocumented in structured electronic health...

Multimodal Artificial Intelligence for Early Detection and Precision Management of Inflammatory and Infiltrative Cardiomyopathies.

BACKGROUND: Inflammatory and infiltrative cardiomyopathies, including cardiac sarcoidosis, transthyretin amyloidosis, and autoimmune myocarditis, are ...

A blockchain-secured 6G smartgrid framework for resilient renewable energy integration and intelligent anomaly detection.

The integration of intermittent renewable energy into smart grids introduces critical vulnerabilities in security, transparency, and real-time resilie...

An innovative hybrid ANN inception model for detecting and preventing man-in-the-middle attacks in smart networks for shielding sensitive data.

Man-in-the-Middle (MitM) attacks represent a significant cybersecurity challenge, particularly within the rapidly growing domain of smart networks and...