医院诊床比影响因素的大数据挖掘  

The Big Date Mining of Hospital Outpatients and Beds Ratio Factors

作　　者：居发礼[1,2] 付祥钊[1] 

机构地区：[1]重庆大学,重庆400045 [2]重庆海润节能研究院,重庆401121

出　　处：《制冷与空调（四川）》2015年第5期485-490,共6页Refrigeration and Air Conditioning

基　　金：国家自然科学基金(NO.51278506)

摘　　要：提出了"诊床比"作为医院暖通空调设计和运行非常重要的基础工程参数,形成了"先挖掘分析因素,再于其中挖掘影响因素"的两级影响因素大数据挖掘方法。利用该方法挖掘得到年平均诊床比显著性影响因素9个,可作为年平均诊床比拟合公式的参数;得到逐日诊床比显著性影响因素2个,可作为逐日诊床比预测的修正影响因素。Proposed the ratio of outpatiems and beds as a very important basic project parameter for hospital HVAC design and operation, and form the big data mining methods of ＂first analysis of the factors, then tap the factors in which＂, then get nine significant factors as a fitting formula factors of the average outpatients and bed ratio, get two significant factors of daily outpatients and beds ratio, which can be used as predicted correction factors.

关 键 词：诊床比 大数据 影响因素 相关性 

分 类 号：TU831.3[建筑科学—供热、供燃气、通风及空调工程]