革新乳腺癌护理:人工智能增强诊断和患者病史
Maleeha Fathima1, Mohammed Moulana1
1Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India.
Computer methods in biomechanics and biomedical engineering
|January 5, 2024
概括
这项研究使用人工智能 (AI) 和机器学习 (ML) 来改善乳腺癌诊断和自动化病史收集. 这些人工智能驱动的方法提高了诊断准确性和患者数据管理,以获得更好的医疗保健.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 诊断乳腺癌需要高度准确和高效的病史记录.
- 目前的方法可能耗时,可能缺乏最佳的诊断精度.
- 在乳腺癌护理中需要集成的AI和ML解决方案.
研究的目的:
- 开发和评估基于AI和ML的乳腺癌诊断方法.
- 创建一个人工智能驱动的系统,以简化和动态的患者病史收集.
- 提高疾病预测的准确性和医疗保健文档的效率.
主要方法:
- 数据预处理和特征提取用于乳腺癌数据集.
- 机器学习算法的应用,包括支持矢量机 (SVM),K-近邻 (KNN) 和模糊逻辑.
- 集成深度学习模型和人工智能用于动态的患者病史记录和报告生成 (GPT-3.5).
主要成果:
- 模糊逻辑在诊断过程中处理不确定的数据时表现出高准确度.
- 深度学习模型提高了预测准确度,显示了与传统的ML方法的协同作用.
- 人工智能驱动的病史记录简化了这一过程,自动化报告提供了结构化的患者数据.
结论:
- 拟议的AI和ML方法大大提高了乳腺癌诊断的准确性.
- 自动化病史收集简化了文档,并支持明智的医疗保健决策.
- 人工智能和机器学习方法对改善医疗评估和患者护理有很大的前景.
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