Use of secondary data to estimate instantaneous model parameters of diabetic heart disease: Lemonade Method

With the increasing burden of chronic diseases on the health care system, Markov-type models are becoming popular to predict the long-term outcomes of early intervention and to guide disease management. However, statisticians have not been actively involved in the development of these models. Typically, the models are developed by using secondary data analysis to find a single “best” study to estimate each transition in the model. However, due to the nature of secondary data analysis, there frequently are discrepancies between the theoretical model and the design of the studies being used. This paper illustrates a likelihood approach to correctly model the design of clinical studies under the conditions where (1) the theoretical model may include an instantaneous state of distinct interest to the researchers and (2) the study design may be such that study data cannot be used to estimate a single parameter in the theoretical model of interest. For example, a study may ignore intermediary stages of disease. Using our approach, not only can we accommodate the two conditions above, but more than one study may be used to estimate model parameters. In the spirit of “If life gives you lemon, make lemonade”, we call this method “Lemonade Method”. Simulation studies are carried out to evaluate the finite sample property of this method. In addition, the method is demonstrated through application to a model of heart disease in diabetes.     

An analysis model of diagnosis and treatment for COVID-19 pandemic based on medical information fusion

Exploring the complicated relationships underlying the clinical information is essential for the diagnosis and treatment of the Coronavirus Disease 2019 (COVID-19). Currently, few approaches are mature enough to show operational impact. Based on electronic medical records (EMRs) of 570 COVID-19 inpatients, we proposed an analysis model of diagnosis and treatment for COVID-19 based on the machine learning algorithms and complex networks. Introducing the medical information fusion, we constructed the heterogeneous information network to discover the complex relationships among the syndromes, symptoms, and medicines. We generated the numerical symptom (medicine) embeddings and divided them into seven communities (syndromes) using the combination of Skip-Gram model and Spectral Clustering (SC) algorithm. After analyzing the symptoms and medicine networks, we identified the key factors using six evaluation metrics of node centrality. The experimental results indicate that the proposed analysis model is capable of discovering the critical symptoms and symptom distribution for diagnosis; the key medicines and medicine combinations for treatment. Based on the latest COVID-19 clinical guidelines, this model could result in the higher accuracy results than the other representative clustering algorithms. Furthermore, the proposed model is able to provide tremendously valuable guidance and help the physicians to combat the COVID-19.     

64排CT在糖尿病下肢动脉硬化闭塞症中的应用

目的:探讨64排CT在糖尿病下肢动脉硬化闭塞症中的应用.方法:选择我院收治的糖尿病下肢动脉硬化闭塞症患者86例,先进行64排CT扫描(CTA),后再进行数字减影检查(DSA),比较两种方法诊断的准确性和血管病变检查情况.结果:CTA对下肢血管病变准确范围、部位等显示清晰.其检测的敏感性显著高于DSA,P<0.05.且前...     

福建甘蔗宿根矮化病的发生及其诊断

甘蔗宿根矮化病在福建蔗区已普遍存在，发病与品种、土壤、水分和宿根性有关。诊断试验表明，用相衬显微镜检查蔗茎或叶片抽出液的方法系一种较简单可靠的诊断方法。     

腰椎重力线在腰椎间盘病变患者中的变化分析及腰腿痛影像的相关性研究

目的:探讨腰椎重力线在腰椎间盘病变患者中的变化分析及腰腿痛影像的相关性.方法:选择2018年4月-2020年1月腰椎间盘病变及腰腿痛患者224例为研究对象,所有患者入院后均进行腰椎正侧位DR检查及腰椎CT或MRI检查,完成患者腰椎重力线测定;采用视觉模拟疼痛(VAS)量表对患者疼痛进行评估;采用SPSSPearson相...     

Combination of salicylic acid and ultrasound to control postharvest blue mold caused by Penicillium expansum in peach fruit

The effect of ultrasound (40 kHz, 10 min) and salicylic acid (SA, 0.05 mM) either separately, or combined on blue mold caused by Penicillium expansum in peach fruit was investigated. The results showed that the application of SA alone could reduce blue mold, while the use of ultrasound had no effect. Our results also revealed that SA combined with ultrasound treatment was more effective in inhibiting fungal decay during storage than the SA treatment alone. The combined treatment increased the activities of defense enzymes such as chitinase, β-1,3-glucanase, phenylalanine ammonia-lyase, polyphenol oxidase and peroxidase, which were associated with higher disease resistance induced by the combined treatment. Furthermore, the combined treatment did not impair the quality parameters of peach fruit after 6 days of storage at 20 °C. These results suggested that the combination of ultrasound and SA treatment may be a useful technique to reduce blue mold in peach fruit.

Industrial relevance

This paper investigates the effect of ultrasound combined with SA on decay incidence of peach fruit. The results presented demonstrate that the effect of the combined treatment on the disease resistance and fruit quality should be considered by processors prior to its adoption as a preservation technique.     

遥感探测小麦条锈病严重度的GBRT模型研究

为了提高小样本数据模型的稳定性,构建具有更高精度和鲁棒性的小麦条锈病遥感探测模型.首先基于辐亮度和反射率荧光指数方法提取了冠层日光诱导叶绿素荧光(SIF)数据,然后结合对小麦条锈病病情严重度敏感的反射率光谱指数并基于改进的分类与回归树(CART)——梯度提升回归树(GBRT)算法,构建了融合反射率和冠层SIF数据的小麦...     

The public health approach to youth violence and child maltreatment prevention at the Centers for Disease Control and Prevention.

Millions of people in the United States suffer the consequences of violence, including physical injuries, psychological trauma, and death. Solutions to violence have traditionally been reactive. Through the lens of the public health perspective, the Centers for Disease Control and Prevention (CDC) views violence as predictable based on various contributing factors, and thus as preventable. Within CDC, the Division of Violence Prevention (DVP) leads efforts to prevent injury, death, and disability, and to reduce the suffering and medical costs caused by violence. DVP employs a multidisciplinary, public health approach to identify factors associated with violence, and to develop, evaluate, and disseminate preventive interventions. Psychology is one discipline that has contributed to our approach. The authors present a series of violence prevention initiatives funded by the CDC that are framed within a public health perspective, with attention to the contributions of psychology to youth violence and child maltreatment prevention. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于医疗文本数据聚类的帕金森病早期诊断预测

针对多发于老龄人群的帕金森病（PD）的早期智能化诊断的问题，提出基于医疗检测文本信息数据的聚类技术来对PD进行分析预测。首先，对原始数据集进行预处理以获取有效特征信息，并通过主成分分析（PCA）方法将原始特征分别降维到8个不同维度的维度空间；然后，应用5个传统的经典聚类模型和3种不同的聚类集成方法分别对8个维度空间的数据进行聚类；最后，采用4个聚类性能指标来预测数据集中的多巴胺异常PD患者、健康体和无多巴胺缺失（SWEDD） PD患者。仿真结果显示，PCA特征维度值取30时，高斯混合模型（GMM）的聚类准确度达到89.12%；PCA特征维度值取70时，谱聚类（SC）的聚类准确度达到61.41%；PCA特征维度值取80时，元聚类算法（MCLA）的聚类准确度达到59.62%。对比实验结果表明，5种经典聚类方法中，PCA的特征维度值小于40时，高斯混合模型聚类效果最佳；3种聚类集成方法中，对于不同的特征维度，MCLA的聚类性能均表现优异，进而为PD的早期智能化辅助诊断提供了技术和理论支撑。     

基于Hilbert2Huang Transform 的心音信号谱分析

心音信号是一种典型的非平稳信号,传统信号处理方法的应用受到很大限制。针对此本文提出了基于 Hilbert - Huang Transform(HHT) 的心音信号的分析方法,对冠心病患者的心音信号进行了分析。通过把心音信号分 解为内蕴模式函数,利用Hilbert 变换建立了心音信号的时间- 频率- 能量三维Hilbert 谱分布以及边界谱分布; Hilbert 谱及其边界谱在时域以及频域以较高的分辨率表征了心音信号的时频变化特性,揭示了冠心病患者心音信 号的病理特征;为冠心病的早期无损诊断奠定了坚实基础,临床实践中有较大的指导价值。