重症监护病房紧急状况预警算法

重症监护病房中的病人身体状况通常很不稳定,常出现各种需要医护人员介入治疗的紧急状况。由于医疗资源有限,医护人员可能无法及时发现并处理这些紧急状况,给病人的存活率带来严重的负面影响。如果可以预测这些紧急状况的发生,并及时通知相关医护人员,将大大提高病人的存活率。常见重症监护病房紧急状况包括突然死亡、败血症、肺部感染、急性低血压、以及器官衰竭等。紧急状况预警建模主要采用病人的长时间生命体征监测数据,预测在一定时间之后发生某种紧急状况的可能性。预警模型所采用的监测数据分为静态数据、事件数据和时间序列数据等三类。静态数据具有容易采集、但预测准确性偏低的特点。事件数据或时间序列数据、以及多种类型数据的混合数据对于紧急状况预警模型的预测性能的提高有重要作用,将会获得更广泛的应用。     

完备ARRs集的生成方法及传感器优化配置应用

广泛应用于故障诊断和传感器优化、分析、证实的解析冗余关系(Analytical redundancy relations,ARRs)缺乏系统、有效的方法来产生完备ARRs集,为此,提出了一种逐次消元法。该方法以系统元关系(Primary relations,PRs)为基础,通过若干次循环消元过程,生成了完备ARRs集,同时生成了对应的假定特征矩阵(Hypothetical signature matrix,HSM);基于HSM,把传感器优化配置问题映射为一个特殊的0-1整数规划模型,并用分支定界法求解该模型。应用表明,该方法能在不降低故障检测率、隔离率的前提下减少传感器数目,降低了测试代价,对故障诊断中的传感器配置问题有借鉴意义。     

基于Flash Builder4.0＋ MyEclipse8.5的新病案首页系统设计与实现

采用Flash Builder4.0、MyEclipse8.5为开发工具,Oracle 10g为数据库,开发了新病案首页系统,详细阐述了系统的设计流程、功能分析和数据库设计与实现。     

医院门诊部局域网和设计实现

分析医院门诊部局域网和实现的功能,并讲解基于ASP实现病人档案数据调取和交换。     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

An evolutionary lion optimization algorithm‐based image compression technique for biomedical applications

Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde–Buzo–Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2‐LBG method utilizing the Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search‐based LBG (CS‐LBG), Firefly‐based LBG (FF‐LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2‐LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS‐LBG, FA‐LBG, and JPEG2000 methods. The experimental values revealed that the L2‐LBG process yielded effective compression performance with a better‐quality reconstructed image.     

A novel method for the classification of Alzheimer's disease from normal controls using magnetic resonance imaging

Alzheimer's disease (AD) is the most prevalent form of dementia. Although fewer people, who suffer from AD are correctly and promptly diagnosed, due to a lack of knowledge of its cause and unavailability of treatment, AD is more manageable if the symptoms of mild cognitive impairment (MCI) are in an early stage. In recent years, computer‐aided diagnosis has been widely used for the diagnosis of AD. The main motive of this paper is to improve the classification and prediction accuracy of AD. In this paper, a novel approach is developed to classify MCI, normal control (NC), and AD using structural magnetic resonance imaging (sMRI) from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset (50 AD, 50 NC, 50 MCI subjects). FreeSurfer is used to process these MRI data and obtain cortical features such as volume, surface area, thickness, white matter (WM), and intrinsic curvature of the brain regions. These features are modified by normalizing each cortical region's features using the absolute maximum value of that region's features from all subjects in each group of MCI, NC, and AD independently. A total of 420 features are obtained. To address the curse of dimensionality, the obtained features are reduced to 30 features using a sequential feature selection technique. Three classifiers, namely the twin support vector machine (TSVM), least squares TSVM (LSTSVM), and robust energy‐based least squares TSVM (RELS‐TSVM), are used to evaluate the classification accuracy from the obtained features. Five‐fold and 10‐fold cross‐validation are used to validate the proposed method. Experimental results show an accuracy of 100% for the studied database. The proposed approach is innovative due to its higher classification accuracy compared to methods in the existing literature.     

基于神经网络观测器的飞控传感器在线故障诊断

为提高无人机飞行安全可靠性,针对飞行控制系统中常出现的传感器故障以及非线性气动力模型参数难以确定的问题,提出了基于BP神经网络观测器估计的故障诊断方法;引用LM改进算法对网络参数进行调整,构造了神经网络观测器模型逼近非线性系统,并运用于飞行控制系统进行在线数字仿真,对垂直陀螺输出卡死故障、恒偏差故障和恒增益故障分别进行仿真分析;仿真结果表明,所设计神经网络观测器可以有效估计系统输出,在线诊断传感器故障。     

基于Elman神经网络的齿轮故障诊断研究

在齿轮故障诊断过程中,针对传统的BP神经网络具有学习、记忆不稳定等缺点,提出了将Elman神经网络应用于齿轮故障诊断中,建立了Elman神经网络的应用结构模型,介绍了该网络的训练算法,阐明了齿轮故障诊断的实现过程;结果表明该神经网络学习记忆稳定,具有很好的学习功能,诊断方法具有高可靠性,达到了预期效果。     

基于灰色关联和D-S证据理论的感应电转子故障诊断

针对单一的灰色关联或D-S证据理论在转子故障诊断中存在的不足,将灰色关联和D-S证据理论相融合的决策级信息融合方法应用到感应电机转子故障诊断中;首先用灰色关联对故障进行初步诊断,然后,将灰色关联分析的诊断输出结果作为D-S证据理论的基本概率分配,最后,依据证据组合规则进行合成,得出转子故障的最终诊断结果;实验仿真结果验证了方法在转子故障诊断中的有效性,可以减小诊断的不确定性,提高故障的诊断准确率和诊断精度。