Information Technology

Challenges involved in the application of artificial intelligence in gastroenterology: The race is on!

Gastroenterology is a particularly data-rich field, generating vast repositories of data that are a fruitful ground for artificial intelligence (AI) and machine learning (ML) applications. In this opinion review, we initially elaborate on the current status of the application of AI/ML-based software in gastroenterology. Currently, AI/ML-based models have been developed in the following application...

Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks.

OBJECTIVE: Development of electronic health records (EHR)-based machine learning models for pediatric inpatients is challenged by limited training data. Self-supervised learning using adult data may be a promising approach to creating robust pediatric prediction models. The primary objective was to determine whether a self-supervised model trained in adult inpatients was noninferior to logistic re...

Enhancing Healthcare Through Telehealth Ecosystems: Impacts and Prospects.

This poster presents a comprehensive assessment of the transformative potential of telehealth ecosystems, integrating Internet of Things (IoT), Intern...

Advances and opportunities in the new digital era of telemedicine, e-health, artificial intelligence, and beyond.

[Exploration of remote management and an intelligent platform for in-hospital respiratory therapy].

The construction of an intelligent remote management platform for respiratory therapy, utilizing artificial intelligence (AI) and the electronic medic...

AI Chatbots, Health Privacy, and Challenges to HIPAA Compliance.

Upper-Limb Rehabilitation of Patients with Neuromotor Deficits Using Impedance-Based Control of a 6-DOF Robot.

This paper presents a study on the use of impedance-based control of a 6-degree-of-freedom robot for upper-limb rehabilitation of patients with neurom...

Fall Risk Prediction in Older Adults Using Free-Text Nursing Notes and Medications in Electronic Health Records.

Nursing notes in Electronic Health Records (EHR) contain critical health information, including fall risk factors. However, an exploration of fall ris...

Unlocking the Power of EHRs: Harnessing Unstructured Data for Machine Learning-based Outcome Predictions.

The integration of Electronic Health Records (EHRs) with Machine Learning (ML) models has become imperative in examining patient outcomes due to the v...

Meta-EHR: A meta-learning approach for electronic health records with a high imbalanced ratio and missing rate.

Data imbalance is a practical and crucial issue in deep learning. Moreover, real-world datasets, such as electronic health records (EHR), often suffer...

Clinical Risk Prediction Models with Meta-Learning Prototypes of Patient Heterogeneity.

Hospitalized patients sometimes have complex health conditions, such as multiple diseases, underlying diseases, and complications. The heterogeneous p...

A Deep Learning Approach for Grading of Motor Impairment Severity in Parkinson's Disease.

Objective and quantitative monitoring of movement impairments is crucial for detecting progression in neurological conditions such as Parkinson's dise...

PPAD: a deep learning architecture to predict progression of Alzheimer's disease.

MOTIVATION: Alzheimer's disease (AD) is a neurodegenerative disease that affects millions of people worldwide. Mild cognitive impairment (MCI) is an i...

The Progress of Speech Recognition in Health Care: Surgery as an Example.

Artificial Intelligence (AI) is a computer system that simulates intelligent human behavior. The use of AI is rapidly shifting Healthcare. Speech reco...

Bottom-up and top-down paradigms of artificial intelligence research approaches to healthcare data science using growing real-world big data.

OBJECTIVES: As the real-world electronic health record (EHR) data continue to grow exponentially, novel methodologies involving artificial intelligenc...

In-Hospital Cancer Mortality Prediction by Multimodal Learning of Non-English Clinical Texts.

Predicting important outcomes in patients with complex medical conditions using multimodal electronic medical records remains challenge. We trained a ...

The Necessity of Multiple Data Sources for ECG-Based Machine Learning Models.

Even though the interest in machine learning studies is growing significantly, especially in medicine, the imbalance between study results and clinica...

Assessing the FAIRness of Deep Learning Models in Cardiovascular Disease Using Computed Tomography Images: Data and Code Perspective.

The interest in the application of AI in medicine has intensely increased over the past decade with most of the changes in the past five years. Most r...

Domain Knowledge-Driven Generation of Synthetic Healthcare Data.

Healthcare longitudinal data collected around patients' life cycles, today offer a multitude of opportunities for healthcare transformation utilizing ...

A Masked Language Model for Multi-Source EHR Trajectories Contextual Representation Learning.

Using electronic health records data and machine learning to guide future decisions needs to address challenges, including 1) long/short-term dependen...