对于PD护理的水晶球:预测模型如何帮助我们看到未来
Keith McCullough1, Lisa Henn1, Dean Tsai2
1Arbor Research Collaborative for Health, Clinical and Epidemiological Studies, Ann Arbor, Michigan, USA.
概括
研究人员为医疗保健开发预测模型,但这些工具往往无法提供新的见解. 一个关键的测试是模型是否提供超越现有临床知识的新信息,确保真正的临床实用性.
科学领域:
- 医疗信息学 医疗信息学
- 临床决策支持 临床决策支持
- 预测分析是一种预测分析.
背景情况:
- 医疗保健团队和患者寻求未来的结果预测.
- 预测模型是为了预测患者的发展轨迹而开发的.
- 目前用于预测模型的验证方法不足.
研究的目的:
- 评估预测模型的临床实用性.
- 为预测模型提出一个额外的验证指标.
- 确保预测模型提供可操作的新型信息.
主要方法:
- 审查现有的预测模型验证技术.
- 一个新的验证标准的概念化,重点是信息的新性.
- 分析统计学意义与临床相关性之间的差距.
主要成果:
- 许多预测模型在统计学上是有效的,但缺乏临床新性.
- 现有的测试证实了模型是否胜过随机机会.
- 有相当数量的模型不提供护理团队不知道的信息.
结论:
- 预测模型必须提供新的见解,才能在临床实践中真正有价值.
- 研究人员应该在统计验证的同时实施"新性测试".
- 这确保了工具真正增强护理团队的决策和患者的理解.
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