AIMC Topic: Humans

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Characterization of Synthetic Health Data Using Rule-Based Artificial Intelligence Models.

IEEE journal of biomedical and health informatics
The aim of this study is to apply and characterize eXplainable AI (XAI) to assess the quality of synthetic health data generated using a data augmentation algorithm. In this exploratory study, several synthetic datasets are generated using various co...

Machine learning models of healthcare expenditures predicting mortality: A cohort study of spousal bereaved Danish individuals.

PloS one
BACKGROUND: The ability to accurately predict survival in older adults is crucial as it guides clinical decision making. The added value of using health care usage for predicting mortality remains unexplored. The aim of this study was to investigate ...

Interaction between maintenance variables of medical ultrasound scanners through multifactor dimensionality reduction.

Expert review of medical devices
BACKGROUND: Proper maintenance of electro-medical devices is crucial for the quality of care to patients and the economic performance of healthcare organizations. This research aims to identify the interaction between Ultrasound scanners (US) mainten...

Self-Attentive Channel-Connectivity Capsule Network for EEG-Based Driving Fatigue Detection.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Deep neural networks have recently been successfully extended to EEG-based driving fatigue detection. Nevertheless, most existing models fail to reveal the intrinsic inter-channel relations that are known to be beneficial for EEG-based classification...

Identification of Child Survivors of Sex Trafficking From Electronic Health Records: An Artificial Intelligence Guided Approach.

Child maltreatment
Survivors of child sex trafficking (SCST) experience high rates of adverse health outcomes. Amidst the duration of their victimization, survivors regularly seek healthcare yet fail to be identified. This study sought to utilize artificial intelligenc...

Machine learning-based ensemble approach in prediction of lung cancer predisposition using XRCC1 gene polymorphism.

Journal of biomolecular structure & dynamics
The employment of machine learning approaches has shown promising results in predicting cancer. In the current study, polymorphisms data of five single nucleotide polymorphisms (SNPs) of DNA repair gene XRCC1 (XRCC1 399, XRCC1 194, XRCC1 206, XRCC1 6...

Using machine learning to estimate health spillover effects.

The European journal of health economics : HEPAC : health economics in prevention and care
We develop a nonparametric model to study health spillover effects of policy interventions. We use double/debiased machine learning to estimate the model using data from 74 hospitals in Rio de Janeiro, Brazil, and examine cross-patient spillover effe...

Starting a robotic thoracic surgery program: From wedge resection to sleeve lobectomy in six months. Initial conclusions.

Cirugia espanola
INTRODUCTION: Robot-assisted thoracic surgery (RATS) is a rapidly expanding technique. In our study, we aimed to analyze the results of the process to adopt robotic surgery in our Department of Thoracic Surgery.

A semi-supervised learning-based quality evaluation system for digital chest radiographs.

Medical physics
BACKGROUND: Digital radiography is the most commonly utilized medical imaging technique worldwide, and the quality of radiographs plays a crucial role in accurate disease diagnosis. Therefore, evaluating the quality of radiographs is an essential ste...