Information Technology

Establishing a Validation Framework of Treatment Discontinuation in Claims Data Using Natural Language Processing and Electronic Health Records.

Measuring medication discontinuation in claims data primarily relies on the gaps between prescription fills, but such definitions are rarely validated. This study aimed to establish a natural language processing (NLP)-based validation framework to evaluate the performance of claims-based discontinuation algorithms for commonly used medications against NLP-based reference standards from electronic ...

The present and future of cardiological telemonitoring in Europe: a statement from seven European countries.

Cardiovascular diseases remain one of the leading causes of death worldwide, placing a significant burden on individuals, families and healthcare systems. Telemedicine, in particular remote monitoring of patients with cardiovascular diseases, reduces this burden as it links the continuous monitoring of the health status with individual education and adaptation of the therapy to the needs of the pa...

Advancing Clinical Information Systems: Harnessing Telemedicine, Data Science, and AI for Enhanced and More Precise Healthcare Delivery.

OBJECTIVE: In this synopsis, the editors of the Clinical Information Systems (CIS) section of the IMIA Yearbook of Medical Informatics overview recent...

An integrated AI knowledge graph framework of bacterial enzymology and metabolism.

The study of bacterial metabolism holds immense significance for improving human health and advancing agricultural practices. The prospective applicat...

Identification of patients at risk for pancreatic cancer in a 3-year timeframe based on machine learning algorithms.

Early detection of pancreatic cancer (PC) remains challenging largely due to the low population incidence and few known risk factors. However, screeni...

A Review of AIoT-Based Human Activity Recognition: From Application to Technique.

This scoping review paper redefines the Artificial Intelligence-based Internet of Things (AIoT) driven Human Activity Recognition (HAR) field by syste...

Clinical implementation of AI-based screening for risk for opioid use disorder in hospitalized adults.

Adults with opioid use disorder (OUD) are at increased risk for opioid-related complications and repeated hospital admissions. Routine screening for p...

A Quantitative Study of Factors Influencing Myasthenia Gravis Telehealth Examination Score.

INTRODUCTION/AIMS: The adoption of telemedicine is generally considered as advantageous for patients and physicians, but there is limited rigorous ass...

Multimodal learning-based speech enhancement and separation, recent innovations, new horizons, challenges and real-world applications.

With the increasing global prevalence of disabling hearing loss, speech enhancement technologies have become crucial for overcoming communication barr...

Leveraging large language models to mimic domain expert labeling in unstructured text-based electronic healthcare records in non-english languages.

BACKGROUND: The integration of big data and artificial intelligence (AI) in healthcare, particularly through the analysis of electronic health records...

The interpretable machine learning model for depression associated with heavy metals via EMR mining method.

Limited research exists on the association between depression and heavy metal exposure. This study aims to develop an interpretable and efficient mach...

DeePMD-GNN: A DeePMD-kit Plugin for External Graph Neural Network Potentials.

Machine learning potentials (MLPs) have revolutionized molecular simulation by providing efficient and accurate models for predicting atomic interacti...

[Therapeutic patient education and telemedicine in the age of artificial intelligence].

Since the promulgation of the July 21, 2009 law on hospital reform and patients, health and territories, known as the HPST law, therapeutic patient ed...

Future horizons in diabetes: integrating AI and personalized care.

Diabetes is a global health crisis with rising incidence, mortality, and economic burden. Traditional markers like HbA1c are insufficient for capturin...

Improving diagnosis-based quality measures: an application of machine learning to the prediction of substance use disorder among outpatients.

OBJECTIVE: Substance use disorder (SUD) is clinically under-detected and under-documented. We built and validated machine learning (ML) models to esti...

A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods.

BACKGROUND: Artificial intelligence (AI) enabled algorithms can detect or predict cardiovascular conditions using electrocardiogram (ECG) data. Clinic...

Identification of heart failure subtypes using transformer-based deep learning modelling: a population-based study of 379,108 individuals.

BACKGROUND: Heart failure (HF) is a complex syndrome with varied presentations and progression patterns. Traditional classification systems based on l...

: Towards Autonomous Electronic Health Record Navigation.

Clinicians spend large amounts of time on clinical documentation, and inefficiencies impact quality of care and increase clinician burnout. Despite th...

Effectiveness of machine learning methods in detecting grooming: a systematic meta-analytic review.

This study presents a systematic review (SR) and meta-analysis (MA) on the use of machine learning (ML) methods for detecting online grooming, a form ...

Deep representation learning for clustering longitudinal survival data from electronic health records.

Precision medicine requires accurate identification of clinically relevant patient subgroups. Electronic health records provide major opportunities fo...