常用分类算法在脑梗死预测中的比较研究

目的：比较常用分类算法对脑梗死的分类预测能力。方法：将反映动脉弹性的6个脉搏波参数加年龄、性别一共8个指标作为每个样本的特征。把样本按3∶1随机分为训练集和测试集两部分。分别利用人工神经网络（ANN）、贝叶斯（Bayes）、决策树（Decision Tree,DT）、K邻近法（k-NN）、支持向量机（SVM）算法构造分类器,使用各分类器对训练集样本进行学习以建立分类预测模型,再用测试集测试各个模型的分类准确度。结果：SVM分类器和DT分类器效果较好,准确率超过80%。结论：以反映血管弹性的脉搏波参数结合性别、年龄作为特征并使用SVM或者DT算法来构建分类预测模型,有一定实用价值。     

Narrative review of artificial intelligence in diabetic macular edema: Diagnosis and predicting treatment response using optical coherence tomography

Diabetic macular edema (DME), being a frequent manifestation of DR, disrupts the retinal symmetry. This event is particularly triggered by vascular endothelial growth factors (VEGF). Intravitreal injections of anti-VEGFs have been the most practiced treatment but an expensive option. A major challenge associated with this treatment is determining an optimal treatment regimen and differentiating patients who do not respond to anti-VEGF. As it has a significant burden for both the patient and the health care providers if the patient is not responding, any clinically acceptable method to predict the treatment outcomes holds huge value in the efficient management of DME. In such situations, artificial intelligence (AI) or machine learning (ML)-based algorithms come useful as they can analyze past clinical details of the patients and help clinicians to predict the patient''s response to an anti-VEGF agent. The work presented here attempts to review the literature that is available from the peer research community to discuss solutions provided by AI/ML methodologies to tackle challenges in DME management. Lastly, a possibility for using two different types of data has been proposed, which is believed to be the key differentiators as compared to the similar and recent contributions from the peer research community.     

Artificial Intelligence in Cardiology

Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. In particular, the paper first reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization. Second, it discusses common algorithms used in supervised learning and reviews selected applications in cardiology and related disciplines. Third, it describes the advent of deep learning and related methods collectively called unsupervised learning, provides contextual examples both in general medicine and in cardiovascular medicine, and then explains how these methods could be applied to enable precision cardiology and improve patient outcomes.     

基于多特征融合的线性内核SVM法挖掘生物实体关联

提高挖掘生物医学文献中的实体关联算法的性能,对开拓研究新思路有重要启示作用。提出一种改进特征的新线性内核SVM关联挖掘方法,以糖尿病相关文献摘要为研究内容,总结归纳出5种实体关联挖掘特征:实体特征、实体对特征、依赖图特征、解析树特征和名词短语约束特征,其中实体对和名词短语约束是所提出的新特征,并使用Huber损失函数作为SVM分类器的线性内核进行计算,挖掘预测疾病、基因和药物实体之间的关联。计算得到10种糖尿病相关病症和23种基因有173种关联,13种糖尿病相关病症和26种药物存在79种关联,18种基因与17种药物组成了159种关联,构建出疾病基因、疾病药物、基因药物和8种糖尿病相关疾病基因药物的关联网络,共计619种实体关联,同时预测出27种新实体关联对,最后使用ROC曲线验证3种关联(0.804、0.847和0.742)。结果表明,所提出算法与CoPub(0.710)、PubGene(0.609)、FBK-irst(0.547,0.800)和WBI(0.510,0.759)所用算法相比,最高精确度提升超过约5%(0.847与0.800),最低提升超过约20%(0.742与0.510),性能更优,为下一步在生物医学大数据中的应用打下良好基础。     

Stimulus dependent neural oscillatory patterns show reliable statistical identification of autism spectrum disorder in a face perceptual decision task

Objective

Electroencephalographic biomarkers have been widely investigated in autism, in the search for diagnostic, prognostic and therapeutic outcome measures. Here we took advantage of the information available in temporal oscillatory patterns evoked by simple perceptual decisions to investigate whether stimulus dependent oscillatory signatures can be used as potential biomarkers in autism spectrum disorder (ASD).

Automated classification of neonatal sleep states using EEG

Objective

To develop a method for automated neonatal sleep state classification based on EEG that can be applied over a wide range of age.

Additive survival least‐squares support vector machines

This work studies a new survival modeling technique based on least‐squares support vector machines. We propose the use of a least‐squares support vector machine combining ranking and regression. The advantage of this kernel‐based model is threefold: (i) the problem formulation is convex and can be solved conveniently by a linear system; (ii) non‐linearity is introduced by using kernels, componentwise kernels in particular are useful to obtain interpretable results; and (iii) introduction of ranking constraints makes it possible to handle censored data. In an experimental setup, the model is used as a preprocessing step for the standard Cox proportional hazard regression by estimating the functional forms of the covariates. The proposed model was compared with different survival models from the literature on the clinical German Breast Cancer Study Group data and on the high‐dimensional Norway/Stanford Breast Cancer Data set. Copyright © 2009 John Wiley & Sons, Ltd.     

基于熵和支持向量机的病态嗓音识别

为了更好地分析实际短数据带噪的病态嗓音信号,利用近年来提出的样本熵、多尺度熵、模糊熵和分层熵的方法来提取嗓音的熵特征参数,并借鉴分层分解方法,提出分层多尺度熵和分层模糊熵,分别对测试集39例正常嗓音和36例病态嗓音进行支持向量机（SVM）识别。实验结果表明：三层分层熵、分层多尺度熵、分层模糊熵的识别率和稳定性均较分层前有提高。在耗时较短的情况下,提取2 000点病理嗓音数据的6种熵特征都能达到较好且较稳定的识别率。提取2 000点病理嗓音数据的三层分层模糊熵特征,能得到较好且较稳定的SVM识别率97.33%,较分层前的模糊熵特征识别率提高约4.00%。熵分析方法可推进病态嗓音研究向临床的应用,为临床分析诊断实时、短数据的带噪病理嗓音提供一定的参考。     

基于NIRS技术和PCA-SVM算法6种树脂及其他类中药的快速鉴别

目的:利用近红外漫反射光谱(NIRS)法,结合主成分分析(PCA)和支持向量机(SVM)联用算法,建立6种树脂及其他类中药安息香(Benzoinum),琥珀(Succinum),没药(Myrrha),乳香(Olibanum),松香(Colophonium),天竺黄(Bambusaen Concretio Silicea)的NIR模式识别模型,用于该6味中药的快速鉴别。方法:收集上述6种中药样品,经性状鉴别和理化分析确定正品药材55批,粉碎成均匀粉末,在4 000~12 000 cm~(-1)光谱区,采集各样品粉末的NIR光谱,选取特征谱段9 000~5 400,5 000~4 000 cm~(-1)为建模谱段,分别采用矢量归一化法(vector normalization,VN),一阶导数法(first derivative,FD),二阶导数法(second derivative,SD)3种不同光谱预处理方法进行预处理并分别进行PCA降维。根据主成分空间散点图,优选最佳预处理方法。利用最佳预处理方法处理后光谱的PCA降维数据,建立SVM模式识别模型,SVM模型参数c和g采用网格搜索法结合五折交叉验证进行寻优。对比不同主成分数所建PCA-SVM模型的预测准确率,确定最佳的主成分数,最终建立6种中药NIR快速鉴别模型。结果:在9 000~5 400,5 000~4 000 cm~(-1)建模谱段,确定最佳光谱预处理方法为SD,SD预处理光谱PCA降维后,确定最佳主成分数为3个,累计贡献率达93.57%。经网格搜索法确定最佳SVM建模参数组为c=65 536,g=512。所建PCA-SVM模型对训练集和验证集样品预测正确率均达100%,模型五折交叉验证准确率亦达100%。结论:所建的6种中药NIR光谱PCA-SVM鉴别模型,预测准确率高,模型预测能力强,结合NIRS技术无损、快速的优点,该模型可用于上述6种中药的无损、快速鉴别。