使用上下文患者分类系统对COVID-19医院记录的数据分析
Vrushabh Gada1, Madhura Shegaonkar1, Madhura Inamdar1
1K. J. Somaiya College of Engineering, Mumbai, Maharashtra 400077 India.
概括
一个新的上下文患者分类系统在分析2019年冠状病毒疾病 (COVID-19) 数据时实现了97.4%的准确性. 该系统通过分析患者数据和结果,有助于更好地为未来的流行病浪潮做好准备.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 流行病学 流行病学
背景情况:
- 冠状病毒疾病2019 (COVID-19) 流行病严重影响了全球卫生系统.
- 尽管医学取得了进步,但病毒的快速传播需要改善患者管理的数据分析.
研究的目的:
- 开发和评估用于分析COVID-19患者数据的上下文患者分类系统.
- 分析COVID-19和非COVID-19患者数据,包括治疗方法,症状和人口统计数据.
主要方法:
- 利用Knuth-Morris-Pratt算法进行上下文患者分类.
- 分析了一家研究医院的出院总结数据.
- 检查的因素包括药物,医疗服务,测试,脉,温度,年龄和性别.
主要成果:
- 在上下文患者分类系统中获得了97.4%的分类准确度.
- 研究了COVID-19阳性患者的死亡与生存率.
- 分析了各种因素对患者结果的影响.
结论:
- 开发的系统提供了一个强大的方法来分类在流行病期间的患者.
- 将数据分析与上下文分类相结合,可以提高对未来COVID-19等健康危机的准备力.
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