医疗保健提供者使用大数据分析的临床支持系统
K Arunmozhi Arasan1, E Ramaraj1, A Padmapriya1
1Department of Computer Science, Alagappa University, Karaikudi, Tamil Nadu, India.
Journal of evaluation in clinical practice
|February 17, 2025
概括
该k-means白集群模型 (KTCM) 显著改善了医疗保健数据分析的疾病预测. 这种新的方法提高了准确性和效率,有助于临床决策.
科学领域:
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
- 计算生物学 计算生物学
背景情况:
- 医疗保健行业面临着快速增长的数据量和需要高效的分析方法的挑战.
- 传统的健康数据分析平台在优化疾病预测和临床决策流程方面扎.
研究的目的:
- 引入k-means白聚类模型 (KTCM) 作为医疗数据分析的新型优化方法.
- 在临床环境中提高疾病预测准确性和效率.
主要方法:
- 该KTCM模型整合了图形缩小用于数据预处理,并使用挖掘算法进行分析.
- 在标准化预测方法上培训医疗保健专业人员,完善疾病预测.
- 绩效评估使用了统计指标,如R2,REMS,MSE,MAE和MAPE.
主要成果:
- KTCM模型实现了99.7%的优异R2值,超过了现有方法.
- 先进的集群和优化技术在疾病预测中显示出更高的准确性和效率.
结论:
- 通过优化疾病预测,KTCM方法显著提高了医疗数据分析和临床决策支持.
- 该模型的高精度和参数优化验证了它的有效性.
- 未来的研究可能将重点放在算法改进和实时实现上.
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