AIMC Topic: Cross-Sectional Studies

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Identifying key influencers of patient satisfaction using an explainable machine learning approach.

Scientific reports
Patient satisfaction is a crucial measure of healthcare quality, influencing both health outcomes and care experiences. This study aims to identify the factors influencing patient satisfaction in healthcare facilities using machine learning algorithm...

Comparison of the completed and discontinued pediatric drug clinical trials in Mainland China: a cross-sectional analysis based on the data from 2003 to 2023.

BMC pediatrics
BACKGROUND: Pediatric drug clinical trials are essential for ensuring the accessibility and safety of medications intended for children. In recent years, the Chinese government has implemented various measures to foster the development of pediatric d...

Drivers of herpes zoster vaccine hesitancy in adults aged 50 and above: A machine learning approach.

Vaccine
BACKGROUND: Herpes zoster (HZ) poses a growing public health challenge among adults aged 50 and above, with vaccine hesitancy being a major barrier to improving immunization rates. Understanding the factors driving HZ vaccine hesitancy is essential f...

Investigating the role of depression in obstructive sleep apnea and predicting risk factors for OSA in depressed patients: machine learning-assisted evidence from NHANES.

BMC psychiatry
OBJECTIVE: The relationship between depression and obstructive sleep apnea (OSA) remains controversial. Therefore, this study aims to explore their association and utilize machine learning models to predict OSA among individuals with depression withi...

Can ChatGPT be trusted? Evaluating AI responses to oral health questions among pregnant Arabic-speaking women.

BMC oral health
BACKGROUND: ChatGPT, an artificial intelligence (AI) chatbot developed by OpenAI, is increasingly being used in healthcare, including dentistry, for patient education; this study aimed to assess the usability and quality of ChatGPT's responses to pre...

Development and validation of a model for predicting depression risk in primary palmar hyperhidrosis: a cross-sectional retrospective observational study.

BMJ open
OBJECTIVE: Primary palmar hyperhidrosis (PPH), characterised by excessive palm sweating, significantly impacts patients' physiology, psychology, self-esteem, work, life and social interactions. The incidence of depression is higher among PPH patients...

Performance of several large language models when answering common patient questions about type 1 diabetes in children: accuracy, comprehensibility and practicality.

BMC pediatrics
BACKGROUND: The use of large language models (LLMs) in healthcare has expanded significantly with advances in natural language processing. Models, such as ChatGPT and Google Gemini, are increasingly used to generate human-like responses to questions,...

SHAP-enhanced machine learning identifies modifiable obesity predictors across adolescent weight groups: A 2021 YRBSS analysis.

PloS one
BACKGROUND: The growing prevalence of obesity in adolescents around the world poses a major threat to public health. This research uses machine learning models to examine the main causes of obesity, in contrast to standard information that typically ...

Accuracy of artificial intelligence in orthodontic extraction treatment planning: a systematic review and meta analysis.

BMC oral health
BACKGROUND: This study aimed to evaluate the diagnostic accuracy of artificial intelligence (AI) models in predicting dental extractions during orthodontic treatment planning.

Attitudes and perceptions of dental students towards artificial intelligence.

BMC medical education
BACKGROUND: Artificial intelligence (AI) is rapidly transforming healthcare, including dentistry, through its applications in diagnosis, prosthetic planning, and oral disease detection. As future professionals, dental students play a vital role in in...