自助式智能医疗保健系统的歧视性输入处理方案
Mohamed Medani1, Shtwai Alsubai2, Hong Min3
1Applied College of Mahail Aseer, King Khalid University, Abha 62529, Saudi Arabia.
Bioengineering (Basel, Switzerland)
|July 27, 2024
概括
本研究介绍了用于智能医疗保健的歧视性输入处理方案 (DIPS). 使用转移学习,DIPS提高了医疗保健系统中的情绪分析和数据预测准确度.
科学领域:
- 医疗保健中的人工智能
- 计算情绪分析 计算情绪分析
- 智能医疗保健系统 智能医疗保健系统
背景情况:
- 智能医疗系统依靠情绪分析进行诊断和自我帮助.
- 传统的医疗监测面临着复杂数据模式的可扩展性和可靠性问题.
- 准确的计算模型对于有效的医疗保健系统性能至关重要.
研究的目的:
- 引入歧视性输入处理方案 (DIPS),以应对医疗保健情绪分析方面的挑战.
- 提高智能医疗保健系统数据预测和建议的准确性和效率.
- 利用转移学习来增强数据利用和跨情绪流的模式识别.
主要方法:
- 开发了使用基于数据分割的处理的歧视性输入处理方案 (DIPS).
- 在DIPS中实现了一个推引擎,以分析来自情绪流的细分数据特征.
- 员工转移学习以识别不同情绪分析流中的相似数据模式.
主要成果:
- 通过数据细分,DIPS有效地合并了多个情绪分析流.
- 使用转移学习,DIPS推引擎表现出更高的准确性和灵活性.
- 使用包括数据利用率,近似度,准确度和错误率在内的指标评估有效性.
结论:
- 歧视性输入处理方案 (DIPS) 显著提高了医疗管理的准确性和效率.
- 通过情绪分析,DIPS通过提供准确的数据预测和建议来增强智能医疗系统.
- 在DIPS中转移学习确保有效地利用历史数据属性,以便在未来提出建议.
相关概念视频
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