报告详情
The Future of Patient Care: Revolutionizing Treatment Plans through Deep Learning and Precision Medicine
编号：73
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更新：2024-08-17 15:15:41 浏览：316次
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摘要
Precision medicine aims to tailor medical treatment to the individual characteristics of each patient, and the integration of deep learning techniques has emerged as a transformative approach in this field. This research paper explores the application of deep learning algorithms in the development of precision medicine strategies, focusing on their ability to analyze complex datasets, including genomic, proteomic, and clinical data. We present a comprehensive framework that utilizes convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to identify biomarkers and predict patient-specific responses to therapies across various diseases, including cancer and cardiovascular disorders. Our findings demonstrate that deep learning models significantly enhance the accuracy of disease prediction and treatment personalization compared to traditional methods. Additionally, we discuss the challenges associated with data heterogeneity, model interpretability, and ethical considerations in deploying these technologies in clinical settings. Through a series of case studies, we illustrate the potential of deep learning to revolutionize patient care by enabling more effective and individualized treatment plans. This research underscores the importance of interdisciplinary collaboration in advancing precision medicine and highlights future directions for integrating artificial intelligence into healthcare systems.
关键词
Deep Learning,Precision Medicine,Health Care,Artificial Intelligence
报告人
Riyaz Ahmad
Researcher Southwest Jiaotong University稿件作者
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重要日期
-
会议日期
10月24日
2024
至10月27日
2024
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10月14日 2024
初稿截稿日期
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10月29日 2024
注册截止日期
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10月31日 2024
报告提交截止日期
主办单位
国际科学联合会
IEEE泰国分会
IEEE计算机学会泰国分会
IEEE泰国分会
IEEE计算机学会泰国分会
联系方式
- Miss AsianComNet
- as******@usssociety.org
历届会议
-
2025年12月19日 约旦 Zarqa
2025亚洲通信与网络会议
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