چاپ صفحه |  صفحه نخست سامانه |  نویسندگان |  اطلاعات تفضیلی |  دانلود مقاله | دانشگاه علوم پزشکی تبریز |
| نویسنده ثبت کننده مقاله | سویل غفارزاده راد |
| مرحله جاری مقاله | تایید نهایی |
| دانشکده/مرکز مربوطه | مرکز تحقیقات غدد درون ریز |
| کد مقاله | 88405 |
| عنوان فارسی مقاله | برنامههای پشتیبانی تصمیمگیری مبتنی بر کامپیوتر و یادگیری ماشین برای پیشگیری و درمان چاقی دوران کودکی |
| عنوان لاتین مقاله | Computer-based decision support applications and machine learning for the prevention and treatment of childhood obesity |
| نوع ارائه | پوستر |
| عنوان کنگره / همایش | 2nd International Congress on Artificial Intelligence |
| نوع کنگره / همایش | بین المللی |
| کشور محل برگزاری کنگره/ همایش | |
| شهر محل برگزاری کنگره/ همایش | تهران |
| سال انتشار/ ارائه شمسی | 1404 |
| سال انتشار/ارائه میلادی | 2025 |
| تاریخ شمسی شروع و خاتمه کنگره/همایش | 1404/02/24 الی 1404/02/26 |
| آدرس لینک مقاله/ همایش در شبکه اینترنت | https://aims.smums.ac.ir/poster_view.php?lang=en&id=210 |
| آدرس علمی (Affiliation) نویسنده متقاضی | Endocrine research center, Tabriz university of medical sciences |
| نویسنده | نفر چندم مقاله |
|---|---|
| سویل غفارزاده راد | اول |
| عنوان | متن |
|---|---|
| کلمات کلیدی | Clinical Decision Support, Machine Learning, Digital Health, Smart Health Interventions. |
| خلاصه مقاله | Background and Aims: Childhood obesity is a growing concern globally, with significant health and societal implications. The increasing prevalence of childhood obesity calls for effective interventions and predictive models to address this issue. This study aims to explore the application of Clinical Decision Support (CDS) systems and Machine Learning (ML) techniques in predicting, diagnosing, and managing childhood obesity. It hypothesizes that integrating CDS with ML algorithms can improve the accuracy and efficiency of obesity care. Method: This study involved a review of recent research on CDS and ML applications in childhood obesity, focusing on interventions using Electronic Health Records (EHRs), mobile apps, and machine learning algorithms like decision trees and artificial neural networks (ANNs). The sample consisted of various studies that employed these technologies to collect data on demographic, clinical, and behavioral factors from both children and their caregivers. The research methods included analyzing the effectiveness of these interventions in predicting obesity and supporting healthcare professionals in adhering to clinical guidelines. Results: The study found that CDS interventions, particularly through EHRs and mobile applications, were effective in supporting self-management and remote medical management of childhood obesity. ML techniques, such as decision trees and ANNs, provided valuable insights into predicting childhood obesity based on various factors, including BMI, physical activity, and diet. However, the review revealed limitations, such as small sample sizes and methodological issues in some studies. Conclusion: The findings suggest that CDS interventions can significantly enhance self-management and remote care for childhood obesity, while ML techniques offer promising tools for prediction and diagnosis. However, the integration of ML algorithms into CDS tools, especially in clinical settings, remains limited. Further research is needed to explore the potential of combining these technologies to develop smarter and more impactful digital health interventions aimed at combating childhood obesity |
| نام فایل | تاریخ درج فایل | اندازه فایل | دانلود |
|---|---|---|---|
| ai and childhood obesity.pdf | 1404/06/23 | 130798 | دانلود |
| puster.png | 1404/06/23 | 242058 | دانلود |
| certificate of ais.jpg | 1404/06/23 | 409712 | دانلود |
