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A self-learning expert system for diagnosis in traditional Chinese medicine   总被引:5,自引:0,他引:5  
A novel self-learning expert system for diagnosis in Traditional Chinese medicine (TCM) was constructed by incorporating several data mining techniques, mainly including an improved hybrid Bayesian network learning algorithm, Naı̈ve–Bayes classifiers with a novel score-based strategy for feature selection and a method for mining constrained association rules. The data-driven nature distinguished the system from those existing TCM expert systems based on if-then rules to address knowledge elicitation problem. Moreover, the learned knowledge was provided in multiple forms including causal diagram, association rule and reasoning rules derived from classifiers. Finally, five representative cases were diagnosed to evaluate the performance of the system and the encouraging results were obtained. The results show that the prototype system performs well in diagnosis of TCM, and could be expected to be useful in the practice of TCM.  相似文献
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A new level set method for inhomogeneous image segmentation   总被引:2,自引:0,他引:2  
Intensity inhomogeneity often appears in medical images, such as X-ray tomography and magnetic resonance (MR) images, due to technical limitations or artifacts introduced by the object being imaged. It is difficult to segment such images by traditional level set based segmentation models. In this paper, we propose a new level set method integrating local and global intensity information adaptively to segment inhomogeneous images. The local image information is associated with the intensity difference between the average of local intensity distribution and the original image, which can significantly increase the contrast between foreground and background. Thus, the images with intensity inhomogeneity can be efficiently segmented. What is more, to avoid the re-initialization of the level set function and shorten the computational time, a simple and fast level set evolution formulation is used in the numerical implementation. Experimental results on synthetic images as well as real medical images are shown in the paper to demonstrate the efficiency and robustness of the proposed method.  相似文献
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The ultimate intention of quantitative structure-activity relationship (QSAR) study in toxicology is to predict the toxic potential of untested compounds with great accuracy. As QSAR has been based on the assumption that compounds from the same chemical domain will behave in similar manner, the QSAR model built upon the analogical chemicals is hypothesized to exhibit better performance than that derived from the miscellaneous data set. In this paper, the acute toxicity, 96 h LC(50) (median lethal concentration) for the fathead minnow from database EPAFHM_v2a_617_1Mar05 served as the interested toxicity endpoint, and the mode of action (MOA) in toxic response was employed as a criterion to compartmentalize the chemical domains. MOA-based local QSAR models were built by partial least squares (PLS) regression for each subset with single mode of action such as Narcosis I, Narcosis II or Reactive, and global model was also developed for the combined data set containing several subsets above. By comparing the performances of these two types of models, the local models were superior to the global model in that the relative standard error (R.S.E.) of the former was much lower for both the training set and the test set of any subset. In addition, the influence of the reliability of MOA determination on the performance of local model was also investigated and the statistical results for subsets with MOAs at A and B confidence level were better than those at C and D confidence level. Therefore, the MOA-based local QSAR models are promising to improve the accuracy of toxicity prediction as long as the assessment of MOA is of high reliability.  相似文献
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Image segmentation plays an important role in medical image analysis. The most widely used image segmentation algorithms, region-based methods that typically rely on the homogeneity of image intensities in the regions of interest, often fail to provide accurate segmentation results due to the existence of bias field, heavy noise and rich structures. In this paper, we incorporate nonlocal regularization mechanism in the coherent local intensity clustering formulation for brain image segmentation with simultaneously estimating bias field and denoising, specially preserving good structures. We define an energy functional with a local data fitting term, two nonlocal regularization terms for both image and membership functions, and a $L_2$ image fidelity term. By minimizing the energy, we get good segmentation results with well preserved structures. Meanwhile, the bias estimation and noise reduction can also be achieved. Experiments performed on synthetic and clinical brain magnetic resonance imaging data and comparisons with other methods are given to demonstrate that by introducing the nonlocal regularization mechanism, we can get more regularized segmentation results.  相似文献
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分析药用植物学实训的难点以及局限性,调查研究中医药院校师生及中医药爱好者对实训的建议,结合虚拟现实技术,使用Unity3D模拟再现真实的药用植物实训场景.实现跨时空学习中药的性味归经、功效主治、配伍禁忌等,认识中药的植物学特征,了解道地药材的生长环境,辨别道地药材及鉴别其特征,采摘中药全株或入药部位.为增强学习的趣味性,加入四季变化,昼夜交替及不同天气等元素,优化环境.另外不同环境可发布任务,以游戏的形式完成任务获得奖励.将虚拟现实应用到实际教学培训中,提高学习效率,增加专业知识,培养自救能力,有效锻炼使用者的安全意识,减少安全事故的发生.  相似文献
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