Machine learning predicts portal vein thrombosis after splenectomy in patients with portal hypertension: Comparative analysis of three practical models

BACKGROUND For patients with portal hypertension(PH),portal vein thrombosis(PVT)is a fatal complication after splenectomy.Postoperative platelet elevation is considered the foremost reason for PVT.However,the value of postoperative platelet elevation rate(PPER)in predicting PVT has never been studied.AIM To investigate the predictive value of PPER for PVT and establish PPER-based prediction models to early identify individuals at high risk of PVT after splenectomy.METHODS We retrospectively reviewed 483 patients with PH related to hepatitis B virus who underwent splenectomy between July 2011 and September 2018,and they were randomized into either a training(n=338)or a validation(n=145)cohort.The generalized linear(GL)method,least absolute shrinkage and selection operator(LASSO),and random forest(RF)were used to construct models.The receiver operating characteristic curves(ROC),calibration curve,decision curve analysis(DCA),and clinical impact curve(CIC)were used to evaluate the robustness and clinical practicability of the GL model(GLM),LASSO model(LSM),and RF model(RFM).RESULTS Multivariate analysis exhibited that the first and third days for PPER(PPER1,PPER3)were strongly associated with PVT[odds ratio(OR):1.78,95%confidence interval(CI):1.24-2.62,P=0.002;OR:1.43,95%CI:1.16-1.77,P<0.001,respectively].The areas under the ROC curves of the GLM,LSM,and RFM in the training cohort were 0.83(95%CI:0.79-0.88),0.84(95%CI:0.79-0.88),and 0.84(95%CI:0.79-0.88),respectively;and were 0.77(95%CI:0.69-0.85),0.83(95%CI:0.76-0.90),and 0.78(95%CI:0.70-0.85)in the validation cohort,respectively.The calibration curves showed satisfactory agreement between prediction by models and actual observation.DCA and CIC indicated that all models conferred high clinical net benefits.CONCLUSION PPER1 and PPER3 are effective indicators for postoperative prediction of PVT.We have successfully developed PPER-based practical models to accurately predict PVT,which would conveniently help clinicians rapidly differentiate individuals at high risk of PVT,and thus guide the adoption of timely interventions.

Creative Imitation: An Answer to the Fundamental Issue of L2 Learning

This paper reports on a study on the effects of reading-writing integrated tasks on vocabulary learning and explored the differential roles of creative construction and non-creative construction in promoting lexical learning. Participants were 90 first-year English majors, randomly assigned to two experimental groups(continuation and retelling) and one control group, with 30 students in each group. Results showed that the continuation group generated a substantial amount of creative construction and produced significantly more instances of creative imitation than the retelling group. The continuation group outperformed the retelling group for both receptive and productive vocabulary knowledge gain and retention, but differences were only significant in terms of productive vocabulary retention. Finally, productive vocabulary knowledge retention among the continuation group was significantly and positively correlated with creative imitation(meaning creation coupled with language imitation), but not with linguistic alignment per se. As productive vocabulary knowledge constitutes the learner ’s ability to use lexical knowledge to express ideas in dynamic contexts, the findings afforded evidence that creative imitation could be the answer to the fundamental issue of L2 learning(i.e., mapping static language onto dynamic idea expression). The pedagogical implications as well as future research directions are also discussed.

A Deep Learning-Based Continuous Blood Pressure Measurement by Dual Photoplethysmography Signals

This study proposed a measurement platform for continuous blood pressure estimation based on dual photoplethysmography(PPG)sensors and a deep learning(DL)that can be used for continuous and rapid measurement of blood pressure and analysis of cardiovascular-related indicators.The proposed platform measured the signal changes in PPG and converted them into physiological indicators,such as pulse transit time(PTT),pulse wave velocity(PWV),perfusion index(PI)and heart rate(HR);these indicators were then fed into the DL to calculate blood pressure.The hardware of the experiment comprised 2 PPG components(i.e.,Raspberry Pi 3 Model B and analog-todigital converter[MCP3008]),which were connected using a serial peripheral interface.The DL algorithm converted the stable dual PPG signals acquired from the strictly standardized experimental process into various physiological indicators as input parameters and finally obtained the systolic blood pressure(SBP),diastolic blood pressure(DBP)and mean arterial pressure(MAP).To increase the robustness of the DL model,this study input data of 100 Asian participants into the training database,including those with and without cardiovascular disease,each with a proportion of approximately 50%.The experimental results revealed that the mean absolute error and standard deviation of SBP was 0.17±0.46 mmHg.The mean absolute error and standard deviation of DBP was 0.27±0.52 mmHg.The mean absolute error and standard deviation of MAP was 0.16±0.40 mmHg.

Digital Disparities:How Artificial Intelligence Can Facilitate Anti-Black Racism in the U.S.Healthcare Sector

This paper delves into the intricate interplay between artificial intelligence(AI)systems and the perpetuation of Anti-Black racism within the United States medical industry.Despite the promising potential of AI to enhance healthcare outcomes and reduce disparities,there is a growing concern that these technologies may inadvertently/advertently exacerbate existing racial inequalities.Focusing specifically on the experiences of Black patients,this research investigates how the following AI components:medical algorithms,machine learning,and natural learning processes are contributing to the unequal distribution of medical resources,diagnosis,and health care treatment of those classified as Black.Furthermore,this review employs a multidisciplinary approach,combining insights from computer science,medical ethics,and social justice theory to analyze the mechanisms through which AI systems may encode and reinforce racial biases.By dissecting the three primary components of AI,this paper aims to present a clear understanding of how these technologies work,how they intersect,and how they may inherently perpetuate harmful stereotypes resulting in negligent outcomes for Black patients.Furthermore,this paper explores the ethical implications of deploying AI in healthcare settings and calls for increased transparency,accountability,and diversity in the development and implementation of these technologies.Finally,it is important that I prefer the following paper with a clear and concise definition of what I refer to as Anti-Black racism throughout the text.Therefore,I assert the following:Anti-Black racism refers to prejudice,discrimination,or antagonism directed against individuals or communities of African descent based on their race.It involves the belief in the inherent superiority of one race over another and the systemic and institutional practices that perpetuate inequality and disadvantage for Black people.Furthermore,I proclaim that this form of racism can be manifested in various ways,such as unequal access to opportunities,resources,education,employment,and fair treatment within social,economic,and political systems.It is also pertinent to acknowledge that Anti-Black racism is deeply rooted in historical and societal structures throughout the U.S.borders and beyond,leading to systemic disadvantages and disparities that impact the well-being and life chances of Black individuals and communities.Addressing Anti-Black racism involves recognizing and challenging both individual attitudes and systemic structures that contribute to discrimination and inequality.Efforts to combat Anti-Black racism include promoting awareness,education,advocacy for policy changes,and fostering a culture of inclusivity and equality.

Machine learning applications in healthcare clinical practice and research

Machine learning(ML)is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis,thus creating machines that can complete tasks otherwise requiring human intelligence.Among its various applications,it has proven groundbreaking in healthcare as well,both in clinical practice and research.In this editorial,we succinctly introduce ML applications and present a study,featured in the latest issue of the World Journal of Clinical Cases.The authors of this study conducted an analysis using both multiple linear regression(MLR)and ML methods to investigate the significant factors that may impact the estimated glomerular filtration rate in healthy women with and without non-alcoholic fatty liver disease(NAFLD).Their results implicated age as the most important determining factor in both groups,followed by lactic dehydrogenase,uric acid,forced expiratory volume in one second,and albumin.In addition,for the NAFLD-group,the 5th and 6th most important impact factors were thyroid-stimulating hormone and systolic blood pressure,as compared to plasma calcium and body fat for the NAFLD+group.However,the study's distinctive contribution lies in its adoption of ML methodologies,showcasing their superiority over traditional statistical approaches(herein MLR),thereby highlighting the potential of ML to represent an invaluable advanced adjunct tool in clinical practice and research.

Web-based learning in inflammatory bowel diseases:General truths and current specifics

In a field rapidly evolving over the past few years, the management of inflammatory bowel diseases(IBD), Crohn's disease and ulcerative colitis, is becoming in-creasingly complex, demanding and challenging. In the recent years, IBD quality measures aiming to improve patients' care have been developed, multiple new medical therapies have been approved, new treatment goals have been set with the "treat--to--target" concept and drug monitoring has been implemented into IBD clinical management. Moreover, patients are increasingly using Internet resources to obtain information about their health conditions. The healthcare professional with an interest in treating IBD patients should deal with all these challenges in everyday practice by establishing, enhancing and maintaining a strong core of knowledge and skills related to IBD. This is an ongoing process and traditionally these needs are covered with additional reading of textbook or journal articles, attendance at meetings or conferences, or at local rounds. Web--based learning resources expand the options for knowledge acquisition and save time and costs as well. In the new era of communications technology, web-based resources can cover the educational needs of both patients and healthcare professionals and can contribute to improvement of disease management and patient care. Healthcare professionals can individually visit and navigate regularly relevant websites and tailor choices for educational activities according to their existing needs. They can also provide their patients with a few certified suitable internet resources. In this review, we explored the Internet using PubMed and Startpage(Google), for web-based IBD--related educational resources aiming to provide a guide for those interested in obtaining certified knowledge in this subject.

Learn to Unlearn for Transformative Learning:An Epistemological Look

The present work corresponds to a reflection about several theories and approaches of the learning and their relevance in the training of social agents,reflection that emerges from the practice of the training,and attending to the fact that the meaning of what it implies to learn and how it is that is learned is not at the center of the discussion in universities in Latin America,which emphasizes more what to teach rather than how to teach.

以任务驱动为导向的AutoCAD绘图课程混合式教学实践研究

任务驱动教学方法能够实现理论知识与实际能力的本位一体化教学,是一种深入运用构建主义的模块式教学方法。结合AutoCAD的教学现状和教学内容,分析了教学过程中存在的问题,进而提出了任务驱动式教学的运用及教学过程的设计,并根据不同学习背景的同学对教学任务的难易程度进行了调整,以保证学生在学习过程中获得学习效能感。研究结果表明,在AutoCAD绘图课程中使用以任务驱动为导向的混合式教学方法,能够满足学生的学习需求,并促进学生在知识、技能和职业素养的全面提升。

Theory and Practice of Learning Organization ——Investigation and Thinking for the Management Committee of the Baotou Rare Earth High-tech Area

Nowadays, many countries and regions use the human resources development as the major approach in holding the initiative in competition. The Baotou Rare Earth High-tech Area implements the strategies for revitalizing the area through establishing science and education learning organization and developing the entire learning in Management Committee. In accordance with the fundamental of mathematics ＂The arithmetical mean is equal to or larger than its geometric mean to any positive real number＂, it submits ＂the theoretical model of proportional development advantage of the same element in the same level＂ in order to solve ＂the problem that a few people behind in the department block the development of the High-tech Area＂, realize every member＇s common progress and each department＇s proportional development, and finally make the effect of Rare Earth High-tech maximum by strengthening team cooperation and producing a multiplier effect.

Practical Activities to Improve Transfer of Learning in the Nuclear-Related Continuing Professional Educations for Developing Countries in Korea

Much in-class education and training for developing countries have focused on how a learner absorbs knowledge and skills efficiently or effectively in the class,but are less interested in how the learners should transfer the knowledge and skills into their jobs in their workplace.In principle,in-class education and training have a difficulty with applying the learned knowledge and skills to learners’jobs in the workplace in comparison with any other practical-basis training.To overcome this difficulty,many educational stakeholders in the nuclear field have concentrated on how learners can transfer the knowledge and skills absorbed in the class into their jobs in their workplace.The action learning activity for learners can be one of the solutions to apply the knowledge and skills to their job in the workplace.The purpose of this study is to clarify how the transfer of learning has been implemented in the nuclear-related continuing professional educations and training for developing countries in Korea.To accomplish this purpose,this study is implemented as follows.The first is to define the concept of the“transfer of learning”clearly.The second is to clarify the core elements of the transfer of learning.Along with the clarification,the third is to show how the transfer of learning has been implemented in the continuing professional nuclear-related education and training for developing countries in Korea.The fourth is to present core problems in such education and training.As the fifth,this study suggests alternatives to overcome the core problems in the nuclear-related continuing professional education and training.

The Practical Path to Cultivating Innovative Talents:Implementing Cross-Disciplinary Learning Communities in Environmental Design Education

In response to the national strategy of“vigorously cultivating interdisciplinary talents and actively promoting interdisciplinary integration,”this article focuses on the nationally recognized Environmental Design program at Hezhou University’s College of Design,leveraging local industry advantages to engage in interdisciplinary integration through educational practices.Using the“Construction of the Panoramic Virtual Nature Museum of the Guizhou Crocodile Lizard at Mount Dagui”as a case study,we aim to establish a professional and interdisciplinary learning community,incorporate student-centered interactive teaching methods,boost student motivation,enhance teaching quality,nurture forward-thinking versatile innovative talents,and provide a guideline for interdisciplinary educational reform.

A Novel Approach to Practical Matrix Converter Motor Drive System With Reverse Blocking IGBT (To continue)

a Pole voltage waveforms (VA20 and VA40) for modulation index 0.4 (middle trace is A-phase voltage waveform) x-axis: 1 div.=10ms, y-axis: 1 div.= 100V b Normalized harmonic spectrum for pole voltage of Fig. 9a c A-phase current and phase voltage for modulation index 0.4 (reference space vector is in inner layer)

基于核方法的连续动作Actor-Critic学习

强化学习算法通常要处理连续状态及连续动作空间问题以实现精确控制.就此文中结合Actor-Critic方法在处理连续动作空间的优点及核方法在处理连续状态空间的优势,提出一种基于核方法的连续动作Actor-Critic学习算法(KCACL).该算法中,Actor根据奖赏不作为原则更新动作概率,Critic采用基于核方法的在线选择时间差分算法学习状态值函数.对比实验验证该算法的有效性.

浅谈如何提高Photoshop的教学效果

根据多年从事Photoshop教学工作的经验,针对计算机专业学生在学习该课程中存在的问题,提出教师在教学中应从提高学生兴趣,培养学生自主学习能力、审美能力、创新精神和综合实践能力等方面提高教学效果。

交互式微视频学习资源的研发与应用研究——以继续教育课程“Photoshop”为例

随着信息技术的不断发展,微视频课程的价值愈加凸显。以继续教育课程"Photoshop"为例,设计并开发一套应用性较强的交互式微视频资源,将课程应用于专业技术人员继续教育培训中,旨在提升专业技术人员的专业技术能力,提高学习效率,促进继续教育方式的变革。研究发现,交互式微视频在一定程度上能够提高专业技术人员学习的积极性以及课程参与度,取得了较好的效果。

基于ITtools3.0的深度学习过程设计与实践研究

近年来,随着教育信息化的持续深入,如何利用信息技术促进学生深度学习引起教育技术研究者的广泛关注。该文从深度学习的特征出发,论述了ITtools3.0平台的主要功能对深度学习的支持。根据"深度学习路线"理论,构建了ITtools3.0平台支撑下的深度学习过程模型。通过具体实践,对该平台支撑下深度学习的效果进行了分析,以期为课堂深度学习的有效开展提供理论与实践的支持。

“帮学课堂”在高职艺术设计专业课程“Photoshop图像处理”的实践及思考

“帮学课堂”教学模式强调生为学本、师为帮本,主张自助学习、帮助学习与互助学习相结合,创建有利于帮助学生学习的环境,助力学生全面发展。笔者以高职艺术设计专业课程“Photoshop图像处理”为例,在动漫制作技术专业6个班级中开展“帮学课堂”实践研究。结果表明,由帮学材料与帮学空间构建的“以学生学会为目的的帮学环境”为帮学实践提供了有效保障;“二分四型”的结构设计使教与学的分工更加明确合理;“两环六步”的帮学流程使学生的技能水平和自助学习能力均得到有效提高。学生对课程整体的满意度较高,可为其它课程的课堂革命提供借鉴和参考。

网络环境下微信英语学习平台“Talk To You”的实践性研究

微信英语学习平台"Talk To You"的创建主要以大学生为学习群体,在总结分析学习者的学习需求基础之上,利用一系列在线学习内容和学习活动激发学习者的英语学习兴趣,提高其英语学习技能。实践证明,学习者的英语学习兴趣增强,听、说、读、写、译的技能有所提高,学习主动性增加;同时也为高校英语教师利用微信等多模态形式进行英语教学提供借鉴。

Model identification of continuous stirred tank reactor based on QKLMS algorithm

The continuous stirred tank reactor(CSTR)is one of the typical chemical processes.Aiming at its strong nonlinear characteristics,a quantized kernel least mean square(QKLMS)algorithm is proposed.The QKLMS algorithm is based on a simple online vector quantization technology instead of sparsification,which can compress the input or feature space and suppress the growth of the radial basis function(RBF)structure in the kernel learning algorithm.To verify the effectiveness of the algorithm,it is applied to the model identification of CSTR process to construct a nonlinear mapping relationship between coolant flow rate and product concentration.In additiion,the proposed algorithm is further compared with least squares support vector machine(LS-SVM),echo state network(ESN),extreme learning machine with kernels(KELM),etc.The experimental results show that the proposed algorithm has higher identification accuracy and better online learning ability under the same conditions.