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Automated Machine Learning versus Expert-Designed Models in Ocular Toxoplasmosis: Detection and Lesion Localization Using Fundus Images.

Ocular immunology and inflammation
PURPOSE: Automated machine learning (AutoML) allows clinicians without coding experience to build their own deep learning (DL) models. This study assesses the performance of AutoML in detecting and localizing ocular toxoplasmosis (OT) lesions in fund...

Sleep-phasic heart rate variability predicts stress severity: Building a machine learning-based stress prediction model.

Stress and health : journal of the International Society for the Investigation of Stress
We propose a novel approach for predicting stress severity by measuring sleep phasic heart rate variability (HRV) using a smart device. This device can potentially be applied for stress self-screening in large populations. Using a Holter electrocardi...

Reassessing acquired neonatal intestinal diseases using unsupervised machine learning.

Pediatric research
BACKGROUND: Acquired neonatal intestinal diseases have an array of overlapping presentations and are often labeled under the dichotomous classification of necrotizing enterocolitis (which is poorly defined) or spontaneous intestinal perforation, hind...

Patient portal messages to support an age-friendly health system for persons with dementia.

Journal of the American Geriatrics Society
BACKGROUND: Patient portal secure messaging can support age-friendly dementia care, yet little is known about care partner use of the portal and how message concerns relate to age-friendly issues.

Exploring subtypes of multiple sclerosis through unsupervised machine learning of automated fiber quantification.

Japanese journal of radiology
PURPOSE: This study aimed to subtype multiple sclerosis (MS) patients using unsupervised machine learning on white matter (WM) fiber tracts and investigate the implications for cognitive function and disability outcomes.

Development of a machine learning model for identifying the optimal situation favoring double-level osteotomy over single-level high tibial osteotomy.

The Knee
BACKGROUND: This study aimed to develop a machine learning (ML) model to identify the optimal situation wherein double-level osteotomy (DLO) is favored for severe varus knees by analyzing unfavorable outcomes. This study hypothesized that there are t...

GADNN: a revolutionary hybrid deep learning neural network for age and sex determination utilizing cone beam computed tomography images of maxillary and frontal sinuses.

BMC medical research methodology
INTRODUCTION: The determination of identity factors such as age and sex has gained significance in both criminal and civil cases. Paranasal sinuses like frontal and maxillary sinuses, are resistant to trauma and can aid profiling. We developed a deep...

Knowledge, Perceptions and Attitude of Researchers Towards Using ChatGPT in Research.

Journal of medical systems
INTRODUCTION: ChatGPT, a recently released chatbot from OpenAI, has found applications in various aspects of life, including academic research. This study investigated the knowledge, perceptions, and attitudes of researchers towards using ChatGPT and...

Development of a deep learning model that predicts critical events of pediatric patients admitted to general wards.

Scientific reports
Early detection of deteriorating patients is important to prevent life-threatening events and improve clinical outcomes. Efforts have been made to detect or prevent major events such as cardiopulmonary resuscitation, but previously developed tools ar...

Effects of Various Heavy Metal Exposures on Insulin Resistance in Non-diabetic Populations: Interpretability Analysis from Machine Learning Modeling Perspective.

Biological trace element research
Increasing and compelling evidence has been proved that heavy metal exposure is involved in the development of insulin resistance (IR). We trained an interpretable predictive machine learning (ML) model for IR in the non-diabetic populations based on...