目的分析上海市某三级甲等公立医院各科室床位利用效率,为评估床位资源配置合理性提供方法学依据。方法以上海市某三级甲等医院2023年的医疗运营数据为基础,利用床位利用模型进行可视化呈现,评价床位资源的利用效率。运用床位评价指标...目的分析上海市某三级甲等公立医院各科室床位利用效率,为评估床位资源配置合理性提供方法学依据。方法以上海市某三级甲等医院2023年的医疗运营数据为基础,利用床位利用模型进行可视化呈现,评价床位资源的利用效率。运用床位评价指标测算各科室床位的合理区间,得出床位调整方案。采用多层感知器神经网络模型评估床位调整方案的准确性、合理性、可行性。结果床位利用模型显示,11个(25.00%)科室属于床位效率型,11个(25.00%)科室属于床位周转型,16个(36.36%)科室属于床位闲置型,6个(13.64%)科室属于压床型。床位评价指标显示,8个科室床位数不需改变,16个科室床位数需要适当减少,20个科室床位数需要结合实际情况增加。利用多层感知器神经网络搭建床位不变、床位减少、床位增加模型。床位不变模型的受试者工作特征曲线下面积(area under curve,AUC)=0.719,灵敏度为100.00%,特异度为40.63%。床位减少模型的AUC=0.875,灵敏度为83.33%,特异度为85.00%。床位增加模型的AUC=0.913,灵敏度为100.00%,特异度为72.22%。结论医院整体床位利用效率较低且不同科室间床位的利用效率存在差异,通过多层感知器神经网络建立的床位增加模型评估结果与床位利用模型和床位评价指标的结果具有较好的一致性,能够为医院床位资源配置管理提供方法学依据,进而实现医院床位精细化管理。展开更多
The technique of lossless image compression plays an important role in image transmission and storage for high quality. At present, both the compression ratio and processing speed should be considered in a real-time m...The technique of lossless image compression plays an important role in image transmission and storage for high quality. At present, both the compression ratio and processing speed should be considered in a real-time multimedia system. A novel lossless compression algorithm is researched. A low complexity predictive model is proposed using the correlation of pixels and color components. In the meantime, perceptron in neural network is used to rectify the prediction values adaptively. It makes the prediction residuals smaller and in a small dynamic scope. Also a color space transform is used and good decorrelation is obtained in our algorithm. The compared experimental results have shown that our algorithm has a noticeably better performance than traditional algorithms. Compared to the new standard JPEG-LS, this predictive model reduces its computational complexity. And its speed is faster than the JPEG-LS with negligible performance sacrifice.展开更多
文摘目的分析上海市某三级甲等公立医院各科室床位利用效率,为评估床位资源配置合理性提供方法学依据。方法以上海市某三级甲等医院2023年的医疗运营数据为基础,利用床位利用模型进行可视化呈现,评价床位资源的利用效率。运用床位评价指标测算各科室床位的合理区间,得出床位调整方案。采用多层感知器神经网络模型评估床位调整方案的准确性、合理性、可行性。结果床位利用模型显示,11个(25.00%)科室属于床位效率型,11个(25.00%)科室属于床位周转型,16个(36.36%)科室属于床位闲置型,6个(13.64%)科室属于压床型。床位评价指标显示,8个科室床位数不需改变,16个科室床位数需要适当减少,20个科室床位数需要结合实际情况增加。利用多层感知器神经网络搭建床位不变、床位减少、床位增加模型。床位不变模型的受试者工作特征曲线下面积(area under curve,AUC)=0.719,灵敏度为100.00%,特异度为40.63%。床位减少模型的AUC=0.875,灵敏度为83.33%,特异度为85.00%。床位增加模型的AUC=0.913,灵敏度为100.00%,特异度为72.22%。结论医院整体床位利用效率较低且不同科室间床位的利用效率存在差异,通过多层感知器神经网络建立的床位增加模型评估结果与床位利用模型和床位评价指标的结果具有较好的一致性,能够为医院床位资源配置管理提供方法学依据,进而实现医院床位精细化管理。
基金This project was supported by the National Natural Science Foundation of China (60172045).
文摘The technique of lossless image compression plays an important role in image transmission and storage for high quality. At present, both the compression ratio and processing speed should be considered in a real-time multimedia system. A novel lossless compression algorithm is researched. A low complexity predictive model is proposed using the correlation of pixels and color components. In the meantime, perceptron in neural network is used to rectify the prediction values adaptively. It makes the prediction residuals smaller and in a small dynamic scope. Also a color space transform is used and good decorrelation is obtained in our algorithm. The compared experimental results have shown that our algorithm has a noticeably better performance than traditional algorithms. Compared to the new standard JPEG-LS, this predictive model reduces its computational complexity. And its speed is faster than the JPEG-LS with negligible performance sacrifice.
