目的研究按疾病诊断相关分组(diagnosis related groups,DRGs)付费实施对血液病患者住院费用的影响,为医院进行医保精细化管理提供决策依据。方法从天津市某三甲医院医院信息系统(hospital information system,HIS)中选取2021年10月—2...目的研究按疾病诊断相关分组(diagnosis related groups,DRGs)付费实施对血液病患者住院费用的影响,为医院进行医保精细化管理提供决策依据。方法从天津市某三甲医院医院信息系统(hospital information system,HIS)中选取2021年10月—2022年3月和2022年10月—2023年3月共21448例患者的病历资料,经过倾向得分匹配法进行匹配,筛选出8134例血液病患者,再利用双重差分法分析DRG付费对血液病患者住院费用的影响。结果实施DRGs付费后,血液病患者的检查费的中位数从6306.50元降至5038.50元,自付费用中位数从7607.17元降至6562.08元,材料费的中位数从305.70元降至286.88元。本研究进一步通过DID分析消除潜在混杂因素的影响,发现自付费用仍下降(P<0.05)。结论DRGs控费初见成效,有效降低了血液病患者的个人负担,医院应持续关注DRGs的长期效果。展开更多
In this paper, we present a simple but powerful ensemble for robust texture classification. The proposed method uses a single type of feature descriptor, i.e. scale-invariant feature transform (SIFT), and inherits t...In this paper, we present a simple but powerful ensemble for robust texture classification. The proposed method uses a single type of feature descriptor, i.e. scale-invariant feature transform (SIFT), and inherits the spirit of the spatial pyramid matching model (SPM). In a flexible way of partitioning the original texture images, our approach can produce sufficient informative local features and thereby form a reliable feature pond or train a new class-specific dictionary. To take full advantage of this feature pond, we develop a group-collaboratively representation-based strategy (GCRS) for the final classification. It is solved by the well-known group lasso. But we go beyond of this and propose a locality-constraint method to speed up this, named local constraint-GCRS (LC-GCRS). Experimental results on three public texture datasets demonstrate the proposed approach achieves competitive outcomes and even outperforms the state-of-the-art methods. Particularly, most of methods cannot work well when only a few samples of each category are available for training, but our approach still achieves very high classification accuracy, e.g. an average accuracy of 92.1% for the Brodatz dataset when only one image is used for training, significantly higher than any other methods.展开更多
文摘目的研究按疾病诊断相关分组(diagnosis related groups,DRGs)付费实施对血液病患者住院费用的影响,为医院进行医保精细化管理提供决策依据。方法从天津市某三甲医院医院信息系统(hospital information system,HIS)中选取2021年10月—2022年3月和2022年10月—2023年3月共21448例患者的病历资料,经过倾向得分匹配法进行匹配,筛选出8134例血液病患者,再利用双重差分法分析DRG付费对血液病患者住院费用的影响。结果实施DRGs付费后,血液病患者的检查费的中位数从6306.50元降至5038.50元,自付费用中位数从7607.17元降至6562.08元,材料费的中位数从305.70元降至286.88元。本研究进一步通过DID分析消除潜在混杂因素的影响,发现自付费用仍下降(P<0.05)。结论DRGs控费初见成效,有效降低了血液病患者的个人负担,医院应持续关注DRGs的长期效果。
文摘In this paper, we present a simple but powerful ensemble for robust texture classification. The proposed method uses a single type of feature descriptor, i.e. scale-invariant feature transform (SIFT), and inherits the spirit of the spatial pyramid matching model (SPM). In a flexible way of partitioning the original texture images, our approach can produce sufficient informative local features and thereby form a reliable feature pond or train a new class-specific dictionary. To take full advantage of this feature pond, we develop a group-collaboratively representation-based strategy (GCRS) for the final classification. It is solved by the well-known group lasso. But we go beyond of this and propose a locality-constraint method to speed up this, named local constraint-GCRS (LC-GCRS). Experimental results on three public texture datasets demonstrate the proposed approach achieves competitive outcomes and even outperforms the state-of-the-art methods. Particularly, most of methods cannot work well when only a few samples of each category are available for training, but our approach still achieves very high classification accuracy, e.g. an average accuracy of 92.1% for the Brodatz dataset when only one image is used for training, significantly higher than any other methods.