人工神经网络在药学中的应用

人工神经网络(Artificial Neural Network,ANN)是根据生物神经系统的认识过程,运用能够模拟生物神经元某些基本功能的元件,按一定拓扑结构组织起来的一种信息处理系统,它模拟人脑神经系统处理信息的过程,以信息处理的巨量并行性、高度容错性、自组织自适应为特征。近十几年来,人工神经网络在定量药物设计、药物分析、药动/药效学等方面得到了广泛的应用。     

人工神经网络及其在医药研究中的应用

简介了人工神经网络的定义及产生与发展,并介绍了人工神经网络中应用最广泛的网络模型—BP网络模型的基本原理。总结了人工神经网络在医药研究中的应用,包括应用于诊断、病人的安全监护、药物分析、构效判定等。     

人工神经网络在药动学中的应用

目的：综述人工神经网络在药动学中的应用。方法：根据近年的文献资料，对人工神经网络在药动学中的应用进行综述。结果：人工神经网络能够从提供给它们的数据中学习建立输入与输出的关系，从而极大地简化了传统药动学数据分析所需的建模工作。结论：人工神经网络是一种极有潜力的药动学建模新方法。     

人工神经网络在临床药学中的应用

介绍人工神经网络的定义及发展简史,并介绍了人工神经网络中应用最广泛的网络模型—BP网络模型的基本原理。总结了人工神经网络在临床药学中的应用,包括应用于药代动力学、药效学、临床药物监测（TDM）等。     

人工神经网络技术及其在药物复方制剂和中药分析中的应用

人工神经网络技术及其在药物复方制剂和中药分析中的应用屈凌波，相秉仁，安登魁（中国药科大学分析计算中心南京２１０００９）人工神经网络（ＡｒｔｉｆｉｃｉａｌＮｅｕｒａｌＮｅｔｗｏｒｋ，ＡＮＮ）技术是一种全新的模拟人脑功能的信息处理系统。它主要借鉴了人脑神...     

人工神经网络在类风湿关节炎诊断中的应用

目的:应用BP人工神经网络原理,设计一种类风湿关节炎疾病诊断的方法。方法:选用对类风湿关节炎敏感的8个指标,作为BP人工神经网络的输入数据,对样本进行训练和预测。结果:BP人工神经网络经通过对150例样本的运算,训练集的113例样本,训练正确率为97.4%;预测集的37例样本,预测正确率为91.9%。结论:BP人工神经网络能为类风湿关节炎作出较准确的诊断,能提高诊断的客观性。     

人工神经网络及其在肿瘤诊断中的应用

人工神经网络(Artificial Neural Network,ANN)具有自组织、自学习、自适应等多种优良而典型的特性,因而在医学领域被广泛适用.本文综述ANN的产生与发展以及在肿瘤诊断中的应用状况,并指出了它在肿瘤学的发展方向.     

人工神经网络在胃癌预后预测中的应用

目的 应用人工神经网络(ANN)技术建立胃癌预后预测模型,并与传统logistic回归模型相比较,探讨其临床意义.方法 回顾性分析安徽省阜阳市人民医院2011年1月至2016年12月收治的152例经内镜活检或术后病理证实的胃癌病人的临床及病理资料,单因素生存分析筛选出有统计学意义的指标,用人工神经网络技术建立胃癌的预后模型对胃癌术后生存时间进行预测,同时与传统logistic回归预测做对比.结果 癌胚抗原(CEA)水平、Borrmann分型、浸润深度、淋巴结转移、分化程度、TNM分期、手术方式、神经脉管浸润与胃癌病人的预后有关,ANN对胃癌病人的5年生存率预测准确率85.19％,敏感度88.24％,特异度80％;二元logistic回归对胃癌病人的5年生存率预测准确率74.07％,敏感度82.35％,特异度60％;ANN模型在不同TNM分期中的预测准确率77.78％,敏感度40％,特异度92.31％.结论 人工神经网络作为预测胃癌术后生存时间的一种新的方法,具有良好的应用前景.     

人工神经网络在冠心病诊断中的应用

冠心病是目前威胁人民健康的一种常见的慢性病之一 ,由于生活节奏加快 ,饮食条件的改善 ,近年来我国冠心病有上升趋势。降低冠心病病死率主要应改善生活方式 ,减少发病的危险因素 ,而早期诊断 ,及时予以合理的治疗也是降低病死率 ,改善患者生活质量的重要措施 ,为此我们对冠心病综合信息诊断进行了研究。１对象与方法１ １对象我院心内科１９９５年—１９９９年住院患者１５００例 ,主诉、病史、临床症状包括性别、年龄、发病时间、症状等４０余项 ,体征和辅助检查包括心电图、酶谱变化、血脂、血糖、心脏彩超等 ,均经冠状动脉造影检查 ,主…     

人工神经网络及其在药剂学研究中的应用

人工神经网络 (artificialneuralnetworks,ANNs)是人工智能技术的一个重要分支。它利用仿生学原理 ,模拟人脑处理信息的方式 ,通过非线性并行神经运算方法处理数据 ,并且有学习能力 ,能进行数值模拟预测。近年来在复杂的多因素多水平非线性问题众多的药剂学研究中成功应用于处方设计、工艺优化、体内 /体外相关性及生物药剂学等领域 ,取得了令人瞩目的成果     

人工神经网络在医学统计与信息处理方中的应用综述

神经网络作为一种新型的统计计算和信息处理方法有其独特的优越性,这里举出近年来我国和国际医学工作者在使用神经网络进行医学统计和信息处理的几个案例。由此说明神经网络将是很有前途的统计和信息处理方法,并对其未来做展望。     

人工神经网络模型在肾移植患者西罗莫司个体化给药中的应用

目的:建立人工神经网络用于估算西罗莫司血药浓度的方法。方法:收集56例肾移植患者口服西罗莫司的182份全血浓度数据,采用遗传算法配合动量法优化网络参数,建立人工神经网络,并对测试数据进行处理,验证测试结果。结果:人工神经网络平均预测误差(MPE)与平均绝对误差(MAE)分别为(0.31±1.14)、(0.89±0.77)ng·mL-1,32例/次(88.9%)血药浓度数据绝对预测误差≤2.0ng·mL-1。人工神经网络模型准确性及精密度优于多元线性回归及非线性混合效应模型。结论:人工神经网络模型可用于预测西罗莫司血药浓度,指导个体化给药。     

基于神经网络的医疗诊断专家系统

以肺癌诊断专家系统为例 ,阐述了基于神经网络的医疗诊断专家系统的原理、应用及特点     

Artificial Neural Network Prediction of Aerosol Deposition in Human Lungs

Purpose. To develop a rapid and reliable method for predicting the pattern of aerosol particle deposition within the human lungs, using artificial neural networks (ANNs). Methods. Experimental data from the literature were used to train multi-layer perceptron (MLP) networks to allow for prediction of regional and total aerosol particle deposition patterns in human lungs. These data covered particle sizes in the range 0.05-15 m and three different breathing patterns (ranging from quiet breathing to breathing under physical work conditions). Three different MLPs were trained, to provide separate predictions of aerosol particle deposition in the laryngeal, bronchial, and alveolar regions. The total deposition fraction for a given set of breathing conditions was computed simply as the sum of the outputs produced from the corresponding regional deposition MLPs. Results. The ANNs developed are shown to give highly accurate predictions for both regional and total aerosol deposition patterns for all particle sizes and breathing conditions (with errors typically less than 0.04%). Conclusions. We conclude that the current set of ANNs can be used to give good predictions of particle deposition from polydisperse pharmaceutical aerosols generated from breath-actuated dry powder inhalers, nebulizers, and metered dose inhalers with spacers.     

Neural Network Computer Simulation of Medical Aerosols

Preliminary investigations have been conducted to assess the potential for using artificial neural networks to simulate aerosol behaviour, with a view to employing this type of methodology in the evaluation and design of pulmonary drug-delivery systems. Details are presented of the general purpose software developed for these tasks; it implements a feed-forward back-propagation algorithm with weight decay and connection pruning, the user having complete run-time control of the network architecture and mode of training. A series of exploratory investigations is then reported in which different network structures and training strategies are assessed in terms of their ability to simulate known patterns of fluid flow in simple model systems. The first of these involves simulations of cellular automata-generated data for fluid flow through a partially obstructed two-dimensional pipe. The artificial neural networks are shown to be highly successful in simulating the behaviour of this simple linear system, but with important provisos relating to the information content of the training data and the criteria used to judge when the network is properly trained. A second set of investigations is then reported in which similar networks are used to simulate patterns of fluid flow through aerosol generation devices, using training data furnished through rigorous computational fluid dynamics modelling. These more complex three-dimensional systems are modelled with equal success. It is concluded that carefully tailored, well trained networks could provide valuable tools not just for predicting but also for analysing the spatial dynamics of pharmaceutical aerosols.     

基于模糊神经网络的人工鱼虚拟味觉系统研究

本文设计了在智能虚拟环境下人工鱼的一种虚拟味觉系统.利用模糊神经网络实现了鱼儿对食物的学习记忆算法.模糊神经网络由于同时具备了模糊逻辑对规则的表达能力以及神经网络的学习能力,非常适合解决虚拟环境中味觉的信号识别问题,经实验验证是切实可行的.基于模糊神经网络的味觉系统的研究和实现为人工鱼多感知融合系统提供了基础.     

早停止策略在BP神经网络中的应用

探讨了 BP神经网络在应用中如何避免过度拟合情况的发生以防止网络的泛化功能降低。结果显示 ,在 BP神经网络的训练中 ,早停止策略是避免过度拟合的一个有效方法。     

应用人工神经网络法预测地高辛血药浓度

目的:探讨应用人工神经网络(ANN)预测地高辛血药浓度的效果和特点。方法:回顾性收集我院2008年3月-2010年3月长期口服地高辛患者的临床资料,其中84例为建模数据集,10例为验证数据集。采用反向传播人工神经网络(BPANN)法预测地高辛血药浓度,并与非线性混合效应模型(NONMEM)法预测结果进行比较,计算影响血药浓度各因素的平均影响值(MIV)并排序。结果:NONMEM和ANN法预测地高辛浓度值与测定值的相关性分别为0.851及0.946,各因素中患者血清肌酐、体重、年龄较其他因素MIV大。结论:ANN法可较好地处理各因素间复杂的关系,为血药浓度预测提供了一条有效思路。     

Application of Artificial Neural Networks to Eating Disorders

An experimental application of Artificial Neural Networks to Eating Disorders is presented. The sample, composed of 172 cases (all women) collected at the Centre for the Diagnosis and Treatment of Eating Disorders of the 1st Medical Division of the St. Eugenio Hospital of Rome, was subdivided, on the basis of the diagnosis made by the specialist of the St. Eugenio, into four classes: Anorexia Nervosa (AN), Nervous Bulimia (NB), Binge Eating Disorders (BED) and Psychogenic Eating Disorders that are Not Otherwise Specified (PED-NOS). The data base was composed of 124 different variables: generic information, alimentary behavior, eventual treatment and hospitalization, substance use, menstrual cycles, weight and height, hematochemical and instrumental examinations, psychodiagnostic tests, etc. The goal of this experiment was to verify the accuracy of the Neural Networks in recognising anorexic and bulimic patients. This article describes 6 experiments, using a Feed Forward Neural Network, each one using different variables. Starting from only the generic variables (life styles, family environment, etc.) and hematoclinical and instrumental examinations, a Neural Networks provided 86.94% of the prediction precision. This work is meant to be a first contribution to creating diagnostic procedures for Eating Disorders, that would be simple and easy-to-use by professionals who are neither psychologists nor psychiatrists nor psychotherapists but who are, however, among the first to meet these patients and who are therefore called upon to give such patients the very first pieces of advice on seeking proper treatment.