报告详情
The Role of Intelligent Computing in Medical and Genomic Healthcare Innovations
编号：173
访问权限：仅限参会人
更新：2025-12-23 13:37:44 浏览：110次
拓展类型2
报告开始：2025年12月30日 15:15（Asia/Amman）
报告时间：15min
所在会场：[S7] Track 7: Pattern Recognition, Computer Vision and Image Processing [S7-2] Track 7: Pattern Recognition, Computer Vision and Image Processing
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摘要
Intelligent computing has revolutionized the landscape of medical and genomic healthcare by enabling data-driven innovations that enhance diagnostic accuracy and treatment efficacy. With the advent of AI technologies, healthcare is shifting towards more personalized, predictive, and precise medical interventions. However, traditional treatment planning approaches often fail to account for the complexities of genomic variability and individual health profiles, leading to suboptimal outcomes and generalized therapeutic strategies. These methods lack adaptability, are heavily rule-based, and do not efficiently utilize the vast biomedical data available. To address these challenges, we propose a novel framework Personalized Treatment Planning using Deep Learning Algorithm (PTP-DLA). This method integrates patient-specific clinical and genomic data through advanced deep learning architectures, including Convolutional Neural Networks (CNNs) for feature extraction and Recurrent Neural Networks (RNNs) for temporal pattern recognition. The framework employs data fusion techniques and attention mechanisms to dynamically tailor treatment plans that are both adaptive and individualized. The PTP-DLA system is designed to support clinicians by recommending optimized treatment regimens based on predicted outcomes, patient history, and genomic markers. It can be applied across various medical conditions, including oncology, rare genetic disorders, and chronic diseases, thereby enhancing decision-making in complex clinical scenarios. Experimental evaluations indicate that PTP-DLA significantly outperforms existing models in terms of treatment outcome prediction accuracy, patient-specific adaptability, and overall computational efficiency. These findings suggest that the proposed method holds substantial promise in bridging the gap between genomic data analysis and actionable, personalized healthcare delivery.
关键词
Intelligent Computing, Personalized Treatment Planning, Deep Learning Algorithm, Genomic Healthcare, Precision Medicine, Cnn, Rnn, Data Fusion, Clinical Decision Support, Medical Ai.
报告人
Prof Anil Srivastava
Professor Assistant Professor； Savitribai Phule Pune University稿件作者
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重要日期
-
会议日期
12月29日
2025
至12月31日
2025
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12月30日 2025
报告提交截止日期
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02月10日 2026
初稿截稿日期
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02月10日 2026
注册截止日期
主办单位
国际科学联合会
承办单位
扎尔卡大学
联系方式
- Miss AsianComNet
- as******@usssociety.org
历届会议
-
2024年10月24日 泰国 Bangkok
2024亚洲通讯与网络会议
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