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在智能智能医疗保健场景中,用于疾病预防数据集构建的人工智能概率方案

B RaviKrishna¹, Mohammed E Seno², Mohan Raparthi³

¹Department of Artificial Intelligence and Data Science, Vignan Institute of Technology & Science, Hyderabad, India.

SLAS technology

|July 21, 2024

概括

本研究介绍了一种新的三阶段人工智能策略,用于生成合成智能医疗数据,解决缺少信息等局限性. 这种方法通过创建强大的数据集来改善分析,从而增强人工智能 (AI) 在智能医疗保健中的应用.

科学领域:

人工智能的人工智能
医疗保健信息学医疗保健信息学
数据科学数据科学数据科学

背景情况:

越来越多的老年人口需要先进的智能医疗保健解决方案.
数字技术和高速互联网使智能医疗保健服务成为可能.
重要的数据挑战,包括缺少数据,噪音和缺乏标准,限制了智能医疗保健中的AI.

研究的目的:

提出基于人工智能的三阶段数据生成策略,以克服智能医疗保健中的数据限制.
创建一个合成数据集,用于训练和评估智能医疗保健应用中的AI模型.
解决缺少数据集的问题,并改善人工智能驱动的医疗保健数据质量.

主要方法:

使用小型智能医疗保健数据集开发了一个基于人工智能的三阶段数据生成过程.
阶段1:基于树的生成策略,考虑基本属性的原始数据分布.
第二阶段:对行为能力评估指标的天真贝叶斯算法.
第三阶段:高水平行为能力标准和指标的多变量线性回归.

主要成果:

生成的数据集被用于六个多重分类和两个多重标签任务.
用各种基于神经网络的培训策略来评估数据集的实用性.
该方法证明了在智能医疗保健下游人工智能任务中创建有价值的合成数据的潜力.

结论:

拟议的人工智能驱动的数据生成策略有效地解决了智能医疗保健中缺失的数据问题.
合成数据集适用于训练人工智能模型,增强智能医疗保健服务开发.
通过实验分析和专家知识整合的验证确保了数据的真实性和实用性.

关键词:

人工智能的人工智能是人工智能.预防疾病预防疾病多重分类是指多重分类.朴素的贝叶斯是一个天真的人.神经网络的神经网络的神经网络智能医疗保健是一个智能医疗保健.

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