人工神经网络在临床应用中的伦理思考

现代临床医学中的海量信息具有非线性特质,人工神经网络的自我学习、记忆和归纳功能,刚好适应了医学的新发展,在医学领域有良好的应用前景.事实上,人工神经网络临床应用的理论研究成果丰富,但商业应用并不多见.本文探讨人工神经网络在临床应用中可能引发的社会伦理争议,对其临床适用范围进行初步的论述.     

人工神经网络应用临床医学诊断的思考

人工神经网络是人工智能的一种形式。近年来．它被广泛用于临床医学的疾病筛查、诊断以及预后预测等多方面。人工神经网络能否取代临床医师的诊断工作，它在临床医学中扮演何种角色，这是一个有意思的话题。本文将从人工神经网络及其医学诊断的原理入手，探讨人工神经网络医学诊断的本质、社会问题等。     

转化医学在机械性创伤致继发性心脏损伤研究中的应用

当前转化医学受到国内外医学工作者的广泛关注,然而在实际应用工作中。,面临着基础研究和临床应用如何有效结合。本文以机械性创伤致继发性心脏损伤的研境课题为例,系统介绍了“临床一实验室一临床”模式的具体实施方法及存在的问题,以促进转化医学在医学研究中的顺利开展。     

转化医学在机械性创伤致继发性心脏损伤研究中的应用

当前转化医学受到国内外医学工作者的广泛关注,然而在实际应用工作中,面临着基础研究和临床应用如何有效结合.本文以机械性创伤致继发性心脏损伤的研究课题为例,系统介绍了“临床-实验室-临床”模式的具体实施方法及存在的问题,以促进转化医学在医学研究中的顺利开展.     

转化医学与肿瘤诊治

转化医学是促进基础研究成果向临床应用转化和根据临床医学的需求从事应用基础研究的桥梁。本文以转化医学在肿瘤防治研究的实例,分析转化医学的意义,以期为使大家重视转化医学,提高肿瘤诊治水平。     

循证医学在神经外科临床决策中的应用

在神经外科复杂的诊治实践中,需要循证医学的方法不断提高临床决策水平。本文从神经外科的角度对循证医学在临床决策中的应用进行了分析和探讨。     

骨科疾病诊治中的临床思维

医学哲学是对医学科学成就总的概括,并以此为基础探讨生命活动和病程的一般规律,研究医学科学的思维方式,直至疾病预防、诊断治疗。同时研究辩证规律和范畴在医学科学中的表现,从而指导骨科医师解决在骨科疾病诊治中遇到的诸多问题,并有助于形成正确的临床思维。结合临床工作体会,探讨医学哲学在骨科疾病诊治临床思维中的应用。     

如何正确处理循证医学与经验医学之间的关系

循证医学主张慎重和准确地应用当前所能获得的最好的研究依据。通过论述循证医学在临床实践中的重要性以及与经验医学的关系,证实循证医学是对经验医学的超越,临床医师应转变单一的经验医学,且有机地将二者结合,深刻理解指南的内容及意见,指导临床实践,达到最佳的治疗目的。     

循证烧伤医学与烧伤医学的发展

循证医学是以证据为基础的医学,其在烧伤医学的应用即为循证烧伤医学。循证医学主要实施策略是发现和提出问题;寻求烧伤医疗实践中或者文献中的有价值证据;进行实验或方法学的评价;应用于临床检验的实践中;评价实践结果。循证烧伤医学和循证医学对烧伤医学的学科发展和临床工作的开展具有重要的意义,烧伤医学今后的发展方向是循证烧伤医学。     

循证医学在神经外科临床决策中的应用

在神经外科复杂的诊治实践中,需要循证医学的方法不断提高临床决策水平.本文从神经外科的角度对循证医学在临床决策中的应用进行了分析和探讨.     

Efficiency investigation from shallow to deep neural network techniques in human activity recognition

In the last years, several researchers measured different recognition rates with different artificial neural network (ANN) techniques on public data sets in the human activity recognition (HAR) problem. However an overall investigation does not exist in the literature and the efficiency of complex and deeper ANNs over shallow networks is not clear. The purpose of this paper is to investigate the recognition rate and time requirement of different kinds of ANN approaches in HAR. This work examines the performance of shallow ANN architectures with different hyper-parameters, ANN ensembles, binary ANN classifier groups, and convolutional neural networks on two public databases. Although the popularity of binary classifiers, classifier ensembles and deep learning have been significantly increasing, this study shows that shallow ANNs with appropriate hyper-parameters in combination with extracted features can reach similar or higher recognition rate in less time than other artificial neural network methods in HAR. With a well-tuned ANN we outperformed all previous results on two public databases. Consequently, instead of the more complex ANN techniques, the usage of simple ANN with two or three layers can be an appropriate choice for activity recognition.     

Applying artificial neural network models to clinical decision making

Because psychological assessment typically lacks biological gold standards, it traditionally has relied on clinicians' expert knowledge. A more empirically based approach frequently has applied linear models to data to derive meaningful constructs and appropriate measures. Statistical inferences are then used to assess the generality of the findings. This article introduces artificial neural networks (ANNs), flexible nonlinear modeling techniques that test a model's generality by applying its estimates against "future" data. ANNs have potential for overcoming some shortcomings of linear models. The basics of ANNs and their applications to psychological assessment are reviewed. Two examples of clinical decision making are described in which an ANN is compared with linear models, and the complexity of the network performance is examined. Issues salient to psychological assessment are addressed.     

Distributed Connectionist Models in Social Psychology

Distributed connectionist models of mental representation (also termed PDP or parallel distributed processing, or ANN or artificial neural networks) constitute a fundamental alternative to the associative or schematic models that have been much more prevalent in social psychology. A connectionist model is made up of a large number of very simple processing units, richly interconnected and able to send signals to each other depending on their momentary activation levels. No individual processing unit represents a meaningful concept; instead, overall patterns of activation hold representational meaning. This article emphasizes the novel properties of connectionist representation that might appeal to theorists and researchers in social psychology, including their context sensitivity and flexibility, ability to represent prototypes and exemplars within a single network, and ability to determine whether a stimulus is familiar even before the stimulus can be identified or categorized.     

Human age classification using facial skin aging features and artificial neural network

In this paper a novel method based on facial skin aging features and Artificial Neural Network (ANN) is proposed to classify the human face images into four age groups. The facial skin aging features are extracted by using Local Gabor Binary Pattern Histogram (LGBPH) and wrinkle analysis. The ANN classifier is designed by using two layer feedforward backpropagation neural networks. The proposed age classification framework is trained and tested with face images from PAL face database and shown considerable improvement in the age classification accuracy up to 94.17% and 93.75% for male and female respectively.     

Support vector machine for classification of walking conditions of persons after stroke with dropped foot

Walking with dropped foot represents a major gait disorder, which is observed in hemiparetic persons after stroke. This study explores the use of support vector machine (SVMs) to classify different walking conditions for hemiparetic subjects. Seven participants with dropped foot (category 4 of functional ambulatory category) walked in five different conditions: level ground, stair ascent, stair descent, upslope, and downslope. The kinematic data were measured by two portable sensor units, each comprising an accelerometer and gyroscope attached to the lower limb on the shank and foot segments. The overall classification accuracy of stair ascent, stair descent, and other walking conditions was 92.9% using input features from the sensor attached to the shank. It was further improved to 97.5% by adding two more inputs from the sensor attached to the foot. Stair ascent was also classified by the inputs from the foot sensor unit with 96% accuracy. The performance of an SVM was shown to be superior to that of other machine learning methods using artificial neural networks (ANN) and radial basis function neural networks (RBF). The results suggested that the SVM classification method could be applied as a tool for pathological gait analysis, pattern recognition, control signals in functional electrical stimulation (FES) and rehabilitation robot, as well as activity monitoring during rehabilitation of daily activities.     

The use of artificial neural networks and multiple linear regression in modelling work–health relationships: Translating theory into analytical practice

Although psychological theory acknowledges the existence of complex systems and the importance of nonlinear effects, linear statistical models have been traditionally used to examine relationships between environmental stimuli and outcomes. The way we analyse these relationships does not seem to reflect the way we conceptualize them. The present study investigated the application of connectionism (artificial neural networks) to modelling the relationships between work characteristics and employee health by comparing it with a more conventional statistical linear approach (multiple linear regression) on a sample of 1003 individuals in employment. Comparisons of performance metrics indicated differences in model fit, with neural networks to some extent outperforming the linear regression models, such that R ² for worn-out and job satisfaction were significantly higher in the neural networks. Most importantly, comparisons revealed that the predictors in the two approaches differed in their relative importance for predicting outcomes. The improvement is attributed to the ability of the neural networks to model complex nonlinear relationships. Being unconstrained by assumptions of linearity, they can provide a better approximation of such psychosocial phenomena. Nonlinear approaches are often better fitted for purpose, as they conform to the need for correspondence between theory, method, and data.     

Artificial neural networks in Neurosciences

This article shows that artificial neural networks are used for confirming the relationships between physiological and cognitive changes. Specifically, we explore the influence of a decrease of neurotransmitters on the behaviour of old people in recognition tasks. This artificial neural network recognizes learned patterns. When we change the threshold of activation in some units, the artificial neural network simulates the experimental results of old people in recognition tasks. However, the main contributions of this paper are the design of an artificial neural network and its operation inspired by the nervous system and the way the inputs are coded and the process of orthogonalization of patterns.     

On simulating one-trial learning using morphological neural networks

“Learning once, remembering forever”, this wonderful cognitive phenomenon sometimes occurs in the learning process of human beings. Psychologists call this psychological phenomenon “one-trial learning”. The traditional artificial neural networks can simulate the psychological phenomenon of “implicit learning”, but can’t simulate the cognitive phenomenon of “one-trial learning”. Therefore, cognitive psychology gives a challenge to the traditional artificial neural networks. From two aspects of theory and practice in this paper, the possibility of simulating this kind of psychological phenomenon was explored by using morphological neural networks. This paper takes advantage of morphological associative memory networks to realize the simulation of “one-trial learning” for the first time, and gives 5 simulating practical examples. Theoretical analysis and simulation experiments show that the morphological associative memory networks are a higher effective machine learning method, and can better simulate the cognitive phenomenon of “one-trial learning”, therefore provide a theoretical basis and technological support for the study of intelligent science and cognitive science.     

Abstract noun classification: using a neural network to match word context and word meaning

Psychologists have used artificial neural networks for a few decades to simulate perception, language acquisition, and other cognitive processes. This paper discusses the use of artificial neural networks in research on semantics—in particular, in the investigation of abstract noun meanings. It is widely acknowledged that a word’s meaning varies with its contexts of use, but it is a complex task to identify which context elements are relevant to a word’s meaning. The present study illustrates how connectionist networks can be used to examine this problem. A simple feedforward network learned to distinguish among six abstract nouns, on the basis of characteristics of their contexts, in a corpus of randomly selected naturalistic sentences.     

Evaluating Explanations in Law, Science, and Everyday Life

ABSTRACT— This article reviews a theory of explanatory coherence that provides a psychologically plausible account of how people evaluate competing explanations. The theory is implemented in a computational model that uses simple artificial neural networks to simulate many important cases of scientific and legal reasoning. Current research directions include extensions to emotional thinking and implementation in more biologically realistic neural networks.