基于改进深度神经网络的心血管疾病预测

心血管疾病是威胁人类健康的常见疾病,为了能够更加准确地对其预测,本文在传统DNN模型基础上进行优化改进,提出定向正则的深度神经网络(TR-DNN)模型,通过改进原有深度神经网络模型所存在的缺陷,使其能够更好地对心血管疾病数据集进行训练并测试,进一步实现心血管疾病预测任务。实验表明该模型在数据集训练上的表现良好,并且在测试集上取得优秀的结果。最后,将TR-DNN与SVM、RF、XGBoost模型在同一数据集进行结果比较,TR-DNN模型的各项评价指标均优于其它模型,在准确率方面相较传统DNN模型提高1.507个百分点,召回率提高1.57个百分点,特异度提高2.54个百分点,精确率提高1.51个百分点。因此,TR-DNN模型可以应用于心血管疾病的预测。     

基于 Asp ．net 的慢性肾脏病分期管理系统的实现与改进

近年来,慢性肾脏病（Chronic Kidney Disease ,CKD）的发病率呈逐年增长趋势,且患者对慢性肾脏病的知晓率非常低,以致病情恶化,错过了治疗最佳时机。论文设计并实现了基于 Asp ．net 的慢性肾脏病分期管理系统,从系统设计、系统架构、关键技术及改进、系统运行等方面介绍了实现过程。通过该系统的应用,在临床上能方便医生有效管理患者,并能掌握每个患者疾病的发展情况;同时,能够提高患者对病情的了解,增加患者的知晓率和防治慢性肾脏病的知识。系统对该种疾病的防治有重要的意义。     

Urinary Proteomics and Precision Medicine for Chronic Kidney Disease: Current Status and Future Perspectives

Precision medicine is since long an ongoing refinement of classical medicine, integrating improved and more detailed pathophysiological understanding with rapid technological advances. In the heterogenous area of chronic kidney disease there seems to be a high potential for the improvement in treatment and prognosis for several causes, with new technologies under development, that are yet to be introduced in clinical practice. As in other medical disciplines, investigation of abundant peptide patterns (proteomics) has gained recent interest. Especially relevant for kidney disease, urinary proteomics may provide both improved diagnosis and, as reviewed here, also holds promise for personalized treatment in the future. So far, capillary electrophoresis coupled to mass spectrometry (CE‐MS) is the most widely applied technique, and in addition to several cross‐sectional and cohort studies, there is even an ongoing randomized controlled trial that will soon report on the concept used as a method of personalizing treatment. In addition, there is hope that urinary proteomics can turn into a “liquid biopsy,” replacing the invasive diagnostic procedure. The next couple of years will provide more answers on the topic.     

Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals

Automated biomedical signal processing becomes an essential process to determine the indicators of diseased states. At the same time, latest developments of artificial intelligence (AI) techniques have the ability to manage and analyzing massive amounts of biomedical datasets results in clinical decisions and real time applications. They can be employed for medical imaging; however, the 1D biomedical signal recognition process is still needing to be improved. Electrocardiogram (ECG) is one of the widely used 1-dimensional biomedical signals, which is used to diagnose cardiovascular diseases. Computer assisted diagnostic models find it difficult to automatically classify the 1D ECG signals owing to time-varying dynamics and diverse profiles of ECG signals. To resolve these issues, this study designs automated deep learning based 1D biomedical ECG signal recognition for cardiovascular disease diagnosis (DLECG-CVD) model. The DLECG-CVD model involves different stages of operations such as pre-processing, feature extraction, hyperparameter tuning, and classification. At the initial stage, data pre-processing takes place to convert the ECG report to valuable data and transform it into a compatible format for further processing. In addition, deep belief network (DBN) model is applied to derive a set of feature vectors. Besides, improved swallow swarm optimization (ISSO) algorithm is used for the hyperparameter tuning of the DBN model. Lastly, extreme gradient boosting (XGBoost) classifier is employed to allocate proper class labels to the test ECG signals. In order to verify the improved diagnostic performance of the DLECG-CVD model, a set of simulations is carried out on the benchmark PTB-XL dataset. A detailed comparative study highlighted the betterment of the DLECG-CVD model interms of accuracy, sensitivity, specificity, kappa, Mathew correlation coefficient, and Hamming loss.     

基于稀疏逻辑回归和多元融合算法的慢性肾病进展预测模型

只有一部分慢性肾病（Chronic Kidney Disease, CKD）3期的患者会进展到4期,观察临床数据发现进展和非进展患者部分生理指标有较大的区别。本文首次将基于L1/2范数正则化的逻辑回归（Sparse Logistic Regression, SLR）用于筛选影响CKD患者进展的关键因素,然后利用SLR、支持向量机（SVM）、提升决策树（AdaBoost Decision Tree, BOOSTDT）建立进展风险预测模型。另外,本文引入堆叠算法Stacking（STKSSD）克服样本量不足使得模型泛化性能不稳定的缺陷。作为对比,本文分别利用神经网络（ANN）、循环神经网络（BLSTM）对数据建模。实验结果表明,当SLR算法选择磷、血清肌酐等11个关键特征时, STKSSD融合模型效果最好,其中测试查全率、查准率、F1值分别为86.97%、92.86%和89.82%。     

Mass Spectrometric Analysis of Cerebrospinal Fluid Ubiquitin in Alzheimer's Disease and Parkinsonian Disorders

1 Purpose

Dysfunctional proteostasis, with decreased protein degradation and an accumulation of ubiquitin into aggregated protein inclusions, is a feature of neurodegenerative diseases. Identifying new potential biomarkers in cerebrospinal fluid (CSF) reflecting this process could contribute important information on pathophysiology.

2 Experimental design

A developed method combining SPE and PRM‐MS is employed to monitor the concentration of ubiquitin in CSF from subjects with Alzheimer's disease (AD), Parkinson's disease (PD), and progressive supranuclear palsy (PSP). Four independent cross‐sectional studies are conducted, studies 1–4, including controls (n = 86) and participants with AD (n = 60), PD (n = 15), and PSP (n = 11).

3 Results

The method shows a repeatability and intermediate precision not exceeding 6.1 and 7.9%, respectively. The determined LOD is 0.1 nm and the LOQ range between 0.625 and 80 nm . The CSF ubiquitin concentration is 1.2–1.5‐fold higher in AD patients compared with controls in the three independent AD‐control studies (Study 1, p < 0.001; Study 2, p < 0.001; and Study 3, p = 0.003). In the fourth study, there is no difference in PD or PSP, compared to controls.

4 Conclusion and clinical relevance

CSF ubiquitin may reflect dysfunctional proteostasis in AD. The described method can be used for further exploration of ubiquitin as a potential biomarker in neurodegenerative diseases.     

慢性肾脏病分期及短信提醒系统

根据医院肾病科的需求,设计并实现基于B/S三层体系结构的慢性肾脏病分期及短信提醒系统。该系统对慢性肾脏病患者按肾小球滤过率（GFR）进行分级管理,以发送手机短信的方式,为不同级别患者发送相应的治疗和护理等指导信息,帮助患者更好地控制和治疗疾病。本文从系统需求分析、体系架构、系统业务流程、数据库的设计及系统实现中的关键技术等方面介绍慢性肾脏病分期及短信提醒系统的设计与实现。目前,系统正在试运行阶段,如果使用效果良好,可以推广到其他慢性病的防治中去,意义重大。      

疾病预防控制中心档案管理中信息技术的运用价值

在信息时代,网络技术进入人们的视野,并在各个行业发展中发挥突出的作用。疾病预防控制中心档案管理工作涉及多方面的内容,比如卫生管理、卫生监测、疾病规划和防控等,在这一过程中合理应用先进的信息化手段可提升档案管理工作质量。为此,疾病预防控制中心要认识到运用信息技术的重要意义,制订完善的运用措施。     

心血管功能脉图检测诊断系统的设计和实现

论文设计了一种新型的由计算机控制的无创伤心血管功能检测诊断系统,系统通过应变片式压力传感器采集脉搏波信号,经过数据处理与分析获取心血管系统的血流动力流变学参数,据此引入神经网络技术智能地诊断人体的心血管功能,为心血管疾病的检测诊断及早期病变的预报提供了一种有力的工具。     

基于温室植物叶片纹理的病害图像处理及特征值提取方法的研究

为了实现温室病害的智能化防治,深入研究了植物病害图像处理中基于叶片纹理的特征值提取方法。通过对温室黄瓜斑疹病和角斑病的处理研究发现,利用灰度共生矩阵方法提取出来的惯性值是识别这两种病害较好的特征参量之一。     

辽宁地区柞树早烘病发病规律研究

通过对辽宁省西丰县、沈阳市及凤城市的几个发病较重柞蚕场的调查和查阅相关资料，研究发现柞树早烘病开始发生于8月下旬，大面积的早烘病开始于9月上旬，此病首先发生在柞树下部枝条的基部叶片上，再向中上部叶片发展。发病初期在柞叶边缘处出现褐色的小斑点，分布不均匀，以后斑点继续扩大，由叶缘部位不均匀地向中部扩展，最终叶片全部变成褐色，但叶片并不脱落。一般柞墩密度大、根刈树形、北向坡（阴坡）、坡度大的蚕场早烘病发病重；从树种看辽东栎、蒙古栎发病较重，槲和麻栎较轻；从树的枝龄来看，3～5年生或者更老枝龄的柞树发病严重，1～2年生的枝龄不发病或发病较轻。     

Driving in an urban environment,the stress response and effects of exercise

Driving may be detrimental to health, with one hypothesis suggesting that driving may elicit an acute stress response and, with repeated exposures, may become a chronic stressor. The present study examined the stress response to driving and the effectiveness of a prior exercise bout in dampening this response. Twenty healthy adults performed three tasks: control, driving and exercise plus driving. Heart rate (HR), heart rate variability (HRV), blood pressure (BP) and cortisol were measured to quantify the acute stress response to each condition. Data indicated a stress response to driving: HR was elevated and HRV was reduced during the driving task compared with control. HR was elevated and HRV was reduced comparing the exercise plus driving with the driving condition. BP and cortisol were not different among conditions. The potential of interventions, such as exercise, to counter daily stressors should be evaluated to safeguard long-term health.

Practitioner Summary: this study confirms that driving induces a stress response, with the exercise intervention providing mixed results (an increase in cardiovascular measures and a decrease in cortisol measure trending significance). Given the known consequences of stress and evidence that exercise can mitigate acute stress, further evaluation of exercise interventions is recommended.     

An integrated methodology to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes

Today, air pollution, smoking, use of fatty acids and ready‐made foods, and so on, have exacerbated heart disease. Therefore, controlling the risk of such diseases can prevent or reduce their incidence. The present study aimed at developing an integrated methodology including Markov decision processes (MDP) and genetic algorithm (GA) to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes. First, the efficiency of GA is evaluated against Grey Wolf optimization (GWO) algorithm, and then, the superiority of GA is revealed. Next, the MDP is employed to estimate the risk of cardiovascular disease. For this purpose, model inputs are first determined using a validated micro‐simulation model for screening cardiovascular disease developed at Tehran University of Medical Sciences, Iran by GA. The model input factors are then defined accordingly and using these inputs, three risk estimation models are identified. The results of these models support WHO guidelines that provide medicine with a high discount to patients with high expected LYs. To develop the MDP methodology, policies should be adopted that work well despite the difference between the risk model and the actual risk. Finally, a sensitivity analysis is conducted to study the behavior of the total medication cost against the changes of parameters.     

Assessing the Value of a Second Opinion: the Role and Structure of Exchangeability

This paper displays the productive role of the judgment of exchangeability and even conditional exchangeability that should replace the misleading assertion of independence of various experts' opinions regarding uncertain situations. The application is technically rather complicated, and a basic understanding of exchangeability in its simple applications is presumed. One component of the analysis is novel. It suggests how we might specify a likelihood function that allows us to learn about one exchangeable sequence of events from the outcomes of another exchangeable sequence. The substantive content of the the paper concerns the use of personal probabilities by two experts in assessing the sex of human skulls found in anthropological investigations. Although we initially value the two experts' assertions exchangeably, we learn to value one of the expert's assertions more than the other's. Moreover we identify precisely how much to value the elicitation from a second expert after we have already learned the assertion of the first. Valuation is based on a decision theoretic procedure assessing reduction in risk.