AIMC Topic: Adult

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A recurrence model for non-puerperal mastitis patients based on machine learning.

PloS one
OBJECTIVE: Non-puerperal mastitis (NPM) is an inflammatory breast disease affecting women during non-lactation periods, and it is prone to relapse after being cured. Accurate prediction of its recurrence is crucial for personalized adjuvant therapy, ...

A comparison of the persuasiveness of human and ChatGPT generated pro-vaccine messages for HPV.

Frontiers in public health
INTRODUCTION: Public health messaging is crucial for promoting beneficial health outcomes, and the latest advancements in artificial intelligence offer new opportunities in this field. This study aimed to evaluate the effectiveness of ChatGPT-4 in ge...

Clinical evaluation of a multiplex droplet digital PCR for diagnosing suspected bloodstream infections: a prospective study.

Frontiers in cellular and infection microbiology
BACKGROUND: Though droplet digital PCR (ddPCR) has emerged as a promising tool for early pathogen detection in bloodstream infections (BSIs), more studies are needed to support its clinical application widely due to different ddPCR platforms with dis...

Single-cell microbiota phenotyping reveals distinct disease and therapy-associated signatures in Crohn's disease.

Gut microbes
IgA-coated fractions of the intestinal microbiota of Crohn's disease (CD) patients have been shown to contain taxa that hallmark the compositional dysbiosis in CD microbiomes. However, the correlation between other cellular properties of intestinal b...

Host-microbe multi-omics and succinotype profiling have prognostic value for future relapse in patients with inflammatory bowel disease.

Gut microbes
Crohn's disease (CD) and ulcerative colitis (UC) are chronic relapsing inflammatory bowel disorders (IBD), the pathogenesis of which is uncertain but includes genetic susceptibility factors, immune-mediated tissue injury and environmental influences,...

A machine learning-based analysis for the effectiveness of online teaching and learning in Pakistan during COVID-19 lockdown.

Work (Reading, Mass.)
BackgroundThe COVID-19 pandemic has significantly disrupted daily life and education, prompting institutions to adopt online teaching.ObjectiveThis study delves into the effectiveness of these methods during the lockdown in Pakistan, employing machin...

Multistage deep learning for classification of Helicobacter pylori infection status using endoscopic images.

Journal of gastroenterology
BACKGROUND: The automated classification of Helicobacter pylori infection status is gaining attention, distinguishing among uninfected (no history of H. pylori infection), current infection, and post-eradication. However, this classification has rela...

Evaluating a clinically available artificial intelligence model for intracranial aneurysm detection: a multi-reader study and algorithmic audit.

Neuroradiology
PURPOSE: We aimed to validate a clinically available artificial intelligence (AI) model to assist general radiologists in the detection of intracranial aneurysm (IA) in a multi-reader multi-case (MRMC) study, and to explore its performance in routine...

Novel Machine-Learning Modeling of Facial Trauma Volume With Regional Event and Weather Data.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: Facial trauma volume is difficult to predict accurately. We aim to understand the capacity of climate and regional events to predict daily facial trauma volume. This can provide epidemiologic understanding and subsequently tailor workforce...

Predicting the risk of a high proportion of three/multiple pronuclei (3PN/MPN) zygotes in individual IVF cycles using comparative machine learning algorithms.

European journal of obstetrics, gynecology, and reproductive biology
BACKGROUND: The majority of machine learning applications in assisted reproduction have been focused on predicting the likelihood of pregnancy. In the present study, we aim to investigate which machine learning models are most effective in predicting...