人工神经网络紫外分光光度法同时测定复方阿司匹林片3组分的含量

目的在不经分离的情况下,用紫外分光光度法同时测定复方阿司匹林片中的3个组分的含量。方法采用人工神经网络法,取已知浓度的咖啡因、阿司匹林、非那西丁标准溶液按不同比例混合生成合成样品,以合成样品的不同波长下的吸光度值作为网络输入值,3组分的量为输出值,训练网络并预测复方阿司匹林片中3组分的含量。结果复方阿司匹林片中阿司匹林、非那西丁和咖啡因的平均回收率分别为98.7%、101.4%、100.4%,RSD值分别为0.7%、1.5%、2.1%。结论人工神经网络有较强的预测能力,能不经分离同时测定复方阿司匹林片中的3组分。     

神经网络在针刺治疗海洛因依赖疗效评价中的应用探讨

运用神经网络预测方法,对针刺治疗海洛因依赖患者的疗效进行评价.通过筛选中医辨证证候、海洛因戒断稽延性症状、SCL90中与海洛因渴求相关项,标准化后作为病因项输入网络,进行针刺疗效的预测.评估网络性能,讨论其评价针灸治疗海洛因渴求的可能性.     

人工神经网络在中医证候研究中的应用

人工神经网络是一种新型的数据挖掘技术,它是将整体论与还原分析方法有机结合的研究复杂系统的有效方法,能够有效处理复杂系统中杂乱无章的海量数据,并能够在海量数据中寻找模式,寻找规律,归纳隐含在信息单元之间的关联规则。中医证候体系是一个非线性的、多维多阶的、可以无限组合的复杂巨系统。采用人工神经网络技术挖掘大样本所蕴含的海量信息,从而建立中医证候诊断模型,可能是解开当前证候研究的僵局、取得实质突破的有效方法。     

应用神经网络和遗传算法优化利福霉素B发酵培养基

对培养基进行优化时，会获得大量的实验数据，但这些实验数据往往不能被进一步利用。如果使用一些不同的方法对历史数据进一步挖掘就可能得出额外的规律。本文使用神经网络对历史数据进行处理，得到了利福霉素B培养基成份和发酵效价之间的关系；再以这个关系作为遗传算法的适应度函数，在一个相对大的空间内快速搜索最优解。优化的培养基使摇瓶发酵效价提高了17．9％，说明所建立的模型能够实现培养基优化的目的并具有数据挖掘的功能。     

人工神经网络紫外光谱法测定复方氧氟沙星滴耳液的药物含量

目的对紫外光谱重叠的复方氧氟沙星滴耳液进行多组分不经分离的含量测定.方法应用人工神经网络原理,采用误差反向传播方法,对紫外吸收光谱重叠的复方氧氟沙星滴耳液进行含量测定.结果 9个模拟样品中,氧氟沙星的平均回收率和RSD为101.02%和0.94%;地塞米松磷酸钠的平均回收率和RSD为98.48%和3.40%.结论该方法测定结果准确,性能良好,对于吸收光谱重叠的药物含量测定有一定参考价值.     

医院负债经营财务风险评估及预警研究

对医院负债和风险管理理论进行系统回顾,构建出适合医院的财务风险预警指标体系;借鉴 企业财务管理经验,设置了“医院负债经营财务风险综合预警指数”(SWI),使医院财务风险的度量 和评估有了较为科学的计量依据;并尝试引入人工神经网络模型进行实证研究,提高了结论的准确率 和可信度;提出了加强政府外部监管与医院管理内部控制相结合的多角度、自上而下的风险控制、防 范模式及政策建议。     

模式识别在中药质量评价中的应用进展

目的 :对模式识别在中药质量评价中研究进展进行综述。方法 :总结有关化学模式识别、显微图像模式识别及人工神经网络在中药质量评价中应用的研究文献 ,综述不同方法在该领域中的应用。结果 :到目前为止 ,红外、紫外、裂解 高分辨气相色谱、GC-Mass总叠加质谱等被应用于中药的化学总特征的表现 ;基于体视学和计算机图像测试技术的图像定量分析 ,以及图形生成理论和计算机图形学的三维重建和显示技术被应用于中药组织的体视学参数的确定 ;误差反传等类型人工神经网络在中药质量评价中也有重要的应用。结论 :化学模式识别、显微图像模式识别、人工神经网络在中药质量评价中起到了重要的作用 ,具有广阔的发展前景。     

应用人工神经网络预测胎儿体重的研究

目的:探讨人工神经网络预测新生儿出生体重的价值。方法:将226例足月、单胎、无妊娠合并症及并发症的初产妇分为训练组(100例,男女胎儿各50例)和验证组(126例,男女胎儿各63例),训练组分别选取不同参数构建3个神经网络,(1)联合参数法:用孕妇身高、体重、腹围、宫高及B超下胎儿双顶径、股骨长和羊水池最大深度作为输入节点;(2)孕妇参数法,用孕妇身高、体重、腹围和宫高作为输入节点;(3)胎儿参数法,用B超下胎儿双顶径、股骨长和羊水池最大深度作为输入节点。神经网络构建完成后以126例验证组来分别测试3种网络的准确性和误差。结果:联合参数法准确率最高为84.94%,母亲参数法为83.45%,胎儿参数法为80.80%。结论:人工神经网络预测胎儿体重有很好的应用前景。选取合适的孕妇及胎儿参数建立网络可提高预测的准确性。     

基于人工神经网络的血清蛋白质指纹图谱模型在先天性巨结肠患儿诊断中的应用

目的建立蛋白质芯片技术检测血清蛋白质指纹图谱的方法，探讨基于人工神经网络（ANN）的血清蛋白质指纹图谱模型在先天性巨结肠（HD）患儿诊断中的应用价值。方法应用蛋白质指纹图谱分析仪测定HD患儿64例、巨结肠类缘病患儿25例和健康儿童23例血清标本的蛋白质指纹图谱，并结合ANN方法进行数据分析。112例标本随机分成训练组66例（HD40例，巨结肠类缘病14例，健康儿童12例）和盲法测试组46例（HD24例，巨结肠类缘病11例，健康儿童11例）。利用从训练组得出的基于ANN的血清蛋白质指纹图谱模型，对46例未知血清进行检测，并与X线影像学检查结果进行比较。结果筛选出质荷比（m／z）位于7211．6和2864．8的蛋白质标志物2个。在HD组表达强度分别为6．15±2．21和2．78±1．21，巨结肠类缘病组表达强度分别为12．82±7．56和4．86±0．91（Pa〈0．01）。筛选出m／z位于6884．2和5639．2的蛋白质标志物2个。HD组表达强度分别为4．09±1．78和15．57±8．87，健康对照组表达强度分别为8．31±3．07和30．31±6．18（P〈0．01）。应用该方法对HD患儿进行诊断的准确率、敏感度和特异度分别为89．13％（41／46例）、87．50％（21／24例）和90．91％（20／22例），明显高于x线影像学检查68．8％（77／112例）、82．8％（53／64例）和50．0％（24／48例）。结论在HD患儿的诊断中，利用从训练组得出的基于ANN的血清蛋白质指纹图谱模型较传统方法有更高的敏感性和特异性。     

Application of serum protein fingerprinting coupled with artificial neural network model in diagnosis of hepatocellular carcinoma

Background Hepatocellular carcinoma tends to present at a late clinical stage with poor prognosis. Therefore, it is urgent to explore and develop a simple, rapid diagnostic method, which has high sensitivity and specificity for hepatocellular carcinoma at an early stage. In this study, the serum proteins in patients with hepatocellular carcinoma or liver cirrhosis and in normal controls were analysed. Surface enhanced laser desorption/ionization time-of-flight mass (SELDI-TOF-MS) spectrometry was used to fingerprint serum protein using the protein chip technique and explore the value of the fingerprint, coupled with artificial neural network, to diagnose hepatocellular carcinoma.Methods Of the 106 serum samples obtained, 52 were from patients with hepatocellular carcinoma, 22 from patients with liver cirrhosis and 32 from healthy volunteers. The samples were randomly assigned into a training group (n=70, 35 patients with hepatocellular carcinoma, 14 with liver cirrhosis, and 21 normal controls) and a testing group (n=36, 17 patients with hepatocellular carcinoma, 8 with liver cirrhosis, and 11 normal controls). An artificial neural network was trained on data from 70 individuals in the training group to develop an artificial neural network diagnostic model and this model was tested. The 36 sera in the testing group were analysed with blind prediction by using the same flowchart and procedure of data collection. The 36 serum protein spectra were clustered with the preset clustering method and the same mass/charge (M/Z) peak values as those in the training group.　 Matrix transfer was performed after data were output. Then the data were input into the previously built artificial neural network model to get the prediction value. The M/Z peaks of the samples with more than 2000 M/Z were normalized with biomarker wizard of ProteinChip Software version 3.1 for noise filtering. The first threshold for noise filtering was set at 5, and the second was set at 2. The 10% was the minimum threshold for clustering. The statistical analysis of the data of serum protein mass spectrum was performed in the groups (normal vs. hepatocellular carcinoma, and liver cirrhosis vs. hepatocellular carcinoma) with the t test. Results Comparison between the groups of hepatocellular carcinoma and normal control: The mass spectra from 56 samples (hepatocellular carcinoma and normal controls) in the training group were analysed and 241 peaks were obtained. In addition, 21 peaks from them were used for comparison between the groups of hepatocellular carcinoma and normal controls (P<0.01). Only 2 peaks at 3015 M/Z and 5900 M/Z were selected with significant difference ［P<10（-9）］. A model was developed based on these two proteins with different M/Z. It was confirmed that this artificial neural network model can be used for comparison between the groups of hepatocellular carcinoma and normal controls. The sensitivity was 100% (17/17), and the specificity was 100% (11/11). Comparison between the groups of hepatocellular carcinoma and liver cirrhosis: The mass spectra from 49 samples in the training group (including patients with hepatocellular carcinoma and liver cirrhosis) were analysed and 208 peaks were obtained. In addition, 21 peaks from them were used for comparison between the groups of hepatocellular carcinoma and liver cirrhosis (P<0.01). Only 2 peaks at 7759 M/Z, 13134 M/Z were selected with significant difference ［P<10（-9）］. A model was developed based on these two proteins with dfferent M/Z. It was confirmed that this artificial neural network model can be used for comparison between the groups of hepatocellular carcinoma and liver cirrhosis. The sensitivity was 88.2% (15/17), and the specificity was 100% (8/8).Conclusions The specific biomarkers selected with the SELDI technology could be used for early diagnosis of hepatocellular carcinoma.