加强基层中医药服务背景下居民首诊医疗机构影响因素分析

目的 通过研究北京市石景山区居民首诊医疗机构的影响因素,为推动分级诊疗提供理论依据。 方法 于2016年3-5月,在北京市石景山区采用分层随机整群抽样法入户进行面对面问卷调查。采用多水平多分类logistic模型分析居民选择首诊医疗机构的影响因素。 结果 共调查1122名居民,24.6%,56.2%%和12.5%的居民一般情况下分别选择区内社区医疗机构、区内综合医院和区外综合医院首诊。多水平多分类logistic模型结果显示,性别、文化程度、离家最近的医疗机构类型以及是否清楚区内社区医疗机构中医药服务情况是居民选择区内社区医疗机构或区内综合医院首诊的影响因素;文化程度、离家最近的医疗机构类型、过去一年家庭人均总支出、是否清楚区内社区医疗机构中医药服务情况是居民选择区内社区医疗机构或区外综合医院首诊的影响因素。 结论 应继续提高石景山区社区医疗机构的诊疗水平及中医药服务能力,同时增进居民对社区中医药服务的认知度,从而推动分级诊疗的实现。     

Evaluating resampling methods and structured features to improve fall incident report identification by the severity level

ObjectiveThis study aims to improve the classification of the fall incident severity level by considering data imbalance issues and structured features through machine learning.Materials and MethodsWe present an incident report classification (IRC) framework to classify the in-hospital fall incident severity level by addressing the imbalanced class problem and incorporating structured attributes. After text preprocessing, bag-of-words features, structured text features, and structured clinical features were extracted from the reports. Next, resampling techniques were incorporated into the training process. Machine learning algorithms were used to build classification models. IRC systems were trained, validated, and tested using a repeated and randomly stratified shuffle-split cross-validation method. Finally, we evaluated the system performance using the F1-measure, precision, and recall over 15 stratified test sets.ResultsThe experimental results demonstrated that the classification system setting considering both data imbalance issues and structured features outperformed the other system settings (with a mean macro-averaged F1-measure of 0.733). Considering the structured features and resampling techniques, this classification system setting significantly improved the mean F1-measure for the rare class by 30.88% (P value < .001) and the mean macro-averaged F1-measure by 8.26% from the baseline system setting (P value < .001). In general, the classification system employing the random forest algorithm and random oversampling method outperformed the others.ConclusionsStructured features provide essential information for categorizing the fall incident severity level. Resampling methods help rebalance the class distribution of the original incident report data, which improves the performance of machine learning models. The IRC framework presented in this study effectively automates the identification of fall incident reports by the severity level.     

基于深度森林的产前胎儿监护不平衡多分类判别

采用深度森林框架构建基于不平衡电子胎心宫缩监护数据的多分类判别模型,验证模型有效性,结果表明该模型预测性能较好,极大降低误判率,在产前胎儿健康状况智能评估中有良好应用前景。     

基于组合学习算法的肺栓塞诊断模型

CT血管造影在肺栓塞临床诊断中有着广泛的应用,然而血管造影大都包含上百张肺部切片,人工筛选这些切片效率低下出错率较高。尝试通过组合学习算法建立自动识别模型来有效提高识别能力,识别模型由三部分构成：数据的不平衡处理,变量选择方法和组合学习模型。通过比较不同不平衡数据处理策略和变量选择方法的基础上,选择Adaboost方法进行分类算法的学习,临床数据的结果表明该方法能较好地辅助实际诊断。     

不平衡氨基酸抑制肝癌增殖的动物实验

近年来 ,通过改变氨基酸成分或含量的“不平衡氨基酸”的抗癌疗法日益受到重视 [1]。但是目前国外研究大多仍停留在实验阶段 ,虽有部分不平衡氨基酸药物应用于临床 ,但其疗效尚难肯定 ,而国内则未见报道。我们在研究肝癌体内、体外氨基酸代谢特点的基础上 ,参考国外相关报道 ,配制出不平衡氨基酸药物 ,用于荷人肝癌模型裸小鼠 ,对其抑制肝癌效果进行了观察。1　材　料　与　方　法1 .1　动物与肿瘤　　生后 4w的 BALB/C裸小鼠 ,雄性 ,2 2只 ,体重( 1 3.70± 0 .90 ) g由中国医学科学院实验动物中心提供。随机分为 5组 :A组 ( 1 8- F) ,4…     

加权Fisher线性判别法在非平衡医学数据集中的应用

目的:介绍加权Fisher线性判别法在非平衡医学数据集中的应用。方法:在两类分类问题中,当两类样本的协方差矩阵不同时,样本不平衡会导致Fisher线性判别的性能下降,使用加权Fisher线性判别法对两类样本同时进行不同倍数的过抽样,可促使两类的样本数目趋向平衡。结果:利用社区居民的血糖流行病学调查资料进行验证,加权Fisher线性判别法较传统Fisher线性判别法的灵敏度高,分类性能明显提高。结论:加权Fisher线性判别法可适用于非平衡数据集,算法简单高效,且基本不增加计算复杂度。