中药学习与分析平台设计与实现

结合光学字符识别技术构建基于安卓的中药学习与分析平台,详细阐述平台设计开发与具体实现, 指出该平台有助于中医药相关领域人员学习与研究中药理论与中药药性配伍规律。     

基于扩散磁共振的脑组织微结构成像研究综述

微结构成像是为了改善传统扩散磁共振成像的一些缺点而发展的一种新技术.微结构成像范式旨在建立组织结构特性与体素级磁共振信号相联系的模型,以此来估计并绘制微结构属性.目前该技术正在实现从实验室研究到临床应用研究的转变.首先介绍扩散磁共振成像并分析传统技术存在的主要问题,然后阐述微结构成像原理;随后,对受阻与受限制复合扩散模...     

915 MHz微波电磁辐射对断乳鼠学习记忆能力的影响

目的 研究915 MHz微波电磁辐射对断乳鼠学习记忆能力的影响.方法 雄性Wistar大鼠(50～90 g)全身暴露于不同强度915 MHz微波电磁辐射(0、30、60 V/m),每天暴露2 h,每周暴露5 d,连续暴露6周,通过食饵迷宫试验和水迷宫试验研究915 MHz微波电磁辐射对断乳鼠学习和记忆能力影响.结果 915 MHz微波电磁辐射暴露3和6周时,与假暴露组相比较,暴露组大鼠食饵迷宫训练错误率及总训练用时增加(P＜0.05或P＜0.01);30 V/m微波暴露组总训练用时和错误次数训练次数与暴露前差异无统计学意义,而60 V/m微波暴露组总训练用时比暴露明显延长,训练次数和错误次数增加(P＜0.01);而假暴露组的训练用时则明显缩短(P＜0.01);水迷宫试验总的学习时间延长(P＜0.05).结论 随着915 MHz微波电磁辐射暴露剂量的加大及暴露时间的增加,Wistar大鼠断乳鼠的学习和记忆能力存在一定程度的减退.     

基于数据驱动的冠状动脉微循环阻力快速计算方法

目的 开发一种基于数据驱动的冠状动脉微循环阻力快速计算方法。方法 构建和优化神经网络对冠状动脉进行截面积特征提取,利用截面积特征、异速标度律和流量分配比例快速计算冠状动脉分支末端的微循环阻力,并基于微循环阻力无创计算血流储备分数。结果 为了验证神经网络的有效性,将40个临床收集的冠状动脉分支测量的截面积特征与神经网络预测的结果进行比较,平均绝对误差为1.08 mm²。为了验证微循环阻力值的准确性,将15位患者的临床血流储备分数与利用微循环阻力值计算的血流储备分数进行比较,计算准确性为86.6%。结论 本文提出的冠状动脉微循环阻力快速计算方法具有潜在的临床应用价值。     

生物医学大数据中的隐私计算

分析生物医学数据流通难点、传统隐私保护手段缺陷,介绍隐私计算优势及其主流技术路线,阐述生物医学中隐私计算的典型应用场景和有关案例,为该领域相关研究提供参考。     

基于无监督深度学习的电子健康档案数据挖掘技术研究进展

介绍自编码器、生成式对抗网络、BERT等无监督深度学习方法,阐述其在电子健康档案数据挖掘中的应用以及存在的挑战,指出无监督深度学习技术能够加速医疗知识发现和临床决策支持,促进个性化医学发展。     

基于机器学习的儿科急诊患者候诊时间预测及关联分析研究

以首都儿科研究所附属儿童医院急诊患者就诊数据为基础,设计并提取19个就诊时间相关变量,构建6种不同的机器学习模型,用于儿科急诊就诊患者候诊时间预测,经检验预测模型表现良好。本研究有助于医院动态分配医疗资源,缓解急诊科拥挤程度,提高患者就诊满意度。     

Many-Scale Investigations of the Deformation Behavior of Polycrystalline Composites: I—Machine Learning Applied for Image Segmentation

Our work investigates the polycrystalline composite deformation behavior through multiscale simulations with experimental data at hand. Since deformation mechanisms on the micro-level link the ones on the macro-level and the nanoscale, it is preferable to perform micromechanical finite element simulations based on real microstructures. The image segmentation is a necessary step for the meshing. Our 2D EBSD images contain at least a few hundred grains. Machine learning (ML) was adopted to automatically identify subregions, i.e., individual grains, to improve local feature extraction efficiency and accuracy. Denoising in preprocessing and postprocessing before and after ML, respectively, is beneficial in high quality feature identification. The ML algorithms used were self-developed with the usage of inherent code packages (Python). The performances of the three supervised ML models—decision tree, random forest, and support vector machine—are compared herein; the latter two achieved accuracies of up to 99.8%. Calculations took about 0.5 h from the original input dataset (EBSD image) to the final output (segmented image) running on a personal computer (CPU: 3.6 GHz). For a realizable manual pixel sortation, the original image was firstly scaled from the initial resolution 10802 pixels down to 3002. After ML, some manual work was necessary due to the remaining noises to achieve the final image status ready for meshing. The ML process, including this manual work time, improved efficiency by a factor of about 24 compared to a purely manual process. Simultaneously, ML minimized the geometrical deviation between the identified and original features, since it used the original resolution. For serial work, the time efficiency would be enhanced multiplicatively.     

Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees

ObjectiveAfter deploying a clinical prediction model, subsequently collected data can be used to fine-tune its predictions and adapt to temporal shifts. Because model updating carries risks of over-updating/fitting, we study online methods with performance guarantees. Materials and MethodsWe introduce 2 procedures for continual recalibration or revision of an underlying prediction model: Bayesian logistic regression (BLR) and a Markov variant that explicitly models distribution shifts (MarBLR). We perform empirical evaluation via simulations and a real-world study predicting Chronic Obstructive Pulmonary Disease (COPD) risk. We derive “Type I and II” regret bounds, which guarantee the procedures are noninferior to a static model and competitive with an oracle logistic reviser in terms of the average loss.ResultsBoth procedures consistently outperformed the static model and other online logistic revision methods. In simulations, the average estimated calibration index (aECI) of the original model was 0.828 (95%CI, 0.818–0.938). Online recalibration using BLR and MarBLR improved the aECI towards the ideal value of zero, attaining 0.265 (95%CI, 0.230–0.300) and 0.241 (95%CI, 0.216–0.266), respectively. When performing more extensive logistic model revisions, BLR and MarBLR increased the average area under the receiver-operating characteristic curve (aAUC) from 0.767 (95%CI, 0.765–0.769) to 0.800 (95%CI, 0.798–0.802) and 0.799 (95%CI, 0.797–0.801), respectively, in stationary settings and protected against substantial model decay. In the COPD study, BLR and MarBLR dynamically combined the original model with a continually refitted gradient boosted tree to achieve aAUCs of 0.924 (95%CI, 0.913–0.935) and 0.925 (95%CI, 0.914–0.935), compared to the static model’s aAUC of 0.904 (95%CI, 0.892–0.916).DiscussionDespite its simplicity, BLR is highly competitive with MarBLR. MarBLR outperforms BLR when its prior better reflects the data.ConclusionsBLR and MarBLR can improve the transportability of clinical prediction models and maintain their performance over time.     

针刺对血管性痴呆大鼠学习记忆能力及海马内乙酰胆碱酯酶含量的影响

目的复制拟血管性痴呆（vascular dementia,VD）模型,探讨针刺百会、人中、大椎穴对大鼠学习记忆能力及血清和海马内乙酰胆碱酯酶（acetylcholinesterase,AchE）含量的影响。方法用双侧颈总动脉缺血再灌注法复制VD大鼠模型,将VD大鼠随机分为模型组、针刺组和西药组。采用大鼠跳台试验检测学习记忆能力;采用酶联免疫法检测血清和海马组织AchE含量。结果针刺组大鼠跳台反应期时间比模型组显著缩短（P〈0.05）;针刺组大鼠血清和海马AchE含量显著低于模型组（P〈0.05）。结论降低VD大鼠血清和海马内AchE含量是针刺的作用机制之一。